Reservoir and Completions Workflow Integration for Optimized Marcellus Field Development
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Cited by 2 publications
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Optimized Rock Fluid Interaction and Stimulation Intensity Enhancement Improves Well Performance: A Marcellus Case Study
Large volume slick-water stimulations have become the de facto standard for completion strategy in the Upper Devonian, Marcellus, and Utica/Point Pleasant. Current completion optimization work has focused on optimizing stage spacing, sand loading, and injection rate which have shown increases in well productivity. One commonly overlooked variable in the design equation is stimulation fluid chemistry and rock/fluid interaction. Friction reducers, the primary additive of a slickwater system, have become a commodity with many service companies providing similar systems. Premium slickwater systems in the Marcellus are generally characterized by the ability to tolerate high percentages of produced water. We have developed an alternative approach to the design of stimulation fluid chemistry. This approach consists of creating a comprehensive laboratory workflow justification for multiple fluid combinations with consideration for specific thermal maturity windows. The laboratory workflow includes proprietary rock/ fluid interaction tests that insure formation compatibility, lever imbibition/displacement production mechanisms, insure compatibility of fluid components inclusive of available water sources, and insure optimization of the fluid based on stimulation intensity (Budney 2017) objectives. After extensive testing, a new stimulation fluid chemistry has been developed that offers several advantages verified by laboratory testing. The new stimulation fluid chemistry consists of a multifunctional additive with the following characteristics: salt tolerant, viscosifying, formation stabilizing, wettability enhancing friction reducer technology paired with a compatible scale inhibitor and biocide. This new stimulation fluid chemistry was field tested against an incumbent fluid chemistry provided by the stimulation service company. Well production data from the first multiple well experiment demonstrated the new stimulation fluid chemistry resulted in significantly improved well performance. A second multi-well experiment in a different area was conducted and proved the well performance improvement associated with the new stimulation fluid chemistry was repeatable. Economic analyses on wells from both field experiments demonstrate an excellent return on investment with the new stimulation fluid chemistry. This study highlights the importance of justifying stimulation fluid chemistry utilizing a laboratory workflow. The laboratory workflow incorporates rock/fluid interaction testing to maximize the imbibition/displacement production mechanism. The laboratory workflow must also prove that the stimulation fluid chemistry satisfies the stimulation intensity objectives of high rate, high sand concentration, and reduced fluid volumes while enabling reliable field execution.
Optimized Rock Fluid Interaction and Stimulation Intensity Enhancement Improves Well Performance: A Marcellus Case Study
Large volume slick-water stimulations have become the de facto standard for completion strategy in the Upper Devonian, Marcellus, and Utica/Point Pleasant. Current completion optimization work has focused on optimizing stage spacing, sand loading, and injection rate which have shown increases in well productivity. One commonly overlooked variable in the design equation is stimulation fluid chemistry and rock/fluid interaction. Friction reducers, the primary additive of a slickwater system, have become a commodity with many service companies providing similar systems. Premium slickwater systems in the Marcellus are generally characterized by the ability to tolerate high percentages of produced water. We have developed an alternative approach to the design of stimulation fluid chemistry. This approach consists of creating a comprehensive laboratory workflow justification for multiple fluid combinations with consideration for specific thermal maturity windows. The laboratory workflow includes proprietary rock/ fluid interaction tests that insure formation compatibility, lever imbibition/displacement production mechanisms, insure compatibility of fluid components inclusive of available water sources, and insure optimization of the fluid based on stimulation intensity (Budney 2017) objectives. After extensive testing, a new stimulation fluid chemistry has been developed that offers several advantages verified by laboratory testing. The new stimulation fluid chemistry consists of a multifunctional additive with the following characteristics: salt tolerant, viscosifying, formation stabilizing, wettability enhancing friction reducer technology paired with a compatible scale inhibitor and biocide. This new stimulation fluid chemistry was field tested against an incumbent fluid chemistry provided by the stimulation service company. Well production data from the first multiple well experiment demonstrated the new stimulation fluid chemistry resulted in significantly improved well performance. A second multi-well experiment in a different area was conducted and proved the well performance improvement associated with the new stimulation fluid chemistry was repeatable. Economic analyses on wells from both field experiments demonstrate an excellent return on investment with the new stimulation fluid chemistry. This study highlights the importance of justifying stimulation fluid chemistry utilizing a laboratory workflow. The laboratory workflow incorporates rock/fluid interaction testing to maximize the imbibition/displacement production mechanism. The laboratory workflow must also prove that the stimulation fluid chemistry satisfies the stimulation intensity objectives of high rate, high sand concentration, and reduced fluid volumes while enabling reliable field execution.
The application of Artificial Intelligence for planning has received increased attention in the energy industry in the past few years, particularly for the increased production efficiency requirements and environmental standards. The objective of this paper is to show the successful integration of production, completion, subsurface and spatial data using machine-learning algorithms to predict production performance for future development wells. The internal Marcellus Business Unit (MBU) well database, populated with data of 500+ historical wells, has been used in this study. Production data, treated as timeseries, has been processed using functional Principal Component Analysis (PCA) to allow removal of outliers and mode detection. Utilizing this data, a suite of machine-learning algorithms has been applied to reconstruct gas production from available and target well data. Uncertainty quantification has been provided for production curves to identify the quality of prediction. During the study, the sensitivity analysis on input variables has been performed iteratively to screen and rank the most important variables for prediction. The workflow, Unconventional Reservoir Assistant (URA), has been implemented in a proprietary cloud-based platform providing the necessary means for data upload, integration, pre-processing, and finally model training and deployment. This allows the user to focus on the evaluation of model output quality, data filter and workspace generation for continuous model testing and improvement. The full well dataset, split into trained and tested data, has been used for prediction as a preliminary guide to where the most prolific areas of development are located. Results were ranked based on production expected by pad and based on normalized performance. The information was then used to compare with type curves and original development order. In parallel, economic evaluation of break-even was performed to rank all future pads. Consequently, integration of the model prediction and breakeven ranking were used to generate the final development order for the MBU. The URA tool has shown preliminary success in predicting production performance for the pilot development area. Multiple case studies have been run achieving blind test results with high accuracy for historical prediction. Results show some dependency of predictor variable ranking on the field development area, providing insight on how subsurface may affect well dynamic behavior. This paper describes how the integration of URA can complement the development workflow for unconventional reservoirs and be used to predict performance based on complex data integration. The methodology used is superior to standard machine learning models providing only production indicators, as it gives the user the possibility to evaluate economics and completion design sensitivity for future well activities. The methodology can be further extended as a proxy model for well schedule optimization in planning or for better insight into well refrac selection.
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