Priyavrat Shukla

Schlumberger (British Virgin Islands)

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Piyush Pankaj

ExxonMobil (Germany)

Timothy R. Carr

West Virginia University

Payam Kavousi Ghahfarokhi

West Virginia University

Wei Kan Wang

Schlumberger (British Virgin Islands)

Samir Menasria

Schlumberger (British Virgin Islands)

Manas Koley

Schlumberger (British Virgin Islands)

Tobias Judd

Schlumberger (British Virgin Islands)

Steve Geetan

EP Energy (Czechia)

Carl Sondergeld

University of Oklahoma

Chandra Rai

University of Oklahoma

Cooperative Institutions

Schlumberger (British Virgin Islands)

University of Oklahoma

Kuwait Petroleum Corporation (Kuwait)

Nanaji Deshmukh Veterinary Science University

West Virginia University

Dow Chemical (United States)

Zhejiang University

West Virginia University Hospitals

National Energy Technology Laboratory

Shanghai Jiao Tong University

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Application of Innovative Hydraulic Fracture Modeling Demonstrates a Holistic Approach to Pad Development in the Delaware Basin

International Petroleum Technology Conference (2019)

Priyavrat Shukla Alejandro Martínez F. Mohamed Fan Li Rui Zhang

10.2523/19141-ms

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Citations (0)

Seismic Attributes Application for the Distributed Acoustic Szensing Data for the Marcellus Shale: New Insights to Cross-Stage Flow Communication

SPE Hydraulic Fracturing Technology Conference and Exhibition (2018)

Payam Kavousi Ghahfarokhi Timothy R. Carr Liaosha Song Priyavrat Shukla Piyush Pankaj

Abstract Recently, oil and gas companies started to invest in fiber optic technology to remotely monitor subsurface response to stimulation. Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) record vibration and temperature around the fiber, respectively. In this research, we introduce new seismic attributes calculated from the DAS data that could suggest cross-stage fluid communication during hydraulic fracturing. The DAS data covers the entire 28 stimulated stages of the lateral MIP-3H well close to Morgantown, WV. We calculated the energy attribute for the DAS data of the studied stages. Subsequently, a Hilbert transform is applied to the DAS data to evaluate the instantaneous frequency of each trace in the DAS. In addition, we applied a fast Fourier transform to each trace for all the SEGY files to calculate the dominant frequency with a 30 second temporal window. The dominant frequency is compared to the DTS data and energy attribute for the stages in the horizontal MIP-3H well. The DTS analysis shows that stimulation of the stages 10 causes a temperature rise in the previous stage 9; in contrast, stage 18 stimulation does not affect stage 17 temperature. We suggest that the common low frequency zone identified in instantaneous frequency and dominant frequency attributes between stages 10 and 9 is related to presence of fluid and gas that transferred cross-stage during hydraulic fracturing. The fluid and results in the frequency damping of the vibrations around the fiber. We show that the frequency attribute reveals increases detail about the stimulation than conventional signal energy attribute of the DAS data.

Distributed Acoustic Sensing

10.2118/189888-ms

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Citations (23)

Fracture Plug Completions: Evolution, Technological Advancements, and Reservoir Simulation Guided Implications

SPE Annual Technical Conference and Exhibition (2022)

Priyavrat Shukla Raj Malpani Michael Dardis Mark Norrid

Abstract Hydraulic fracturing operations are increasingly becoming larger through a reduction in cluster spacing, wellbore proximity, and fast-paced "zipper" operations to maximize efficiency. Low-cost operation is the primary motive behind this. Some issues hindering optimal performance include fracturing plug failure, under/over stimulation and adverse well performance as stated in published literature. This paper addresses plug effectiveness and impacts on production, fracture geometry and well spacing using a historical review of fracturing plugs and a simulation-based approach. Using numerical simulation, the study also shows that if a fracture plug failure happens, it results in under or overstimulation which leads to inefficient reservoir drainage. This paper will elaborate on the technological growth for fracturing plugs (material choices and plug architecture). A simulation-based approach will then be used to understand the effect of fracturing plug failures on well interference and the production impact on a three-well pad containing one parent and two child wells in an unconventional reservoir. A parent well will be produced for 1.5 years before two child wells are drilled and completed. Knowing the performance from the three-well case, a comparative simulation study is performed for frac plug failure at 25%, 50%, and 75% of the job pumped on the child wells. Quantifying the potential losses from plug failure can help to understand potential effects on well recovery. Such an event can be recognized through a surface pressure signature or using DAS/DTS or microseismic measurements or even using sealed wellbore pressure monitoring (SWPM) on parent wells. A comparative analysis using numerical simulation of hydraulic fractures and their calibration and production modeling is presented in this paper. Three cases for 25%, 50% and 75% understimulation is considered in failed stages. One-third of the well portion of the well is considered understimulated, one third is overstimulated and one third is treated as per design. Impact of well spacing (330 ft and 660 ft) as well as parent well depletion is considered to create a realistic scenario of plug failures to quantify the impact. Applying advanced materials and using the bottom set, mandrel-less concept has resulted in the shortest, strongest, best milling and most reliable plugs currently available. At lesser well spacing, this is even more drastic when parent wells get fracture hits due to prior depletion and also because stages larger than planned are pumped at the neighboring child wells due to fracture plug failure. This paper will enable the reader to make better decisions about the best plug technology depending on the application. It also provides a quantifiable understanding on the adverse effects of frac plug failure on production. So far, this understanding has been limited to field observations alone. This paper combines the understanding from field evidence, literature and modeling-based approach.

Spark plug

Wellbore

Microseism

Reservoir Simulation

10.2118/210396-ms

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Citations (2)

Hydraulic Fracture Calibration for Unconventional Reservoirs: A New Methodology for Predictive Modelling

Day 2 Wed, March 23, 2022 (2019)

Piyush Pankaj Priyavrat Shukla Wei Kan Wang

Abstract Hydraulic fracture calibration in an unconventional environment is a complex process and is inconsistently practiced. Automated calibration methods are not effective or efficient in accounting for the heterogeneity and variation of constraining parameters. However, it is important to build a consistent methodology to calibrate hydraulic fractures incorporating the observed data. This paper covers the systematic "Seismic to Simulation" workflow for unconventional reservoirs to constrain a hydraulic fracture model to obtain a calibrated result. For the hydraulic fracture calibration, injection fall-off tests, sonic logs and image logs are commonly used as the primary inputs to calibrate the geomechanical model. A new workflow is developed to be used consistently incorporating the learnings from the traditional fracture calibration methods. Impact of high stress barriers and height and pinchouts of fractures are incorporated in a geomechanical-flow model. Simultaneous matching of the observed net pressure trend, incorporating the effect of reservoir laminations on fracture height growth is made using a complex fracture model. The effect of the natural fracture networks (NFN) on pressure losses and proppant transport is also accounted for in the fracture geometry. Further, hydraulic fracture geometry is calibrated using the microseismic data. The production behavior was validated using numerical simulation for production history matching. A case study from the Permian basin is considered for the paper. The fracture geometry and footprint obtained using the calibration workflow match very closely the observed surface and downhole measurements. We constrained the model by matching the net pressures and achieved simulated production to match within 10% error compared to the actual oil and gas production. The fracture geometry was calibrated using microseismic data and controlled by incorporating the effect of weak interfaces and laminations. This workflow successfully demonstrates hydraulic fracture model calibration using pressure matching, microseismic data and production history matching. Systematically and consistently using this workflow provides solutions for infill well planning and well spacing for asset optimization. This paper explains a systematic fracture calibration procedure that can be easily adopted by the operators to obtain reliable results in unconventional wells. The effect of reservoir laminations and impact of natural fracture in calibrating the fracture geometry and fracture pressure trend is uniquely demonstrated in this study.

10.2523/iptc-19209-ms

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Hydraulic Fracture and Reservoir Characterization for Determining Optimal Well Spacing in the Marcellus Shale

Piyush Pankaj Priyavrat Shukla Payam Kavousi Ghahfarokhi Timothy R. Carr

Abstract Naturally fractured reservoirs such as the Marcellus shale require an integrated reservoir modeling approach to determine well spacing and well-to-well interference. The Marcellus Shale Energy and Environment Laboratory (MSEEL) is a joint project between universities, companies, and government to develop and test new completion technologies and acquire a robust understanding of the Marcellus shale. The study presented in this paper aims to reveal an approach to determine reservoir depletion with time through coupled geological modeling and geomechanical evaluation followed by completion and well performance history matching for a multiwell pad in the Marcellus shale. The geomechanical model was prepared with interpreted vertical log data. A discrete natural fracture (DFN) model was created and used to determine the complexity of hydraulic fracture geometry simulated through complex fracture models on a two well pad. The microseismic data obtained during the hydraulic fracture simulations served as a constraining parameter for the hydraulic fracture footprint in these wells. Sensitivity to the DFN is realized by parametric variations of DFN properties to achieve a calibrated fracture geometry. Reservoir simulation and history matching the well production data confirmed the subsurface production response to the hydraulic fractures. Well spacing sensitivity was done to reveal the optimum distance that the wells need to be spaced to maximize recovery and number of wells per section. Hydraulic fracture geometry was found to be a result of the calibration parameters, such as horizontal stress anisotropy, fracturing fluid leakoff, and the DFN. The availability of microseismic data and production history matching through integrated numerical simulation are therefore critical elements to bring unique representation of the subsurface reaction to the injected fracturing fluid. This approach can therefore be consistently applied to evaluate well spacing and interference in time for the subsequent wells completed in the Marcellus. With the current completion design and pumping treatments, the optimal well spacing of 990 ft was determined between the wells in this study. However, wells to be completed in the future need to be modeled due to the heterogeneity in the reservoir properties to ensure that wells are not either underspaced to cause well production interference or overspaced to create upswept hydrocarbon reserves in the formation. By adopting the key learnings and approach followed in this paper, operators can maximize subsurface understanding and will be able to place their wellbore in a nongeometric pattern based on reservoir heterogeneity to optimize well spacing and improve recovery.

Microseism

10.2118/191802-ms

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Citations (5)

Application of Refracturing Using Coiled Tubing Opens a New Door of Opportunities for Unconventional Reservoir Stimulation

SPE/ICoTA Coiled Tubing and Well Intervention Conference and Exhibition (2018)

Piyush Pankaj Priyavrat Shukla

Abstract In North America, refracturing has been found to be effective in many instances for increasing the longevity of the well production and helping to drill and complete offset wells. Several instances suggest that refracturing by bull-heading is relatively ineffective because fluids and proppants are lost in the pre-existing hydraulic fractures. Refracturing through coiled tubing (CT) provides a large benefit in giving ability to pinpoint the location of the refracturing treatment by creating new perforations using abrasive jetting and using diversion pills for isolating high-permeability clusters. This paper helps elucidate the benefits and production gain when using CT for refracturing jobs. A case study from the Eagle Ford shale illustrates the impact of CT refracturing applications. When CT was hydraulic fracturing was applied in the first generation of wells with 18 stages, 36% extra production was observed in the first year as compared to the bullheading technique. Simulations based on integrated reservoir and geomechanical earth models, complex hydraulic fracture models, diversion simulation, numerical production simulation and finite element computations enable characterizing the productivity from the CT refrac operations. A comparison is made between the bullheading technique and CT based refracturing jobs. The impact of refracturing using CT on the offset child wells to be drilled and completed is also studied. The study demonstrates that it is critical to place the perforation locations in areas of undepleted reservoir for successful refracturing. Reservoir simulation results in combination with measurements of fluid flow profile in the wellbore can be used to place new perforations in the right sections. Using the diversion pill was found to be greatly effective in improving the fracturing fluid diversion and stimulating the undrained reservoir. With the refracturing using CT, the child wells show improvement in productivity along with the parent well. Overall, the parent and child well combination shows 23% increase in production after one year of refracturing when compared to no refracturing in the parent wellbore. The new approach is verified through the application of simulation and modeling to prove the benefit of CT refracturing operations in unconventional reservoirs. By adopting the key learnings and approach followed in this paper, operators can maximize their chances to improve productivity and compare various refracturing scenarios.

Coiled tubing

Wellbore

10.2118/189933-ms

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Citations (5)

Application of Innovative Hydraulic Fracture Modeling Demonstrates a Holistic Approach to Pad Development in the Delaware Basin

International Petroleum Technology Conference (2019)

Priyavrat Shukla Alejandro Martínez F. Mohamed Li Fan Wei Kan Wang

10.2523/iptc-19141-ms

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Citations (0)

Automated Production Forecasting Using a Novel Machine Learning Based Approach

Kaustubh Shrivastava Priyavrat Shukla Raj Banerjee Manas Koley

Abstract Production forecasting and hydrocarbon reserve estimation play a major role in production planning and field evaluation. Traditional methods of production forecasting use historical production data and do not account for completion and geolocation attributes that limit their prediction ability, especially for wells with a short production history. In this paper, we present a novel data-driven approach that accounts for the completion and geolocation parameters of a well along with its historical production data to forecast production. In this work, we used supervised learning to develop an ensemble of machine learning (ML) based models to forecast production behavior of oil and gas wells. The developed models account for historical production data, geolocation parameters, and completion parameters as features. The dataset used to create the models comprises publicly available data from 80,000 unconventional wells in North America. The developed models are rigorously tested against 5% of the original data set. The models are systematically studied and compared against traditional forecasting techniques and results are presented here. The created ensemble of models was tested by forecasting the production of 3,700 wells and the obtained results were compared against real production data. We show that the models clearly capture the natural decline trend of the produced hydrocarbon. In cases where the natural decline of the well has been temporarily modified, possibly due to operations, the production during other periods of the time series matches the prediction. This indicates that, unlike in traditional methods, such changes don't adversely impact the forecasting ability of our method. We also conducted a systematic investigation and compared the forecast from the developed model against the forecast from a traditional method (Arps, 1945). During the comparison, it was observed that for short-production history wells (available production data from 2 to 12 months), the error rate in the predicted production behavior from traditional methods was higher when compared with the developed method. As the quantity of historical production data increases, the forecasting ability of traditional methods improves. By comparison, the decline from the developed method matches the real production data for both short- and long-production history wells, and clearly outperforms the traditional methods based on blind tests. In this work, we present a novel ML based approach for forecasting production. This approach overcomes the challenge of the traditional time-series forecasting techniques that use only the past data for forecasting. It also incorporates static parameters (completion and geolocation parameters) in its architecture. The developed method leverages statistical averaging by employing an ensemble of random forest models, making the developed approach better than traditional ML based methods (ARIMA and LSTM) for forecasting time-series data.

Geolocation

Predictive modelling

Ensemble Learning

10.2118/212723-ms

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Citations (0)

Modeling and Experimental Investigations of Fluid-Related Damage to Hydraulic Fractures

Proceedings of the 9th Unconventional Resources Technology Conference (2018)

Patrice Abivin Rasika Prabhu Daria Khvostichenko Casie Hilliard Christopher E. Nelson

10.15530/urtec-2018-2899497

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Citations (3)