High-quality fracture network mapping using high frequency logging while drilling (LWD) data: MSEEL case study

Fathi, Ebrahim; Carr, Timothy R.; Adenan, Mohammad Faiq; Panetta, Brian; Kumar, Abhash; Carney, B. J.

doi:10.1016/j.mlwa.2022.100421

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…This facility, located in Monongalia County, West Virginia, operates wells in the central region of the Marcellus Shale, making it an ideal location for comprehensive research (Figure 1). which was used for predicting the number of natural fractures and their distribution using artificial intelligence and machine learning [24]. Permeant fiber optic cables installed in Boggess 5H were used to collect DAS, DTS, and DSS data during both the stimulation and production of the wells, whereas the deployable fiber optic used in Boggess 1H was only used during the stimulation period.…”

Section: Field Case Study Demonstrating the Application Of Fiber Opticsmentioning

confidence: 99%

“…The full core and side walls are obtained from the science well, which is used for rock and fluid characterization. Formation micro imager log (FMI) and drilling acceleration data were obtained from all the wells in the Boggess pad, except Boggess 17H, which was used for predicting the number of natural fractures and their distribution using artificial intelligence and machine learning [24]. Permeant fiber optic cables installed in Boggess 5H were used to collect DAS, DTS, and DSS data during both the stimulation and production of the wells, whereas the deployable fiber optic used in Boggess 1H was only used during the stimulation period.…”

Section: Field Case Study Demonstrating the Application Of Fiber Opticsmentioning

confidence: 99%

“…Section E covered the final stages (22)(23)(24)(25)(26)(27)(28), and a combination of an engineered design, sapphire fluid, and an accelerated pump schedule was used. Figure 2 details the MIP 3H engineering design with Track 1 illustrating the measured depth in the lateral section, Track 2 showcasing gamma ray data, and Track 3 revealing the transverse isotropic vertical (TIV) Minimum horizontal stress gradient that is crucial for geomechanical completion design.…”

Section: Completions Design Of Boggess and Mip Padsmentioning

confidence: 99%

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Application of Fiber Optics for Completion Design Optimization: A Methodological Approach and Key Findings

Fathi,

Belyadi,

Adenan

et al. 2024

Fuels

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This study investigates the application of fiber optic technology to optimize completion design in a hydraulic fracture stimulation for Marcellus Shale Reservoir. With a focus on improving cluster efficiencies and overcoming interstage communication challenges, the research utilizes real-time data from distributed acoustic (DAS), temperature (DTS), and strain (DSS) measurements. The methodology comprises a comprehensive analysis of completion and stimulation reports, fiber optics, microseismic data, and well logs. Conducted at the MSEEL well pads, MIP, and Boggess, and equipped with permanent and deployable fiber optic cables, this study emphasizes that engineered/geomechanical completion design leads to sustained cluster efficiency and stage production performance. Inefficient cluster efficiencies are primarily linked to fracture communication. Recommendations include employing a geomechanical completion design, avoiding non-uniform high natural fracture zones during hydraulic fracture stimulations, implementing short stage length, and using more 100 mesh sand. These insights, derived from correlations between fracture counts, distributed strain sensing (DSS), cluster efficiency, production logging, and production data, offer significant implications for optimizing completion design in unconventional reservoirs. The effective application of fiber optic technology, providing real-time DAS, DTS, and slow strain data, proves instrumental in addressing interstage communication challenges, contributing to improved reservoir performances and cost-effective operations in hydraulic fracture stimulations.

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Section: Field Case Study Demonstrating the Application Of Fiber Opticsmentioning

confidence: 99%

Section: Field Case Study Demonstrating the Application Of Fiber Opticsmentioning

confidence: 99%

Section: Completions Design Of Boggess and Mip Padsmentioning

confidence: 99%

See 1 more Smart Citation

Application of Fiber Optics for Completion Design Optimization: A Methodological Approach and Key Findings

Fathi,

Belyadi,

Adenan

et al. 2024

Fuels

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“…Accurate quantification of fracture intensity through machine learning facilitates optimized reservoir development and production strategies, harnessing high-intensity areas efficiently. Integrating machine-learning fracture models into reservoir simulators enables scenario forecasting to optimize hydrocarbon recovery (He et al 2020;Fathi et al 2022;Ng et al 2023). However, addressing the demands for training data volume and natural fracture heterogeneity complexities remains an ongoing research challenge.…”

Section: Introductionmentioning

confidence: 99%

Robust fracture intensity estimation from petrophysical logs and mud loss data: a multi-level ensemble modeling approach

Azadivash,

Soleymani,

Seifirad

et al. 2024

J Petrol Explor Prod Technol

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This study presents a pioneering machine learning approach to continuously model fracture intensity in hydrocarbon reservoirs using solely conventional well logs and mud loss data. While machine learning has previously been applied to predict discrete fracture properties, this is among the first attempts to leverage well logs for continuous fracture intensity modeling leveraging advanced ensemble techniques. A multi-level stacked ensemble methodology systematically combines the strengths of diverse algorithms like gradient boosting, random forest and XGBoost through a tiered approach, enhancing predictive performance beyond individual models. Nine base machine learning algorithms generate initial fracture intensity predictions which are combined through linear regression meta-models and further stacked using ridge regression into an integrated super-learner model. This approach achieves significant improvements over individual base models, with the super-learner attaining a mean absolute error of 0.083 and R^2 of 0.980 on test data. By quantifying the crucial fracture intensity parameter continuously as a function of depth, this data-driven methodology enables more accurate reservoir characterization compared to traditional methods. The ability to forecast fracture intensity solely from conventional well logs opens new opportunities for rapid, low-cost quantification of this parameter along new wells without requiring advanced logging tools. When incorporated into reservoir simulators, these machine learning fracture intensity models can help optimize production strategies and recovery management. This systematic stacked ensemble framework advances continuous fracture intensity modeling exclusively from well logs, overcoming limitations of prior techniques. Novel insights gained via rigorous model evaluation deepen the understanding of naturally fractured reservoirs.

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Fault and fracture network characterization using soft computing techniques: application to geologically complex and deeply-buried geothermal reservoirs

Yasin,

Ding,

et al. 2024

Geomech. Geophys. Geo-energ. Geo-resour.

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Geothermal energy is a sustainable energy source that meets the needs of the climate crisis and global warming caused by fossil fuel burning. Geothermal resources are found in complex geological settings, with faults and interconnected networks of fractures acting as pathways for fluid circulation. Identifying faults and fractures is an essential component of exploiting geothermal resources. However, accurately predicting fractures without high-resolution geophysical logs (e.g., image logs) and well-core samples is challenging. Soft computing techniques, such as machine learning, make it possible to map fracture networks at a finer resolution. This study employed four supervised machine learning techniques (multilayer perceptron (MLP), random forests (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR)) to identify fractures in geothermal carbonate reservoirs in the sub-basins of East China. The models were trained and tested on a diverse well-logging dataset collected at the field scale. A comparison of the predicted results revealed that XGBoost with optimized hyperparameters and data division achieved the best performance than RF, MLP, and SVR with RMSE = 0.02 and R2 = 0.92. The Q-learning algorithm outperformed grid search, Bayesian, and ant colony optimizations. The blind well test demonstrates that it is possible to accurately identify fractures by applying machine learning algorithms to standard well logs. In addition, the comparative analysis indicates that XGBoost was able to handle the complex relationship between input parameters (e.g., DTP > RD > DEN > GR > CAL > RS > U > CNL) and fracture in geologically complex geothermal carbonate reservoirs. Furthermore, comparing the XGBoost model with previous studies proved superior in training and testing. This study suggests that XGBoost with Q-learning-based optimized hyperparameters and data division is a suitable algorithm for identifying fractures using well-log data to explore complex geothermal systems in carbonate rocks. Graphical abstract

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High-quality fracture network mapping using high frequency logging while drilling (LWD) data: MSEEL case study

Cited by 7 publications

References 18 publications

Application of Fiber Optics for Completion Design Optimization: A Methodological Approach and Key Findings

Application of Fiber Optics for Completion Design Optimization: A Methodological Approach and Key Findings

Robust fracture intensity estimation from petrophysical logs and mud loss data: a multi-level ensemble modeling approach

Fault and fracture network characterization using soft computing techniques: application to geologically complex and deeply-buried geothermal reservoirs

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