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Collaborating Authors
Benchmarking of Ultimate Recovery Factor Across Kuwait: Unlocking Additional Potential
Al-Ghanemi, Mohammad (Kuwait Oil Company) | Dhote, Prashant (Kuwait Oil Company) | Bora, Anup (Kuwait Oil Company) | Al-Bahar, Mohammad (Kuwait Oil Company) | Walker, Jake (DeGolyer and MacNaughton) | Dharnidharan, Bala (DeGolyer and MacNaughton) | Hornbrook, John W. (DeGolyer and MacNaughton)
Abstract A countrywide benchmarking of ultimate recovery factor (URF) for oil reservoirs in Kuwait is presented in this study. The results of this study have been useful to support identification of long-term opportunities and have influenced the creation of conceptual development plans for some of the newly discovered prospects of the company. The Kuwait Oil Company (KOC) has a diverse inventory of petroleum reservoirs having a wide range of geologic age, depth, reservoir complexity, and development maturity. This study focused on the reservoir complexity index (RCI) method and global analogs to identify development opportunities and improvements to the URF in the brown and green fields of Kuwait. A customized benchmarking workflow was developed to assess the URF of approximately 100 conventional oil reservoirs. First, a detailed 17-parameter RCI factor and a simplified 7-parameter RCI factor were developed. These two RCI methods were then validated against simulation-estimated URF from waterflood development for the study reservoirs. Next, the two RCI factors were used to benchmark URF for study reservoirs against the company's peers and against global analogs having best practices in operations. Finally, a set of benchmarking dashboards were refined to identify reservoir-complexity concerns, comparisons of company and global peers, and identification of potential upsides or risks in URF. Comparison of higher performing global analogs to the company's reservoirs with similar RCI resulted in identification of additional development opportunities and upsides in URF.
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (0.56)
- Geology > Rock Type (0.47)
- North America > United States > Mississippi > Baxterville Field (0.99)
- North America > United States > Florida > Jay Field (0.99)
- Europe > Russia > Northwestern Federal District > Komi Republic > Timan-Pechora Basin > Pechora-Kolva Basin > Usa Field (0.98)
Summary This study explains how production performance of the multifractured horizontal wells can be divided into two key contributing components: geographical location and completion strategy. Furthermore, we show how to quantify the contribution of these two independent components to production and to understand the variations in key performance drivers across the evaluated field. Being able to differentiate these contributions allows us to compare well performance in a consistent manner and identify potential upside opportunities, such as refracturing candidates, infill well development, and operator benchmarking. Further analysis uses multiple benchmarks to evaluate operator performance and assess how underperforming operators can optimize their completion strategies. We use a novel machine learning approach--a combination of XGBoost and Factor Contribution Analysis (FCA)--that not only allows for fieldwide well evaluations, but also provides a quantifiable contribution of each feature to production. Our approach generates a production prediction model and accounts for the completion parameters and geological information for each well. The final model can be used to either predict future performance of a field/well or to understand reservoir and completion characteristics. This study focuses on the latter and provides an approach to understand the main influencing factors behind well performance as a result of location and completion strategies. Our study is conducted on three major unconventional plays (Haynesville, Eagle Ford, and Bakken), where we demonstrate how different completion features (e.g. We show how to combine the effect of individual controlling factors (e.g. This enables us to quantify what portion of the production is a result of rock quality and how much is due to its completion strategy. This technique also allows us to quantify and relate each of these features, and highlight areas with desirable geological features, as well as good candidates for refracturing jobs. Moreover, we benchmark different operators' performance as it relates to changing rock quality and completion strategies. Introduction Benchmarking, as a tool, is widely used in the oil and gas industry in a variety of use cases (Sonmor 1995; Bybee 2005; Straub et al. 2021). Benchmarking enables oil and gas professionals to identify and analyze how their company is doing compared to their peers in the industry and allows for those companies to track improvements on a continuous basis. By tracking these improvements over time, companies are able to not only improve their own internal processes, but they can benefit from the experience of other similar companies. Benchmarking is also important to demonstrate to shareholders that a company is performing to an acceptable level.
- North America > United States > Texas > Haynesville Shale Formation (0.99)
- North America > United States > Louisiana > Haynesville Shale Formation (0.99)
- North America > United States > Arkansas > Haynesville Shale Formation (0.99)
- (6 more...)
- Well Completion > Hydraulic Fracturing (1.00)
- Well Completion > Completion Installation and Operations (1.00)
- Management > Strategic Planning and Management > Benchmarking and performance indicators (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Application of Combined Statistics, Machine Learning and Game Theory Approach for Shale Gas Production Performance Evaluation: Insights of Frac-Hit Timing and Severity
Zhang, Chunyu (PetroChina Oil, Gas & New Energies Company) | Pan, Yuewei (Research Institute of Petroleum Exploration and Development, CNPC) | Ma, Wei (Texas A&M University) | Bi, Ran (Research Institute of Petroleum Exploration and Development, CNPC)
Abstract The underperforming production wells in many pilot research are commonly ascribed to two key contributing components: geological variation and completion strategy. Traditional statistical analysis and machine learning approaches have overlooked the integration of frac-hit and its impact on production performance due to the complexity and uncertainty. This paper carries out a careful study to compare well production performance by examining the frac-hits timing and severity in addition to differentiating the aforementioned component contributions via combined statistics, machine learning and game theory approach. Being able to analyze the two blocks in Luzhou shale gas reservoir using multiple benchmarks further identifies potential upside opportunities (eg.well spacing, completion schedule). In this study, we utilize the tree-based machine learning model and the explicability tool Shapley Additive exPlanations (SHAP) with the key production indicators to distinguish the trend out of chaos. Specifically, the frac-hits timing and severity such as during which flow regime frac-hit occurred, the pressure increment and the duration are considered along with the completion and geological data. The proposed approach further enables fieldwide production forecast and to understand the additional impact of frac-hit timing and severity on top of the geo- and engineered-features. The application of the two blocks in Luzhou shale gas reservoir in Sichuan Basin showcases the significance and insight by incorporating frac-hits information to facilitate and optimize the asset portfolio. A novel developed solution has been successfully applied to two shale gas blocks in Sichuan Basin to disentangle the complex interactions of increasing scale of completion design, changing geology, disparity of well spacing and the inclusion of the frac-hit event in particular. The results are mainly but not limited to: 1) the key drivers for well productivity in both blocks are identified and quantified in importance hierarchy (Eg. gas content, TOC, proppant intensity etc.); 2) casing deformation is a critical issue for both blocks that negatively influence the well performance; 3) wells experienced frac-hits at late time BDF with a controlled influencing duration leads to smaller damage to the productivity; 4) Higher pressure spike during frac-hits at linear flow most likely triggers a long-term loss of productivity.
- North America > United States (1.00)
- Asia > China > Sichuan Province (0.55)
- Asia > China > Xinjiang Uyghur Autonomous Region > Junggar Basin (0.99)
- Asia > China > Sichuan > Sichuan Basin (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.93)
- (5 more...)
Abstract This study explains how production performance of the multi-fractured horizontal wells can be divided into two key contributing components: (1) geographical location and (2) completion strategy. Furthermore, we show how to quantify the contribution of these two independent components to production, and to understand the variations in key performance drivers across the evaluated field. Being able to differentiate these contributions allows us to compare well performance in a consistent manner and identify potential upside opportunities such as re-frac candidates, infill well development, and operator benchmarking. Further analysis uses multiple benchmarks to evaluate operator performance and assess how underperforming operators can optimize their completion strategies. We use a novel machine learning approach โ a combination of XGBoost and Factor Contribution Analysis (FCA) - that not only allows for field-wide well evaluations, but also provides a quantifiable contribution of each feature to production. Our approach generates a production prediction model and takes into account the completion parameters and geological information for each well. The final model can be used to either predict future performance of a field/well, or to understand reservoir and completion characteristics. This study focuses on the latter and provides an approach to understand the main influencing factors behind well performance as a result of location and completion strategies. Our study is conducted on three major unconventional plays, Haynesville, Eagle Ford and Bakken, where we demonstrate how different completion features (e.g., lateral length, proppant volume, fluid volume) affect production data, and what we could expect in terms of production should the well have been completed differently. We show how to combine the effect of individual controlling factors (e.g., location, depth, lateral length, proppant volume, fluid volume and well spacing) to appropriately characterize the performance of each well in terms of two key components, location and completion. This enables us to quantify what portion of the production is a result of rock quality and how much is due to its completion strategy. This technique also allows us to quantify and relate each of these features, and highlight areas with desirable geological features, as well as good candidates for re-frac jobs. Moreover, we benchmark different operatorsโ performance as it relates to changing rock quality and completion strategies. The proposed procedure allows us to answer a series of important questions that are asked quite often. These include questions such as, is a well's production performance a factor of its location or the way it was completed? How to quantify, separately, the contribution of completion and location to production? Can sweet spots be identified in an area using production data? Does completion effectiveness vary with location, or operator, or year?
- North America > United States > Texas > Haynesville Shale Formation (0.99)
- North America > United States > Louisiana > Haynesville Shale Formation (0.99)
- North America > United States > Arkansas > Haynesville Shale Formation (0.99)
- (6 more...)
Abstract This study used production data and a novel machine learning approach utilizing Factor Contribution Analysis (FCA) to highlight geologic sweet spots for multiple US on-shore basins. Each model result was validated against key geologic parameters to establish if the geologic conditions exist for the modeled sweet spots. Further analysis shows how geologic production drivers can change across each play. Geologic assessments rely primarily on parameters related to tectonic/depositional settings, reservoir storage, saturations, hydrocarbon phase, and wellbore deliverability to define resource play outlines. These same parameters are often used to identify geologic sweet spots and help explain production drivers. Available data resolution varies widely across plays depending on maturity of the play and/or complexity of subsurface relationships. Using only publicly available production and well completion data, XGBoost and SHAP machine learning approaches were used to identify play sweet spots and prepare reservoir quality maps. The focus of this study was on validating the results obtained from machine learning of production variables by using geological information. These geological data were derived from multiple sources including regional interpretations and incorporating geologic parameter cutoffs traditionally used for highlighting geologically favorable areas. Regional play data was provided through public data sources, technical publications, and investor presentations. Parameter cutoffs were overlayed with model results to validate the process. The machine learning methodology utilizing FCA was used to highlight production sweet spots across multiple US on-shore basins. This study has validated the production-based machine learning results through geologic analysis. The result was a strong correlation between key geologic parameters and model results. Specific relationships are established between the geology and model results that allow for deeper insights to be uncovered regarding changing geologic production drivers across the play. This analysis has corroborated independently that machine learning of production variables does result in a reliable characterization of reservoir rock quality. This type of analysis has been applied successfully to several unconventional resource plays, and provides significant impetus for intelligent use of explainable machine learning modeling. Moving forward, application of similar approaches can not only validate model results, but also highlight key geologic production drivers. Validation of the machine learning methodology allows users to better answer questions related to completion effectiveness, well evaluations, and development strategies.
- North America > United States > Texas (1.00)
- North America > United States > North Dakota (1.00)