The Marcellus Shale Energy and Environmental Lab (MSEEL) provides a publicly available dataset and a hypothesis-driven field test of the significance of preexisting natural fractures at multiple scales on the effectiveness of the stimulation of an unconventional reservoir. Sonic and microresistivity imaging show the presence of numerous preexisting cemented fracture swarms, which are evaluated in terms of their influence on the fracture stimulation. Natural fracture intensity in the Boggess 5H and MIP-3H were interpreted based on wireline and logging while drilling (LWD) image logs showing that 1000's of calcite and bitumen cemented, but relatively weak, fractures are present along the laterals as swarms that are at an angle to the present-day stress regime. Fractures with complex bitumen and calcite filling were recognized in cores from pilot wells at the micro and macroscales (micron to millimeter). The importance of pre-existing fractures on geometric stimulations was evaluated and compared to cluster locations that avoided intense preexisting fractures using fiber-optic distributed acoustic and distributed temperature sensing (DAS/DTS) data and supported by production and simulation. Fiber-optic DTS and DAS measurements were coupled with wireline and LWD image logs from the lateral to recognize preexisting and cemented fractures. This data is supplemented with core analysis including (CT and thin sections) from vertical pilot wells shows that clusters in parts of a stage dominated by preexisting fractures have significantly more hydraulic fracture activity to the point that other clusters appear largely inactive. In addition, processed fiber-optic data indicates that preexisting fractures can form near-well bore leak-off pathways to previous stimulated stages. Both can lead to stimulation and subsequent production inefficiencies. Two wells (Boggess 1H and 3H) that attempted to avoid preexisting fractures showed a significant increase in fracture stimulated volume based on decline curve analysis and microseismic. Production history and simulated future production support the conclusion that avoiding preexisting fractures in the Marcellus Shale can increase estimate ultimate production. We present conclusions about stage and cluster spacing and the significance of preexisting natural fractures on stage isolation and fracture efficiency. The publically available data and workflow allow others to use, verify, and evaluate our findings using the same initial data.

Summary: This paper presents a novel workflow for high-precision leak detection in pipeline networks using the negative pressure wave (NPW) technique. The proposed workflow addresses challenges associated with noisy and convoluted pressure transducer data, rapid pressure decay, and the need for robustness in leak event detection. To overcome these challenges, the workflow incorporates data preprocessing techniques for cleansing, normalization, and denoising, as well as dynamic pressure control limit lines to differentiate between pump and leak events. Multiple transducer analysis techniques are used to minimize false positives. Synthetic leak scenarios are generated using the Water Network Tool for Resilience (WNTR) package, enabling a comprehensive assessment of the workflow’s performance. The generated scenarios are validated through pressure history matching against field inline pressure recordings. A dashboard is developed for real-time visualization and verification of leak events. The effectiveness of the workflow is demonstrated through testing on a real network, resulting in the successful detection and precise localization of a confirmed leak event. The workflow proves its capability to achieve high accuracy, with a 100-m resolution in a complex network configuration with 29 pipe sections and 1-Hz pressure signal recordings. For synthetic leak events, a 10-Hz pressure signal is utilized, achieving a remarkable 10-m accuracy. Moreover, the integration of the workflow with supervisory control and data acquisition (SCADA) systems is showcased, highlighting its potential for near real-time leak detection in practical applications. Overall, this paper presents a comprehensive and effective workflow for high-precision leak detection and localization in pipeline networks, offering valuable insights into improving the efficiency and reliability of leak detection systems.

The accurate quantification and mapping of subsurface natural fracture systems using borehole imaging logs are critical for the success of CO2 sequestration in geologic formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and DL workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by using actual electrical resistivity of borehole wall from microresistivity imaging device alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to the Illinois Basin-Decatur Project site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with a high resolution of 0.01 ft or 0.12 in. (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO2 pressure forecasting and postinjection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared with alternative feature extraction technologies. It presents exciting possibilities for advancing CO2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO2 sequestration projects, facilitating sustainable environmental practices, and combating climate change.

Friction reducer (FR) is a chemical additive utilized in hydraulic fracturing operations to minimize friction between the fracturing fluid and the wellbore walls. Its purpose is to overcome tubular drag during high-flow-rate pumping in stimulation treatments. In recent years, there has been a growing preference in the oil and gas industry for high viscosity friction reducers (HVFRs) in fracturing fluids due to their operational and economic benefits. While slick water fracturing fluids contain low concentrations of friction reducers, the concentration of FR used in shale gas reservoirs typically ranges from 0.5 to 2.0 gallons per thousand gallons (gpt). However, the optimal FR concentration for each stage has not been extensively studied. As a result, many oil and gas companies tend to use higher FR concentrations than necessary as a precautionary measure to achieve the desired injection rate and prevent screen out. Unfortunately, this practice leads to excessive FR usage during stimulation treatments, resulting in significant economic losses. The primary objective of this study is to gain a comprehensive understanding of the performance of the Hydraulic Fracturing Friction Reducer used in the completion of six horizontal Marcellus Shale wells. To achieve this, the data collected from wells drilled and completed in Marcellus Shale Energy and Environment Laboratory (MSEEL) project (Boggess pad) with over 340 hydraulic fracturing stages will be evaluated using different Machine Learning (ML) techniques. This study was conducted in three phases, employing a range of machine learning techniques to develop the optimal model for predicting surface treating pressure during hydraulic fracturing and use that to optimize the FR concentration. In phase 1, utilizing data from stage 1 of Boggess 1H, XGBoost emerged as the best-performing model, demonstrating high accuracy in predicting surface treating pressure. In phase 2, when deploying the same model on all other stages of Boggess 1H, promising results were observed. However, phase 3 revealed that extending the model to other adjacent wells resulted in unsatisfactory results, highlighting the necessity for new and distinct models tailored to each individual well.

This paper presents a novel machine learning workflow for automated fracture and baffle identification in completion design optimization for unconventional reservoirs, engineered geothermal systems, and CO2 storage sites. The current manual interpretation process of wellbore microimaging logs by geology experts is time-consuming and uncertain (Fathi et al., 2022, Pham et al 2021). The proposed workflow utilizes computer vision and deep learning techniques to automate the detection of fractures and baffles, significantly reducing cost, time, and human bias. Applying the workflow to The Illinois Basin – Decatur Project (IBDP) site, achieves significant reductions in interpretation time and cost. The results identified major fractured zones and multiple baffles, along with an interbedded layer that enhances reservoir pore pressure and protects cap rock integrity. Validated by microseismic and image log interpretations, the automated workflow provides accurate and near-real-time mapping of fractures and baffles, enhancing CO2 pressure forecasting and supporting postinjection analysis. This approach stands out for its robustness, accuracy, and reduced computational cost compared to alternative feature extraction technologies.