Fracwave

Introduction: The Stimulation Challenge at the Heart of Geothermal Expansion

The pursuit of sustainable baseload energy has renewed global attention toward geothermal systems—specifically Enhanced Geothermal Systems (EGS), which aim to transform hot, impermeable rock formations into productive heat exchangers. Unlike conventional hydrothermal reservoirs, where natural permeability supports fluid circulation, EGS relies on engineered fracture networks created through well stimulation.

This requirement—creating and sustaining sufficient permeability under extreme stress and temperature—has emerged as the principal obstacle to commercial deployment. Stimulation governs both the thermal performance and the economic viability of EGS projects. Despite advances in drilling, geomechanics, and real-time monitoring, the fundamental physics of hydraulic stimulation in crystalline basement rock remain limiting.

Traditional methods adapted from oil and gas—hydraulic fracturing, acidizing, and chemical stimulation—struggle to perform under high-temperature and high-stress regimes. The injected fluids can react with minerals, scale fractures, or dissipate through leak-off, while the sustained pressure injection carries risks of induced seismicity. Moreover, EGS projects must meet far more stringent safety and environmental expectations than conventional stimulation operations.

To unlock the full potential of geothermal energy, the industry must rethink how permeability is created. One promising innovation gaining attention is Nanoparticle-Enhanced Plasma Pulse Stimulation (PPPS), a hybrid electro-mechanical method developed by the FracWave Research Group at the University of Houston. Early results indicate that PPPS could alleviate several core challenges in EGS by substituting fluid-driven pressure with precisely controlled electrical discharges.

---

EGS Stimulation: Persistent Challenges in the Subsurface

1. Subsurface Uncertainty and Limited Connectivity

The fundamental difficulty in EGS stimulation lies in predicting how induced fractures propagate and interconnect between injection and production wells. Crystalline basement formations—granites, gneisses, and metamorphic rocks—possess limited pre-existing permeability. When high-pressure fluid is injected, it tends to reactivate pre-existing joints or faults rather than form new fractures.

Field experience from European and US projects demonstrates that fracture propagation is highly anisotropic and sensitive to local heterogeneity. At the Soultz-sous-Forêts site in France, hydraulic stimulation achieved measurable permeability gains, but fracture growth followed complex natural fault planes, limiting connectivity. The Utah FORGE project has since advanced understanding of stress anisotropy and fracture network evolution, yet results continue to show that the stimulated volume is typically smaller and less connected than models predict (WGC, 2020).

Numerical simulators—whether based on discrete-fracture networks (DFN) or coupled thermo-hydro-mechanical (THM) frameworks—remain constrained by uncertain input parameters. Fracture toughness, in-situ stress orientation, and rock anisotropy often vary by orders of magnitude across short distances. Even with advanced modeling, stimulation outcomes are difficult to forecast.

2. Well Integrity and Material Durability

Geothermal wells must endure conditions rarely encountered in hydrocarbon systems: temperatures exceeding 250 °C, pressure fluctuations during stimulation and production, and chemically aggressive fluids rich in dissolved minerals. Conventional cements and steel grades degrade under cyclic thermal stress, leading to micro-annuli formation, casing deformation, or corrosion.

Failures in well integrity not only jeopardize zonal isolation but can terminate entire projects. Once compromised, geothermal wells are extremely expensive to remediate because of depth and heat. Consequently, the stimulation technique must minimize long-duration stress on well materials and avoid the use of reactive chemicals that could exacerbate corrosion or scaling.

3. Induced Seismicity and Operational Risk

No technical issue has affected public perception of EGS more than induced seismicity. At Basel, Switzerland (2006), fluid injection triggered events up to magnitude 3.4, forcing permanent project closure. In Pohang, South Korea (2017), the correlation between stimulation and a magnitude 5.5 earthquake further heightened global scrutiny.

While modern “traffic-light” monitoring systems have improved safety, they constrain injection rates and volumes, often reducing the ability to create large connected fracture networks. EGS operators must navigate a narrow operational window—high enough pressure to induce fractures, yet low enough to avoid triggering seismic slip on existing faults.

Moreover, the public risk tolerance for seismic events in renewable projects is far lower than in oil and gas. The challenge, therefore, is not simply technical—it is societal. Achieving both effective permeability enhancement and seismic safety requires stimulation methods that minimize sustained pore-pressure buildup and fault activation potential.

4. Water and Chemical Use

Typical EGS stimulation campaigns can consume tens of thousands of cubic meters of water. In arid or remote regions, this requirement poses logistical and environmental constraints. Additionally, at high temperature, injected water interacts chemically with formation minerals, leading to silica or carbonate scaling that reduces permeability over time.

Chemical stimulation methods—acidizing or chelating agents—aim to dissolve obstructive minerals, but reaction kinetics under geothermal conditions are difficult to control. The risk of near-wellbore damage, corrosion, or rapid reaction remains high. A truly sustainable stimulation approach should minimize water and chemical use while maintaining long-term formation stability.

5. Economic Viability

Even with technical success, the economics of EGS remain challenging. Deep drilling, specialized completions, and extended operational timelines contribute to high capital expenditures. Because stimulation outcomes are uncertain, financial risk is significant. A stimulation method that offers predictability, reduced water logistics, and minimal surface infrastructure could meaningfully alter the cost equation.

---

The Concept of Nanoparticle-Enhanced Plasma Pulse Stimulation (PPPS)

Nanoparticle-Enhanced Plasma Pulse Stimulation proposes a fundamentally different approach to permeability creation—replacing hydraulic energy with controlled electrical energy. The method relies on discharging stored electrical power from surface capacitor banks through coiled-tubing-deployed electrodes inside the wellbore.

A small volume of conductive nanoparticle fluid acts as the transmission medium. When discharged, the plasma forms within the fluid in microseconds, converting electrical energy into a mechanical shock wave that propagates radially into the surrounding rock. Each pulse generates transient pressures that can exceed 100,000 psi for microseconds—sufficient to fracture or reopen sealed natural fractures without the need for continuous hydraulic pressurization.

Between discharges, the well remains at hydrostatic pressure, which minimizes sustained stress loading on the casing and formation. The process is staged and modular: operators can target specific intervals, adjust discharge energy, and repeat the sequence as needed.

Nanoparticles within the fluid improve conductivity, stabilize plasma generation, and enhance the mechanical coupling between the discharge and rock. Laboratory results suggest that nanoparticles may also influence microfracture propagation by modifying interfacial energy at grain boundaries, potentially producing more distributed fracture networks (FracWave, 2025).

---

How PPPS Addresses Core EGS Challenges

Localized Energy, Global Impact

Unlike hydraulic fracturing, where energy dissipates gradually over large volumes of fluid, PPPS releases energy instantaneously and locally. Each plasma pulse acts as a discrete event, inducing tensile and shear stresses within the near-wellbore region. Repeated pulses can progressively extend the fracture network outward, forming an interconnected system without requiring large-scale fluid movement.

This localized, pulsed energy delivery allows for more precise control over stimulation geometry. Because the pulses are short in duration, the pressure front decays rapidly, reducing the risk of distant fault activation. The ability to direct and sequence pulses along the wellbore offers operators a level of adaptability not available in conventional methods.

Lower Seismic Risk Through Pressure Transience

Induced seismicity in conventional EGS arises largely from prolonged fluid injection, which elevates pore pressure across broad regions of the formation. PPPS operates differently: the pressure perturbation is sharp but transient, and the cumulative pore-pressure increase is minimal.

Theoretically, this minimizes the potential for fault slip at large offsets from the wellbore. Microseismic monitoring during laboratory tests indicates that plasma-induced events are of low magnitude and confined near the wellbore. While full-scale field validation remains ongoing, the pressure-transient character of PPPS aligns well with the seismic safety demands of EGS operations.

Enhanced Permeability and Network Complexity

The shock waves generated by plasma pulses propagate radially, inducing a network of microfractures that may intersect natural discontinuities. Over multiple pulses, these fractures can coalesce into a complex, multi-directional network—precisely the configuration desired in EGS for efficient heat extraction.

The process can be tailored by controlling pulse amplitude, repetition frequency, and duration, allowing the fracture geometry to be tuned to local stress conditions. Because PPPS does not depend on proppant placement, the created fractures rely on asperity support and self-propping under residual stress conditions, similar to shear-dominated dilation observed in some natural systems.

Well Integrity Preservation

Continuous high-pressure injection exerts sustained mechanical loads on casing and cement, promoting deformation and debonding. PPPS, in contrast, delivers very short impulses that dissipate quickly. The absence of extended pressure exposure reduces fatigue on well components.

Furthermore, the nanoparticle fluid is chemically inert and compatible with geothermal well materials. This eliminates corrosion risk associated with acids or high-salinity brines and reduces scaling tendencies. The stimulation therefore imposes minimal chemical or mechanical stress on well infrastructure—a key consideration for long-term EGS operations.

Minimal Water and Chemical Footprint

Because PPPS uses only small fluid volumes for electrical conduction, its water footprint is orders of magnitude lower than hydraulic stimulation. This makes it particularly suited for geothermal fields in arid regions or areas where water management imposes regulatory constraints.

The method also avoids chemical additives, surfactants, or proppants, reducing both environmental exposure and supply-chain complexity. From a sustainability perspective, this shift aligns closely with broader industry trends toward low-impact, resource-efficient operations.

Operational Simplicity and Cost Implications

On the surface, PPPS requires only electrical infrastructure—capacitor banks, control systems, and a coiled-tubing unit—eliminating the need for large pump spreads or fluid-handling equipment. The reduced logistical footprint can lower mobilization costs, site emissions, and noise.

If energy-to-fracture coupling efficiency proves high, the operational cost per stimulated interval may decrease substantially. However, early deployment will likely carry a premium due to specialized equipment and safety certification requirements. As the technology matures and scales, cost competitiveness with hydraulic methods may be achievable.

---

Research Progress and Validation Pathways

Although the PPPS concept builds on decades of research in plasma-based rock fracturing and pulsed-power physics, its application to deep geothermal systems is recent. Laboratory studies at the University of Houston have confirmed the formation of multi-directional fracture networks in granite samples under confining stress. Visualization with micro-computed tomography (µCT) revealed distributed microcrack systems extending several centimeters from discharge centers.

Numerical models coupling electromagnetic and mechanical dynamics are under development to simulate energy transfer, fracture initiation, and propagation. These simulations suggest that fracture growth is primarily driven by the rapid thermal expansion of plasma channels and resulting pressure waves, rather than electrical breakdown alone.

Upcoming field-scale pilots aim to test the method in 3–5 km-deep wells at temperatures above 200 °C. Instrumentation will include downhole pressure transducers, distributed acoustic sensing (DAS), and microseismic arrays to quantify the spatial and temporal characteristics of induced fractures. The objectives are to assess fracture geometry, induced seismicity, and injectivity improvement, providing the data necessary to calibrate THM models and refine operational parameters.

---

Comparative Perspective: Hydraulic vs. Plasma Pulse Stimulation

While hydraulic fracturing and PPPS share the same goal—enhancing subsurface permeability—their operational principles diverge sharply.

This comparison underscores why plasma-based methods may represent a new class of stimulation: one that integrates the controllability of electrical systems with the mechanical impact of shock-wave physics.

---

Integration with Monitoring and Digital Control

Modern EGS projects rely heavily on high-resolution diagnostics—microseismic arrays, distributed temperature sensing (DTS), and fiber-optic acoustic monitoring. PPPS lends itself naturally to integration with such systems. Each pulse is discrete and timestamped, allowing correlation between discharge energy and microseismic response.

This closed-loop stimulation concept could evolve into an autonomous optimization framework, where real-time monitoring feeds back into the discharge control algorithm. Such integration would align with emerging digital twin architectures for geothermal reservoirs, enabling adaptive stimulation with minimal human intervention.

---

Remaining Challenges

Despite its promise, PPPS faces several technical and logistical challenges before it can become mainstream.

1. Scaling Laws: Laboratory results must be extrapolated to field scale. The interaction between shock waves and large-scale heterogeneities in natural rock remains poorly understood.

2. Regulatory Framework: Current well stimulation regulations are based on hydraulic methods. New safety and certification standards will be needed for high-voltage downhole operations.

3. Economic Validation: The cost structure of plasma equipment, power delivery, and operational redundancy will determine whether PPPS can compete commercially once development costs are amortized.

---

Industry Implications and Outlook

If PPPS delivers on its promise, it could redefine stimulation strategy not only for geothermal systems but also for tight gas, coalbed methane, and carbon storage projects where water or seismic constraints limit hydraulic methods. The potential for cross-sector technology transfer—from petroleum engineering to geothermal energy—is significant.

In the near term, PPPS will likely complement rather than replace hydraulic stimulation. Hybrid workflows—using plasma pulses to precondition rock before low-pressure fluid injection—may offer the best of both worlds: enhanced connectivity with reduced seismic and environmental impact.

The broader implication is philosophical as much as technical. PPPS exemplifies a shift toward electrification of the subsurface, where energy is delivered in controlled, measurable quanta rather than bulk fluids. This aligns with the industry’s move toward digitalization, precision operations, and carbon-conscious engineering.

---

Conclusion: From Pressure to Precision

Enhanced Geothermal Systems hold the promise of near-unlimited clean energy, but their success depends on overcoming one stubborn challenge—creating reliable, sustainable permeability in hot, impermeable rock. Nanoparticle-Enhanced Plasma Pulse Stimulation represents a significant step toward that goal.

By converting electrical energy into controlled mechanical work, PPPS addresses many of the barriers that have hindered EGS deployment: uncertain fracture growth, induced seismicity, water scarcity, and material degradation. The technology is still in early stages, and robust field validation remains ahead. Yet its conceptual alignment with the physical and environmental constraints of geothermal reservoirs positions it as one of the most intriguing innovations in stimulation science in decades.

As the energy industry evolves, technologies like PPPS highlight the emerging convergence between petroleum and geothermal engineering—where insights from one field accelerate progress in another. The path to a clean, continuous geothermal future may not lie in higher pressures or deeper wells, but in smarter, electrically driven precision stimulation.

From Empirical Practice to Predictive Science: The Role of Hydraulic Fracture Modeling

Hydraulic fracture modeling is central to modern stimulation engineering because it provides the quantitative link between design parameters, rock mechanics, and fluid dynamics that govern fracture propagation, transforming a complex subsurface process into a predictive, physics-based framework. By solving the coupled equations of fluid flow, stress redistribution, and rock failure, the model enables engineers to explore how injection rate, viscosity, proppant concentration, and in-situ stress anisotropy control fracture length, height, width, and conductivity. Practically, it functions as a virtual laboratory where the interplay between fluid and rock can be studied under controlled conditions before field execution, minimizing uncertainty and cost. The typical inputs include mechanical and hydraulic properties of the formation (elastic modulus, Poisson’s ratio, fracture toughness, leak-off coefficients), fluid characteristics (density, viscosity), and operational parameters such as injection rate and stage spacing. The resulting outputs—fracture geometry, net pressure, and proppant distribution-provide not only the basis for treatment optimization but also critical information for reservoir engineers. These parameters are directly implemented in stimulated reservoir volume (SRV) calculations, reservoir simulation models, and production forecasts to quantify the extent and effectiveness of the stimulated region. Thus, hydraulic fracture modeling bridges completion design and reservoir management by converting invisible subsurface processes into quantifiable engineering insights, elevating hydraulic fracturing from an empirical operation to a predictive, data-informed science that underpins both efficiency and long-term reservoir performance.

Physics-Based and Numerical Frameworks for Fracture Simulation

Hydraulic fracture physics-based modeling encompasses a hierarchy of analytical, semi-analytical, and fully numerical approaches, each tailored to the complexity of the formation and the objective of the study. Simplified analytical or pseudo-3D models are often employed in early design stages to rapidly estimate fracture geometry and treatment pressures, offering practical insight into fracture containment and fluid efficiency. In contrast, fully coupled 3D numerical models—such as finite element, boundary element, or discrete fracture network simulations—capture detailed stress redistribution, fracture coalescence, and fluid-solid interaction, providing high-fidelity predictions of fracture complexity and proppant transport. In unconventional shale reservoirs, these models help interpret stage-by-stage performance, optimize cluster spacing, and assess inter-well interference, while in geothermal systems they are used to evaluate heat-extraction efficiency, fracture conductivity evolution, and long-term thermal recovery. Regardless of scale, all modeling frameworks share a common goal: to translate subsurface physics into actionable parameters that guide completion design, reservoir connectivity, and sustainable production. By integrating these modeling tools across disciplines, engineers can bridge the gap between stimulation and reservoir management, ensuring that hydraulic fracturing evolves from a trial-and-error operation into a predictive and continuously improving engineering science.

Challenges and Uncertainties in Linking Pressure to Geometry

Despite its critical role, hydraulic fracture modeling remains one of the most challenging domains in subsurface engineering due to the inherent complexity and uncertainty of the coupled rock–fluid–stress system. The foremost challenge lies in characterizing the heterogeneous reservoir, where natural fractures, bedding planes, and variable stress fields control fracture initiation and propagation yet are only indirectly constrained by limited data. Laboratory-measured mechanical properties rarely capture true in-situ conditions of temperature, pore pressure, and anisotropy, introducing substantial uncertainty into model inputs. A particularly crucial role of modeling is its ability to link treating pressure-one of the few directly measurable field parameters-to fracture geometry and evolution, thereby converting surface observations into subsurface understanding. This connection allows engineers to interpret pressure signatures in terms of fracture growth, containment, and fluid efficiency, forming the foundation for real-time treatment control and post-job evaluation. However, achieving this linkage is inherently difficult because the system couples multiple nonlinear processes—non-Newtonian fluid flow, proppant transport, leak-off into a deforming porous medium, and dynamic fracture propagation-each occurring at vastly different time and spatial scales. Numerical models must reconcile fracture-tip physics at the millimeter scale with field-scale flow dynamics extending hundreds of meters, while maintaining computational stability and physical accuracy. Simplifications such as planar or homogeneous-layer assumptions often sacrifice realism, particularly in unconventional reservoirs where fracture complexity and stress interference dominate behavior. Moreover, validating model predictions remains a persistent obstacle, as the fracture network cannot be directly observed; calibration relies on indirect data that carry their own uncertainties. Consequently, hydraulic fracture modeling is not merely a theoretical construct but a vital interpretive framework-transforming pressure, rate, and injection data into geometric and mechanical insights that drive the next stages of stimulation design, reservoir simulation, and production optimization.

Hydraulic fracturing in unconventional reservoirs operates at an industrial scale characterized by repetitive, high-intensity stimulation campaigns across laterally extensive shale formations with relatively uniform mechanical and petrophysical properties. Because these reservoirs typically exhibit low permeability and high brittleness within a continuous stratigraphic horizon, the design parameters,such as fluid type, injection rate, stage spacing, and proppant concentration,tend to remain consistent across multiple wells and pads, adjusted only for local stress variations or natural fracture intensity. This standardization enables large-scale development but also amplifies the need for accurate modeling to ensure that uniform designs still achieve effective reservoir contact. The complete workflow,from modeling inputs like in-situ stress and rock elastic properties to the final production forecast—faces challenges in uncertainty propagation, as small errors in property estimation can lead to large discrepancies in predicted fracture geometry and drainage efficiency. Consequently, even though unconventional plays follow highly systematic operational practices, the underlying physics remain complex; understanding how each modeled parameter translates into actual fracture behavior and long-term production is essential for sustaining efficiency, consistency, and economic viability across large development programs.

Modern hydraulic fracturing relies on a suite of advanced diagnostic technologies to estimate fracture geometry and monitor stimulation effectiveness. Microseismic monitoring is widely applied in shale and geothermal wells to map fracture propagation in three dimensions, while tiltmeters and fiber-optic distributed sensing (DAS/strain) provide near-wellbore and along-wellbore measurements of deformation, temperature, and strain evolution. Tracer surveys and production logging complement these measurements by revealing post-stimulation fluid connectivity and fracture extent. However, such technologies are typically restricted to research wells, pilot programs, or high-value development areas where cost and logistics justify their deployment. In contrast, treating pressure remains the only universally available and continuously measured dataset for every hydraulic fracture job, serving as the common diagnostic thread across all operations. Because surface pressure is recorded in all treatments, it forms the backbone for integrating and upscaling the insights from high-resolution diagnostics-allowing the relationships between microseismic, fiber-optic, and tracer responses to be generalized across larger well populations thtough usig them as calibration of physics based models. This unified interpretation framework transforms treating pressure from a simple operational metric into a scalable diagnostic proxy that bridges individual high-tech observations with field-wide understanding of fracture geometry, containment, and stimulation efficiency.

Although treating pressure remains the only continuously measured and universally available dataset during hydraulic fracturing, the interpretive frameworks built around it are still limited. The classical Nolte–Smith plot provides a simple and widely used approach to infer fracture propagation and fluid efficiency from net-pressure versus time behavior. Its main advantage lies in its rapid field applicability and its ability to identify basic fracture growth trends, fluid efficiency, and possible height containment effects. However, it requires accurate determination of closure pressure, assumes a fixed reference point (typically at pump start or breakdown), and presumes uniform stress and leak-off conditions—assumptions that often fail in heterogeneous, multi-cluster, or non-planar fracture environments. The Moving Reference Point (MRP) technique was developed as an improvement, dynamically updating the reference time and pressure to better capture pressure fluctuations and detect transitions in fracturing modes such as dilation, containment, or height growth during pumping. MRP’s key advantage is its capacity to track real-time fracture behavior and adapt to changing operational conditions, but it still depends on empirical power-law relationships and requires computational tuning. Like all pressure-based techniques, it lacks direct spatial resolution of fracture geometry, meaning that in complex unconventional reservoirs, its interpretation remains indirect and must be carefully coupled with physics-based understanding or complementary diagnostics for reliable application.Complementing it using machine learning has serious obstacle of relying on physical assumption simplifying the complexity of fracture propagation complexity.

Toward a Unified Pressure-Based Diagnostic Ecosystem

Building upon the limitations of classical treating-pressure diagnostics such as Nolte–Smith and MRP, we introduced a new physics-based framework that extracts the hidden subsurface information embedded in surface pressure data through time–frequency wavelet analysis. In Calibration of CWT for Dynamic Hydraulic Fracture Propagation with Microseismic Data (SPE-217789-MS, 2024), we presented the Continuous Wavelet Transform (CWT) as a novel diagnostic tool capable of linking treating-pressure variations to fracture geometry evolution. By correlating distinct frequency–time energy bands with microseismic event clouds and Moving Reference Point (MRP) diagnostics, we established a physics-based connection between surface pressure and subsurface fracture dynamics. This correlation demonstrated that specific wavelet-energy signatures correspond to identifiable fracture-growth stages-such as early dilation, lateral extension, and vertical containment-enabling qualitative fracture interpretation from treating pressure alone. The calibration process transformed the CWT from a purely visual signal-processing method into a validated diagnostic framework that can track fracture propagation in real time and under field-scale complexity.

We advanced this methodology further in Advanced Deep Learning for Microseismic Event Prediction for Hydraulic Fracture Treatment via CWT (Geoenergy Science & Engineering, 2024), where we introduced the normalized CWT scalogram to enhance feature consistency and interpretability across different wells, stages, and formations. The normalized representation converts the one-dimensional pressure signal into a time–scale energy map that reveals dynamic fracture behavior—such as branching, leak-off transitions, and confinement changes—that are often obscured in conventional time-domain pressure records. We then extended the analysis by training a deep-learning model using CWT-derived features as inputs and microseismic event coordinates as outputs. This approach enabled direct prediction of three-dimensional fracture geometries and microseismic clouds from pressure data alone, establishing the foundation for automated stimulated reservoir volume (SRV) estimation and real-time fracture evaluation.

We later reinforced this framework through integration with fiber-optic distributed sensing, as presented in Hydraulic Fracture Characterization Using CWT for Treating Pressure Calibrated with Fiber Optics (URTeC-4203446-MS, 2025). By synchronizing CWT energy patterns with Distributed Acoustic Sensing (DAS)  data, we verified that the wavelet-derived energy transitions aligned with deformation and temperature anomalies along the wellbore. This cross-calibration validated that surface-based CWT analysis captures the same fracture-activation signatures observed by high-resolution downhole monitoring, extending its diagnostic fidelity to field-scale operations.

We further expanded the machine-learning integration in Estimating SRV from ML-Predicted Microseismic Clouds Using Pressure-Only CWT Features (SPE-228059-MS, 2025), where we applied the trained model to the Boggess-5H well in the Marcellus Shale. The synthetic microseismic clouds and corresponding SRV estimates—computed via convex-hull and Delaunay-triangulation methods—matched closely with field-derived and rate-transient-analysis results, confirming that treating-pressure-based wavelet features can quantitatively reproduce fracture geometry and production performance. This outcome marked a pivotal advancement toward pressure-only fracture diagnostics that combine physics and data science in one consistent workflow confirming scaleability of our deep learning model to be generalized all over the same basin.

In Advanced Fracture Diagnostics in Utah FORGE EGS: Integrating CWT, Microseismic, and Fiber-Optic Data (SPE-228063-MS, 2025), we applied our previously developed CWT-based fracture diagnostic framework to the unique thermo-mechanical environment of an Enhanced Geothermal System (EGS). The focus was not on the field site itself but on validating our methodology’s universality—whether the same physics-informed signal-analysis principles that capture hydraulic fracture propagation in shale could quantify fracture evolution in crystalline geothermal rock. We adapted the CWT workflow to process treating-pressure data from injection stages, linking wavelet-energy patterns to microseismic activation sequences and fiber-optic strain/temperature anomalies.

Our framework demonstrated that pressure-derived CWT scalograms could identify key fracture processes-initiation, branching, containment, and closure- even under high-temperature, low-permeability conditions. The time-localized energy clusters in the wavelet domain correlated with bursts in microseismic activity and strain fronts detected along the monitoring wells, proving that the dynamic frequency content of the pressure signal carries a quantifiable imprint of the evolving fracture network. Additionally, we incorporated water-hammer damping analysis into the wavelet energy field, showing that higher damping rates correspond to greater fracture complexity, consistent with both fiber-optic strain gradients and distributed temperature drops during injection.

By cross-validating these independent observables within a single analytical space, we established a multi-physics data-fusion framework where the treating-pressure CWT served as the temporal backbone, and the microseismic and fiber-optic data provided spatial calibration. The result is a fully interpretable system capable of mapping Activated Reservoir Volume (ARV), quantifying network complexity, and diagnosing containment without relying on high-cost downhole imaging.Our work showed that CWT-based pressure diagnostics are not only transferable but scalable-offering a single, unified methodology for understanding stimulation physics across petroleum and geothermal systems alike.

Through these collective developments, we established a unified pressure-based diagnostic and predictive ecosystem that bridges analytical modeling, field data, and machine learning. This framework enables quantitative estimation of fracture geometry, SRV, and microseismic distribution from treating pressure alone, transforming the most accessible operational signal into a high-resolution subsurface diagnostic. It represents a decisive shift toward intelligent, data-driven stimulation design—bridging the gap between physics, diagnostics, and predictive modeling in both unconventional and geothermal energy systems.

 

Moving Beyond Conventional Pressure Diagnostics

The accurate identification of fracture events is one of the most critical — and most challenging — aspects of hydraulic fracturing. Nolte–Smith plots, long the standard for treating pressure interpretation, assume continuous fracture growth from a single initiation point. Field and experimental evidence, however, shows that fracture propagation is intermittent, with alternating periods of dilation, extension, and interaction with natural features.

To capture this complexity, the FracWave Research Group at the University of Houston developed the normalized Continuous Wavelet Transform (CWT) scalogram. Instead of relying on smoothed or averaged pressure curves, the CWT decomposes treating pressure into fine-scale, time–frequency features. The result is a high-resolution map that directly displays fracture propagation signatures as they evolve during pumping.

What the Scalogram Shows

The normalized CWT scalogram turns bottomhole pressure into a color-coded diagnostic that distinctly separates fracture behaviors:

• Green “flames” (normalized coefficients 0.4–0.6): signature of lateral fracture length propagation.

• Red zones (0.8–0.95): indicators of vertical fracture growth or strong fluid diversion into high leak-off intervals.

• Blue areas (0.0–0.25): regions of limited fracture width development, often linked to near-wellbore complexity.

This framework allows fracture propagation modes to be seen directly in treating pressure, without requiring specialized monitoring hardware.

Validation Against Fiber Optics

To verify that these pressure-based signatures represent actual fracture behavior, the method was calibrated against Distributed Fiber Optic Sensing (DFOS) measurements from the Marcellus Shale Energy and Environment Laboratory (MSEEL).

Across multiple stages of the Boggess-5H well, strong alignment was observed:

• Stage 41: Early red-dominated scalogram regions corresponded to limited propagation, matching weak DFOS strain activity. As green patterns emerged, DFOS simultaneously recorded tensile strain bands along the well, confirming fracture tip extension. Later red bursts in the scalogram matched localized DFOS strain spikes, indicating vertical growth or interaction with high leak-off pathways.

• Stage 26: Sustained green scalogram zones reflected dominant lateral propagation across clusters. DFOS strain maps showed consistent extension, while intermittent red spikes coincided with fracture activation events.

• Stage 4: Alternating green and red patterns revealed shifts between fracture length growth and episodes of fluid diversion. DFOS data confirmed these transitions, capturing strain intensifications at the same moments highlighted by the scalogram.

This side-by-side calibration confirmed that fracture propagation signatures are embedded in treating pressure and can be reliably extracted through normalized CWT analysis.

Figures at a Glance

Figure 1. Normalized CWT scalogram of treating pressure compared with DFOS strain response in Boggess-5H Stage 26. 

Figure 2. Normalized CWT scalogram of treating pressure compared with DFOS strain response in Boggess-5H Stage 41. 

Figure 3. Normalized CWT scalogram of treating pressure compared with DFOS strain response in Boggess-5H Stage 4. 

Value to the Industry

The significance of this work lies in its practicality:

• We can now see fracture propagation modes directly on treating pressure — a signal that is already recorded in every frac job.

• No special hardware is required. Unlike microseismic or fiber deployments, this method relies only on standard treating pressure measurements.

• Real-time visibility. Fracture behavior can be tracked while pumping is ongoing.

• Scalable and cost-effective. Applicable to every stage in every well, not just pilots.

By transforming pressure into a diagnostic canvas, the normalized CWT scalogram makes high-resolution fracture diagnostics accessible for routine operations.

Looking Forward

Building on this foundation, the FracWave team has also demonstrated that CWT scalograms can feed machine learning models to predict three-dimensional microseismic event clouds from pressure alone. With validation against both DFOS and microseismic, this creates a path toward fully automated, low-cost fracture monitoring and optimization.

Conclusion

The normalized CWT scalogram reveals what was previously hidden: the dynamic signatures of fracture propagation embedded in treating pressure. Distinct color patterns correspond to lateral extension, vertical growth, and limited width development. These interpretations, validated by fiber-optic strain data from the Marcellus Shale, demonstrate that treating pressure is not just an operational record — it is a powerful real-time diagnostic tool.

Reference

Gabry, M. A., Ramadan, A., and Soliman, M. Y. 2025. Hydraulic Fracture Characterization Using Continuous Wavelet Transform for Treating Pressure, Calibrated with Fiber Optics Data. URTeC 4203446, Houston, Texas.

Watch this video for more details

Introduction

Fracture closure detection has long been one of the most debated topics in petroleum and geothermal engineering. Determining minimum horizontal stress (Shmin) is essential for fracture design, reservoir management, and geomechanical modeling. Conventional approaches, such as the Nolte G-function, tangent method, and compliance method, rely on simplifying assumptions that often break down in unconventional reservoirs where fracture networks are complex and heterogeneous.

To overcome these challenges, we developed a novel fracture closure detection method based on the Continuous Wavelet Transform (CWT). Unlike traditional techniques, the CWT approach does not rely on pre-set assumptions about fracture geometry, stiffness, or leak-off behavior. Instead, it analyzes the pressure falloff signal in the time–frequency domain to identify fracture closure more robustly. This article summarizes the theoretical basis of the technique, its calibration through laboratory experiments, validation using field data, and its overall integration into modern workflows.

Theoretical Development of the CWT Technique

The foundation of the CWT method was established to address limitations in G-function derivatives and compliance analysis. By decomposing the pressure falloff signal into multiple frequency scales using the Morlet wavelet, CWT provides a “mathematical microscope” for identifying closure. Closure is marked by a distinct peak in wavelet energy followed by stabilization at a lower level.

Key advantages of this theoretical framework include:

• Independence from assumptions about fracture stiffness evolution or Carter leak-off models.

• Sensitivity to dynamic fracture behavior, including sequential closures in naturally fractured formations.

• Ability to distinguish closure signals from noise, which is especially valuable in field applications.

Theoretical modeling using synthetic pressure data and flow regime simulations confirmed that the CWT method consistently detected closure pressure more accurately than tangent or compliance methods.

CWT Fracture Closure Detection Technique Workflow

CWT Fracture Closure Detection Technique Criteria

Laboratory Calibration

Controlled laboratory experiments under true-triaxial conditions provided the first systematic calibration of the CWT technique. Samples of granite and sandstone were fractured under known minimum stress conditions, followed by pressure falloff cycles.

The results demonstrated that:

• CWT estimates of closure pressure consistently matched applied stress within about 5 percent.

• The method remained robust across rock types with different permeability and surface roughness.

• CWT resolved sequential closure events associated with natural fracture interactions, while tangent and compliance methods systematically underestimated stress.

These laboratory calibrations confirmed the technique’s accuracy and adaptability to different geomechanical settings.

Field Calibration with SIMFIP Measurements

The next stage of validation used field-scale data from the EGS Collab project, where fracture closure was directly measured with the SIMFIP tool. This probe records micron-level borehole deformation during fracture opening and closure, providing an independent benchmark.

CWT-based closure detection aligned closely with SIMFIP-derived stress values. The method also recognized complex closure behavior, including the influence of natural fractures and rough fracture faces. By comparison, conventional methods often produced inconsistent or underestimated values. The successful field calibration demonstrated that CWT can serve as a reliable tool in both geothermal and petroleum reservoirs, where stress estimation accuracy is critical.

Field calibration setup for CWT fracture closure detection technique.

Comparison between fracture closure using the new technique and measured fracture closure using SIMFIP tool

Integration and Comprehensive Review

A broader review of closure detection methods highlighted the limitations of conventional techniques. The tangent method tends to identify closure too early under nonlinear leak-off, while the compliance method often fails to provide consistent signatures in naturally fractured reservoirs.

By contrast, CWT functions as an assumption-free technique that can arbitrate between tangent and compliance interpretations. This enables a more reliable workflow for closure detection, improving confidence in stress estimation across diverse reservoir types.

Implications for Industry

The introduction and calibration of CWT fracture closure detection carry major implications for subsurface engineering:

• Improved fracture design from more accurate stress profiles.

• Reduced uncertainty in geomechanical models.

• Applicability across oil, gas, geothermal, and carbon storage projects.

• Integration into DFIT workflows as a benchmark method that reconciles discrepancies between existing techniques.

The CWT technique provides a universal and validated approach to fracture closure detection, enhancing both academic research and field operations.

For more details

• Gabry, M.A., Eltaleb, I., Ramadan, A., Rezaei, A., Soliman, M.Y. Hydraulic Fracture Closure Detection Techniques: A Comprehensive Review. Energies, 2024. DOI: 10.3390/en17174470
• Gabry, M.A., Eltaleb, I., Soliman, M.Y., Farouq-Ali, S.M. A New Technique for Estimating Stress From Fracture Injection Tests Using Continuous Wavelet Transform. 2022. https://doi.org/10.3390/en16020764
• Gabry, M.A., Eltaleb, I., Soliman, M.Y., Farouq-Ali, S.M., Cook, P.J., Soom, F.A., Guglielmi, Y. Validation of Estimating Stress from Fracture Injection Tests Using Continuous Wavelet Transform With Experimental Data. 2022. https://doi.org/10.3390/en16062807
• Gabry, M.A., Ye, Z., Ramadan, A., Ghassemi, A., Soliman, M.Y. Calibration of Continuous Wavelet Transform (CWT) Fracture Closure Detection Using Laboratory Hydraulic Fracturing Experiments. URTeC 4221618

Hydraulic fracturing is a cornerstone technology for enhancing hydrocarbon recovery from unconventional and tight formations. Successful fracture treatments depend critically on understanding the real-time behavior of the fracture as it propagates through the reservoir. Among the many diagnostic tools developed for fracturing analysis, pressure data measured during fracturing remains the most accessible and valuable source of information.

Traditionally, the Nolte-Smith technique has been the industry-standard method for interpreting fracturing pressure-time data. However, despite its wide use and theoretical foundation, this method operates under assumptions and limitations that can delay or obscure critical insight about fracture growth and behavior. To address these challenges, the Moving Reference Point (MRP) technique was developed, significantly improving real-time diagnostic capability and providing a more precise, faster, and less assumption-laden approach.

This article reviews the development, theoretical basis, practical implementation, and application of the MRP technique to hydraulic fracturing pressure data interpretation. Multiple field examples and case studies are discussed to illustrate its advantages over traditional methods and its integration with advanced fracture simulators.

Background: Conventional Fracture Pressure Interpretation and Its Limitations

The conventional approach, represented primarily by the Nolte-Smith (1981) method, uses a log-log plot of net fracturing pressure (pressure above formation closure stress) versus time to categorize fracture propagation into four distinct modes. These modes represent typical fracturing behavior such as normal fracture extension, screenouts, height growth, and critical pressure behavior. While theoretically sound and validated for many treatments, this approach hinges on key assumptions:

• Constant injection rate during fracturing.

• Continuous and smooth fracture propagation over time.

• Accurate knowledge of formation closure pressure to compute net pressure.

• Use of a log-log scale which compresses data and reduces sensitivity to rapid or subtle changes.

In real life, especially with complex shale and naturally fractured formations, these assumptions frequently break down. Fracture growth often occurs intermittently, with periods of ballooning or dilation interspersed with propagation spurts. This can cause delayed detection of important events, such as screenouts or fracture height growth, because the constant initial reference time compresses the temporal resolution of changes on the log-log plot. Moreover, closure pressure is often uncertain or varies between stages, making net pressure difficult to establish precisely in real time.

The pressure-derivative technique, introduced by Ayoub et al. (1992), enhanced sensitivity by plotting the time-derivative of pressure multiplied by time on a log-log scale, sharing the slope with the original log-log plot. However, it retains the dependency on closure pressure and assumptions of smooth fracture growth, still limiting its real-time application.

The Moving Reference Point Concept: Foundational Innovation

The Moving Reference Point (MRP) technique, introduced by Pirayesh et al. (2013), was developed to overcome these limitations by fundamentally altering the reference framework used to examine fracturing pressure data. Instead of referencing all fracturing events to the initial injection time and closure pressure (fixed reference), MRP dynamically updates the reference time and pressure point when fracturing behavior changes.

This approach reflects and accommodates the observation that fractures do not grow continuously but undergo cycles of propagation, dilation (ballooning), and height growth. Each of these phases can be better identified by shifting the reference point to the onset of that phase rather than rigidly linking all data to treatment start time.

The MRP technique employs a power-law relationship linking pressure and time relative to the current reference point. From this, an exponent $e$ describing fracturing behavior is computed continuously over time. This exponent is the central diagnostic parameter allowing classification of fracturing events:

• $e\approx \; between\; -0.25\; and0.25$ indicates normal fracture extension.

• $e\approx 1$ signifies dilation due to low fracture growth rate (ballooning).

• $e<-\; 0.25$ indicates rapid height growth or excessive fluid leak-off.

This flexible, shifting-frame analysis allows rapid detection of changes in fracturing behavior within seconds to a few minutes of their onset, compared to the tens of minutes lag encountered in the Nolte-Smith approach.

Workflow for the Moving reference point technique

Example of MRP Plot

Numerical Implementation and Workflow

The interpretation workflow begins by selecting an initial reference time and pressure point after formation breakdown or fracture reopening, omitting early noisy data. From there, the technique proceeds sequentially through time, calculating the exponent $e(t)$ for each new point relative to the current reference.

When the mismatch between the estimated bottomhole pressure (calculated from $e$ and other parameters) and the observed wellbore pressure exceeds a predefined threshold, the reference point is shifted to this new time and pressure, resetting the sliding window of analysis. This computationally intensive procedure requires computer programming, but yields a smooth, updated profile of e(t) over the treatment duration.

Plotting $e(t)$ versus time on a Cartesian graph provides a visually intuitive interpretation of changing fracturing modes without data compression. Peaks and troughs on this graph correspond to ballooning, propagation, or height growth phases.

Broader Applications and Advances

Following successful initial demonstrations, MRP has been applied across diverse formations, including complex heterogenous and naturally fractured systems such as the Cotton Valley and Travis Peak sandstones in East Texas.

Studies incorporating MRP with integrated fracture simulators, like the GOHFER planar 3D model, enable:

• Calibration of fracture treatments against detailed reservoir petrophysical and geomechanical data.

• Comparison of fracture events detected by MRP with synthetic simulation fracture growth, height propagation, and leak-off cycles.

• Improved understanding of fluid efficiency and fracture complexity, guiding modifications in pumping rate, fluid properties, and proppant schedules to avoid sanding out or delivery inefficiencies.

These integrated approaches extend MRP’s utility from diagnostic interpretation to a key element in decision-making workflows for optimizing hydraulic fracturing design and execution.

Recommendations for Practice

• Use downhole pressure gauges to capture accurate, high-frequency pressure data necessary for MRP’s detailed temporal resolution.

• Apply MRP analysis in real time during fracturing treatments to detect events early and adjust parameters—pumping rate, fluid viscosity, proppant concentration—to prevent adverse outcomes like screenouts and excessive height growth.

• Combine MRP results with microseismic monitoring and hydraulic fracture simulation outputs to triangulate fracture geometry and propagation mode more confidently.

• Continuously calibrate and optimize MRP numerical routines and threshold parameters for field-specific conditions and treatment types.

Conclusion

The Moving Reference Point technique revolutionizes hydraulic fracturing pressure interpretation by introducing a dynamic frame of reference aligned with fracture growth phases. This method addresses key limitations of traditional log-log methods by improving temporal resolution, eliminating dependency on closure pressure knowledge, and accommodating complex, intermittent fracture behavior.

Validated through multiple field cases and enhanced by integration with sophisticated simulation models, MRP supports real-time decision-making that improves treatment efficiency and well performance. As real-time monitoring and digital oilfield capabilities grow, MRP stands as an essential tool for smarter, more adaptive hydraulic fracturing operations.

You can now uplaod your treating pressure and get MRP Curve for your job using the following tool:

You will need time of the pumping period in minutes , downhole (prefered) or surface treating pressure and date/time column for the job during pumping

🚀 Launch MRP Analysis Tool

Abstract

A breakthrough methodology has been developed that combines continuous wavelet transform (CWT) signal processing with deep learning to predict microseismic events during hydraulic fracturing operations. This innovative approach transforms treating pressure data into normalized CWT scalograms, creating unique signatures for different fracture propagation modes. The technique was validated using data from the Marcellus Shale Energy and Environment Laboratory (MSEEL) and demonstrates exceptional accuracy in predicting three-dimensional microseismic event clouds from treating pressure alone. The deep learning model achieves prediction errors below 0.025% for horizontal coordinates and below 0.2% for vertical coordinates, while generating results in near real-time (40 seconds for a 2-hour fracturing job).

Introduction

Understanding hydraulic fracture propagation events is crucial for optimizing completion designs, estimating fracture geometry, and maximizing production. Traditional methods like the Nolte-Smith technique and moving reference point (MRP) analysis, while useful, have limitations in real-time application and require complex workflows or prior knowledge of closure pressure.

Microseismic monitoring provides valuable insights into fracture propagation by detecting small-scale earthquakes resulting from rock ruptures during high-pressure fluid injection. However, microseismic monitoring is expensive and not always available. The ability to predict microseismic events from readily available treating pressure data would provide operators with critical fracture characterization capabilities at significantly reduced cost.

This study introduces a novel signal processing approach that treats hydraulic fracturing as an input-output system, where pumping rate and proppant concentration are inputs and treating pressure is the output. By analyzing the output signal using advanced CWT techniques, the method reveals fracture propagation dynamics without requiring simplifying assumptions common in traditional approaches.

Methodology

Normalized CWT Scalogram Technique

The foundation of this methodology lies in applying CWT to treating pressure data during hydraulic fracturing. CWT provides continuous scaling and shifting of wavelet functions, offering high resolution in both frequency and time domains. This makes it particularly suitable for analyzing non-stationary signals like treating pressure during fracturing operations.

Figure 1: The CWT mechanism involves comparing a selected wavelet (Complex Morlet Wavelet) to segments of the treating pressure signal, calculating correlation coefficients at different scales and time positions.

[Figure shows the wavelet transform mechanism: original signal → wavelet comparison at multiple scales → energy scalogram generation]

The process involves three key steps:

1. Signal Analysis: A Complex Morlet wavelet is systematically compared to the treating pressure signal across its entire duration
2. Scale Variation: The wavelet is stretched and compressed to capture both fine (high-frequency) and coarse (low-frequency) features
3. Energy Calculation: Wavelet coefficients are squared to create an energy scalogram showing dominant frequencies at each time interval

The energy scalogram undergoes normalization using minimum-maximum scaling to ensure consistency across different fracturing jobs:

(log₂ E)ₙₒᵣₘ = [(log₂ E) – (log₂ E)ₘᵢₙ] / [(log₂ E)ₘₐₓ – (log₂ E)ₘᵢₙ]

This normalization creates normalized CWT coefficients ranging from 0 to 1, enabling direct comparison between different wells and formations.

Fracture Event Classification

The normalized CWT scalogram reveals distinct signatures for different fracture propagation modes:

• Limited Width Propagation: Normalized coefficients 0.0-0.25 (blue regions)
• Length Growth: Normalized coefficients 0.4-0.6 (green regions)
• Height Growth/High Leak-off: Normalized coefficients 0.8-0.95 (red regions)

Figure 2: Example normalized CWT scalogram showing color-coded fracture propagation events over time. The scalogram acts as a “mathematical microscope” revealing subtle changes in treating pressure that correspond to different fracture behaviors.

[Figure shows heat map with time on x-axis, scale on y-axis, and color intensity representing normalized CWT coefficients, with distinct regions corresponding to different fracture events]

Deep Learning Architecture

The deep learning model employs a compact neural network designed specifically for regression tasks with tabular data. The architecture includes:

• Input Layer: 256 features from normalized CWT scalogram
• Hidden Layers: Sequential layers with batch normalization and ReLU activation
  • Linear(256→200) → ReLU → Linear(200→100) → ReLU
• Output Layer: Linear(100→3) for X, Y, Z coordinates of microseismic events

Figure 3: Deep learning framework architecture showing the flow from CWT scalogram input through hidden layers to microseismic event coordinate prediction.

[Figure shows neural network diagram with input layer (256 nodes), hidden layers, and output layer (3 nodes for X,Y,Z coordinates)]

The model was trained on 256,555 data points from 48 hydraulic fracture stages in the Marcellus Shale, with a 70/30 train/test split. Stratified K-fold cross-validation with 10 folds ensured robust performance evaluation across different microseismic event distributions.

Results and Validation

Three-Case Validation Approach

The technique underwent rigorous validation through three distinct cases:

Case 1 – Simulated Fracture: Using a commercial planar 3D simulator with realistic geomechanical properties, the normalized CWT scalogram successfully identified three distinct fracture propagation stages, matching simulated fracture geometry evolution.

Case 2 – Field Data Comparison: Real field data from Well X1 was analyzed and compared with the previously validated MRP technique. The normalized CWT scalogram showed excellent correlation with MRP results while offering significantly faster analysis.

Case 3 – Microseismic Validation: Using MSEEL project data from wells MIP-3H and MIP-5H, the technique was directly calibrated against recorded microseismic events, providing physical validation of fracture event detection.

Performance Results

Figure 4: Comparison of predicted vs. actual microseismic events for Stage 20 in well MIP-5H, showing excellent agreement in both spatial distribution and temporal evolution.

[Figure shows 3D plot comparing actual microseismic events (blue dots) with predicted events (red dots), demonstrating spatial accuracy and event cloud similarity]

The deep learning model achieved remarkable accuracy:

• X and Y directions: <0.025% error for training data, <0.02% for testing data
• Z direction: <0.2% error for training data, with slightly higher but acceptable errors in testing
• Processing Speed: 20 seconds to generate CWT scalogram, 40 seconds to predict microseismic cloud
• Real-time Capability: Updates every 40 seconds during operations

Field Applications

The methodology was successfully applied to multiple fracture stages in the Marcellus Shale, demonstrating consistent performance across varying geological conditions. The technique accurately predicted:

• Fracture propagation direction and extent
• Temporal evolution of microseismic events
• Three-dimensional fracture geometry development
• Identification of height growth vs. length propagation periods

Industry Impact and Applications

Operational Benefits

This technology offers significant advantages for hydraulic fracturing operations:

Real-Time Monitoring: Unlike traditional microseismic monitoring, which requires expensive equipment and post-processing, this method provides near-instantaneous feedback using only treating pressure data.

Cost Reduction: Eliminates the need for dedicated microseismic monitoring equipment while providing similar insights into fracture propagation.

Enhanced Decision Making: Operators can make real-time adjustments to pumping parameters based on predicted fracture development, optimizing treatment effectiveness.

Completion Optimization: Understanding fracture propagation patterns enables better stage spacing, cluster positioning, and completion design.

Comparison with Existing Methods

Figure 5: Performance comparison between normalized CWT scalogram, MRP technique, and Nolte-Smith method showing superior event detection capability and simplified workflow.

[Figure shows side-by-side comparison of three methods analyzing the same treating pressure data, highlighting the clarity and detail provided by the CWT approach]

The normalized CWT technique offers several advantages over traditional methods:

• No closure pressure required (unlike Nolte-Smith)
• Simplified workflow (compared to MRP complexity)
• Real-time capability with continuous updates
• Higher resolution fracture event detection
• Quantitative predictions rather than qualitative interpretations

Technical Innovations

Signal Processing Advances

The application of CWT to fracture analysis represents a significant advancement in signal processing for petroleum engineering. The technique:

• Captures both transient and continuous fracture events
• Provides time-frequency resolution impossible with traditional Fourier methods
• Adapts to non-stationary nature of fracture propagation signals
• Maintains mathematical rigor while offering practical applicability

Machine Learning Integration

The deep learning component successfully bridges the gap between signal processing and physical phenomena:

• Transforms mathematical features into geological interpretations
• Learns complex relationships between pressure signatures and fracture geometry
• Generalizes across different formations and completion designs
• Provides probabilistic predictions with quantified uncertainty

Implementation Guidelines

Data Requirements

For successful implementation, operators need:

• High-resolution treating pressure data (1 Hz sampling recommended)
• Pumping rate and proppant concentration records
• Initial formation characterization for model calibration
• Historical microseismic data for initial training (if available)

Workflow Integration

Step 1: Real-Time Processing

• Continuous CWT analysis of treating pressure
• Automated scalogram generation and normalization
• Integration with existing data acquisition systems

Step 2: Event Prediction

• Deep learning model inference on CWT features
• Microseismic event coordinate prediction
• Fracture geometry estimation and visualization

Step 3: Decision Support

• Comparison with planned fracture geometry
• Identification of unexpected propagation behavior
• Recommendations for pumping parameter adjustments

Quality Control and Validation

Regular calibration against available microseismic data, pressure matching with fracture simulation models, and continuous refinement based on production outcomes ensure maintained accuracy and reliability.

Future Developments

Technology Extensions

Research is ongoing to expand the methodology’s applicability:

• Multi-Formation Training: Developing models for different geological settings
• Enhanced Physics Integration: Incorporating geomechanical constraints
• Uncertainty Quantification: Probabilistic predictions with confidence intervals
• Multi-Well Analysis: Simultaneous monitoring of pad drilling operations

Digital Integration

Future developments will focus on:

• Integration with digital oilfield platforms
• Automated workflow optimization
• Machine learning model continuous improvement
• Integration with production forecasting models

Economic Impact

Cost-Benefit Analysis

The economic advantages of this technology are substantial:

• Microseismic Monitoring Savings: $100,000-500,000 per pad
• Completion Optimization: 10-20% improvement in production through better fracture design
• Reduced Treatment Failures: Early detection of screen-outs and poor propagation
• Operational Efficiency: Real-time adjustments reduce non-productive time

 

Conclusions

This research introduces a paradigm shift in hydraulic fracture monitoring and analysis. The combination of advanced signal processing through normalized CWT scalograms with deep learning creates a powerful tool for real-time fracture characterization. Key achievements include:

1. Breakthrough Accuracy: Prediction errors below 0.025% for horizontal microseismic event locations demonstrate unprecedented precision in fracture event prediction from treating pressure alone.
2. Real-Time Capability: Processing times of 40 seconds for complete fracture analysis enable true real-time monitoring and decision-making during hydraulic fracturing operations.
3. Comprehensive Validation: Three-case validation approach using simulated, field, and microseismic data provides robust confirmation of technique effectiveness across different scenarios.
4. Operational Simplicity: The method requires only treating pressure data, eliminating the complexity and cost of traditional microseismic monitoring while providing similar insights.
5. Universal Applicability: Successful application across multiple formations and fracture stages demonstrates the technique’s broad applicability in the petroleum industry.

The technology represents a significant advancement in completion engineering, offering operators the ability to optimize hydraulic fracturing operations in real-time while reducing costs and improving outcomes. As the industry continues to focus on efficiency and cost reduction, this methodology provides a practical solution that bridges advanced signal processing with operational decision-making.

The successful validation using publicly available MSEEL data demonstrates the technique’s reliability and sets the stage for widespread industry adoption. Future developments will focus on extending the methodology to additional formations and integrating with broader digital oilfield initiatives.

This work exemplifies how advanced mathematical techniques can be successfully applied to solve practical petroleum engineering challenges, providing both academic rigor and immediate industrial value.

You can read the details in the following paper Advanced Deep Learning for microseismic events prediction for hydraulic fracture treatment via Continuous Wavelet Transform

At ATCE 2025, we will evaluate the scalability of the model by validating its results against Rate Transient Analysis (RTA) for a Marcellus Shale well located 3–5 km away from the training dataset. (SPE-228059).

Hydraulic fracturing has long relied on microseismic monitoring, tracers, and fiber optics to infer fracture geometry and complexity. Yet every stage of every well already contains a widely overlooked signal: the pressure oscillations that occur at pump shutdown, known as water hammer (WH).

In our recent SPE Journal publication (SPE-225459), we introduced a novel technique that transforms this free but underestimated signal into a powerful diagnostic tool. By treating water hammer as a damped harmonic oscillator and analyzing its pressure response with the Continuous Wavelet Transform (CWT), we extract damping coefficients that correlate directly with fracture complexity in the reservoir.

he workflow applies the CWT using a complex Morlet wavelet to isolate the dominant frequency ridge of the WH signature, then calculates the damping coefficient from the logarithmic envelope decay. When applied across multiple fracture stages in Marcellus wells, the damping coefficient trends correlated strongly with natural fracture density interpreted from image logs. This provides operators with a cost-effective, pressure-only method to evaluate induced fracture complexity stage by stage.

Value for Unconventional Reservoirs

Unconventional reservoirs depend on maximizing stimulated reservoir volume (SRV) through the creation of complex fracture networks. Traditionally, operators rely on indirect diagnostics such as production logging, tracers, or costly microseismic surveys to estimate fracture effectiveness. The WH-CWT method directly leverages existing pressure data to provide real-time, low-cost insights into fracture complexity, even in the absence of other diagnostics.

This is particularly valuable in resource plays where thousands of stages may be completed annually and marginal wells cannot justify the expense of advanced diagnostics. By linking damping behavior to fracture tortuosity and natural fracture interaction, operators can rapidly identify which stages achieved high complexity and which underperformed, supporting design optimization, well-to-well benchmarking, and economic decision-making.

Model Assumptions and Limitations

The model assumes that water hammer can be represented as a partially damped harmonic oscillator with an added exponential decay term to capture fluid leakoff effects. Under this assumption, damping is governed primarily by fracture tortuosity, interaction with natural fractures, and leakoff into the formation. The approach further assumes:

• Immediate pump shutdown is necessary to avoid overlapping signals from stepped closures.

• Consistent fracturing design across stages allows damping coefficient variations to be attributed mainly to differences in induced complexity rather than treatment changes.

• The complex Morlet wavelet adequately captures oscillatory behavior and phase shifts in the WH signal.

While the method has shown strong correlation with fracture density logs and robust performance in Marcellus wells, operators should note that gradual pump shutdowns, noise, or highly heterogeneous reservoirs can complicate interpretation. Even in such cases, overdamped signatures often provide useful qualitative indications of poor fracture-wellbore communication.

Key findings include:

• Damping coefficients serve as reliable indicators of fracture complexity and natural fracture intensity.

• WH responses can be classified from high-amplitude oscillations to overdamped signals, reflecting changes in fracture tortuosity and communication.

• Nonlinear optimization methods, especially basinhopping, successfully matched modeled WH responses with field data, achieving R² values above 0.86 in most cases.

This new approach leverages data already collected during every hydraulic fracturing stage, requiring no additional field instrumentation. The information is freely available and can be analyzed for each job. The only operational step is a hard pump shutdown for approximately five minutes—no special gauges, sensors, or extra procedures are needed. By reframing water hammer as more than background noise, this method unlocks valuable insights for fracture diagnostics, completion design optimization, and stage-by-stage performance evaluation.

For full methodology, mathematical formulation, case studies, and correlation with fracture density logs, see the peer-reviewed article in the SPE Journal:  SPE-225459 or watch our the following webinar : Decoding Induced Fracture Complexity: Water Hammer Damping Analysis with Continuous Wavelet Transform

The Fracwave Research Group at the University of Houston has developed a new stimulation technology that is now field-ready for deployment. Known as Nanoparticle-Enhanced Plasma Pulse Stimulation (PPPS), this electrified method combines microsecond plasma discharges with engineered nanoparticle fluids to create complex fractures deep in the formation—without massive water volumes, chemicals, or proppant.

Why PPPS, Why Now

For decades, acidizing and hydraulic fracturing have been the mainstay of stimulation. Yet both face mounting limits. Acidizing works primarily in carbonates and carries corrosion and scaling risks. Hydraulic fracturing requires millions of gallons of water and extensive logistics, while concerns over induced seismicity and environmental footprint continue to grow.

The industry needs a stimulation method that is formation-agnostic, operationally efficient, and ESG-aligned. PPPS was designed precisely to fill this gap.

How It Works

Surface capacitor banks release controlled electrical pulses down coiled tubing to a rugged plasma tool. Once discharged, the tool ignites a nanoparticle-laden fluid. Thermite-like reactions amplify the discharge into shock pulses greater than 100,000 psi, generating fractures that extend well beyond the near-wellbore. The fluids are engineered for conductivity and cleanup, leaving no proppant behind.

What Makes It Different

PPPS is compact, precise, and versatile. A treatment requires only coiled tubing, a power unit, and the plasma tool—a fraction of the equipment footprint of a frac spread. The system is effective in tight oil and gas reservoirs, depleted producers, water injectors, and even high-temperature geothermal formations where conventional fluids fail. By eliminating water and proppant demand, PPPS offers a significant cost and logistics advantage, while also aligning with corporate sustainability commitments.

Ready for Field Pilots

Following extensive design and validation, PPPS is now prepared for multi-well pilot trials. Candidate applications include horizontal producers where enhanced connectivity is needed, vertical wells where near-wellbore damage limits performance, and injectors where increased index is sought at lower pressures.

Performance will be measured by fold-of-increase in productivity or injectivity, fracture confirmation through imaging or logging, and diagnostic improvements in pressure transient and rate transient analysis. The safety case is strong, with short-duration pulses, precise zonal placement, and reduced surface intensity.

Collaboration Opportunity

Fracwave is now inviting operators, service companies, and technology partners to join in bringing PPPS into the field. Operators can nominate wells and provide performance data, while service companies can support tool manufacturing and integration. In return, collaborators gain early access to a disruptive technology with the potential to reduce costs, lower environmental footprint, and improve well performance across reservoir types.

A Call to Action

The transition to cleaner, more adaptable stimulation is already underway. With PPPS now field-ready, the next step is collaboration. Fracwave is preparing a kickoff workshop to align specifications, finalize candidate wells, and begin deployment planning.

PPPS offers efficiency, sustainability, and performance in one package. The question is no longer whether the technology works—it is which companies will move first to prove it in the field.

How Companies Can Collaborate

With PPPS now field-ready, the next step is industry collaboration. The Fracwave Research Group invites operators, service companies, and technology partners to join in advancing this technology through structured multi-well pilots.

Operators can contribute candidate wells across diverse applications—tight oil and gas horizontals, depleted producers, injectors, and high-temperature geothermal wells. In return, they gain first-mover access to field data demonstrating how electrified stimulation can lower costs, reduce water use, and improve recovery.

Service companies can support with tool manufacturing, coiled tubing integration, and pulsed-power delivery. This creates the opportunity to establish a new service line built around compact, electrified stimulation instead of conventional large-scale frac fleets.

Technology partners and investors can collaborate on scaling the surface capacitor systems, refining nanoparticle fluid supply chains, and accelerating deployment across basins. Their involvement ensures the system can move quickly from pilot to commercial scale.

Together, these collaborations form the backbone of PPPS deployment. By sharing wells, expertise, and operational data, partners not only help validate the technology but also position themselves at the forefront of a cleaner, more efficient future for well stimulation.

Contact us to discuss more.

We are excited to announce that our team, Fracwave from the University of Houston, together with collaborators from VERTEX group at the Colorado School of Mines, will present three breakthrough studies at the upcoming SPE Annual Technical Conference and Exhibition (ATCE 2025) in The Woodlands, Texas. Collectively, these contributions showcase how wavelet analysis, deep learning, and real-time drilling intelligence are transforming fracture diagnostics for both unconventional reservoirs and next-generation geothermal systems.

Unlocking SRV with Wavelets and Deep Learning

Tuesday, October 21 (15:15 – 15:45, Station 5 – ePosters)

Calibration of Stimulated Reservoir Volume (SRV) Estimation Using Continuous Wavelet Transform (CWT) and Advanced Deep Learning With Rate Transient Analysis (RTA): A Case Study From The Marcellus Shale (SPE-228059).

We demonstrate how the humble treating pressure signal can be elevated into a powerful diagnostic tool. By marrying wavelet transforms with deep learning, we deliver SRV predictions that align with RTA – without relying on costly microseismic surveys. This scalable, pressure-only workflow pushes shale reservoir diagnostics into the digital age.

Expanding the Frontier with Real-Time Fracture Detection

Monday, October 20 (14:50 – 15:15, Room 342BE)

Real-time Automated Fracture Detection in Geothermal Wells Using Low-cost Drilling Data (SPE-227991).

Presented by VERTEX research group from Colorado School of Mines in collaboration with the University of Houston, this study introduces a novel approach that combines CWT with machine learning to extract fracture signatures directly from low-cost drilling data such as ROP, MSE, and torque. Achieving over 95% predictive accuracy, the workflow enables real-time fracture detection that reduces drilling risks, lowers costs, and accelerates geothermal well construction.

Bringing New Clarity to Utah FORGE Geothermal Stimulation

Wednesday, October 22 (08:55 – 09:20, Room 342BE)

Advanced Fracture Diagnostics in Utah FORGE Enhanced Geothermal Systems (EGS): Integrating Continuous Wavelet Transform (CWT), Microseismic, and Fiber-Optic Data for Enhanced Stimulation Insights (SPE-228063).

Here, we integrate pressure-derived wavelet diagnostics with fiber-optic strain sensing and microseismic catalogs into a holistic framework. This approach provides unprecedented clarity on fracture propagation in high-temperature granite and offers a real-time decision-making lens for one of the world’s most advanced geothermal laboratories.

Why It Matters

Across shale gas and geothermal frontiers, our mission remains the same: turning complex signals into actionable insights. From boosting SRV prediction in the Marcellus to unraveling fracture dynamics in Utah FORGE and enabling real-time drilling intelligence, we are laying the data-driven foundation for smarter, cleaner, and more efficient subsurface energy systems.

We look forward to sharing these advances – and the vision driving them – at ATCE 2025.