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The objective of this study is to summarize a proven solution workflow to address the challenges to handle the high volume of well tests daily incorporating information from operational activities, and especially, potential delays and errors in validation impacting other dependent business processes. The proposed solution aims to reduce processing time, minimize human error, and enhance accuracy in well test analysis. Having up-to-date and reliable well test data, engineers can improve engineering workflows, and optimize production. The solution covers data consumption, data preparation, machine learning (ML) solution, cooperating with dependent business processes, deployment and retrain strategy. The ML solution learns from historical well test data with accepted and rejected flag to build a rule-based deterministic ML model to automatically validate and detect the invalid well test with probability. The solution does not only consume structure data but also textual data with natural language processing (NLP), such as well test comments provided by well testing engineers and operational activities in Daily Operational Reports (DORs). Data consumption, operational activities, dependent workflow control are customizable based on different projects. Retrain strategy is based on model prediction accuracy trend and defined during deployment. The solution triggers insights with confidence scores, suggesting acceptance/rejection or review of new well tests. Early detection of possible rejections enables timely actions, including retesting if necessary. The solution was implemented and significantly reduces well test validation time from weeks to hours, enhancing the accuracy of production analysis and optimizations. The data-driven approach offers flexibility and adaptability to meet operation needs, presenting a robust alternative to rule-based validation. By integrating ML and NLP, the solution provides a comprehensive and efficient framework for well test validation, improving decision-making and ensuring compliance with Standard Operation Procedure (SOP). This study introduces a novel approach to well test validation by leveraging ML and NLP. By considering both historical data and manual operational event inputs from engineers, the solution enhances the accuracy and efficiency of the validation process. It contributes to improved production performance analysis, diagnostics, and issue detection. The solution deployment can be customized and adaptable to different data storage and availability, to automate well test validation process in the oil and gas industry.
The objective of this study is to summarize a proven solution workflow to address the challenges to handle the high volume of well tests daily incorporating information from operational activities, and especially, potential delays and errors in validation impacting other dependent business processes. The proposed solution aims to reduce processing time, minimize human error, and enhance accuracy in well test analysis. Having up-to-date and reliable well test data, engineers can improve engineering workflows, and optimize production. The solution covers data consumption, data preparation, machine learning (ML) solution, cooperating with dependent business processes, deployment and retrain strategy. The ML solution learns from historical well test data with accepted and rejected flag to build a rule-based deterministic ML model to automatically validate and detect the invalid well test with probability. The solution does not only consume structure data but also textual data with natural language processing (NLP), such as well test comments provided by well testing engineers and operational activities in Daily Operational Reports (DORs). Data consumption, operational activities, dependent workflow control are customizable based on different projects. Retrain strategy is based on model prediction accuracy trend and defined during deployment. The solution triggers insights with confidence scores, suggesting acceptance/rejection or review of new well tests. Early detection of possible rejections enables timely actions, including retesting if necessary. The solution was implemented and significantly reduces well test validation time from weeks to hours, enhancing the accuracy of production analysis and optimizations. The data-driven approach offers flexibility and adaptability to meet operation needs, presenting a robust alternative to rule-based validation. By integrating ML and NLP, the solution provides a comprehensive and efficient framework for well test validation, improving decision-making and ensuring compliance with Standard Operation Procedure (SOP). This study introduces a novel approach to well test validation by leveraging ML and NLP. By considering both historical data and manual operational event inputs from engineers, the solution enhances the accuracy and efficiency of the validation process. It contributes to improved production performance analysis, diagnostics, and issue detection. The solution deployment can be customized and adaptable to different data storage and availability, to automate well test validation process in the oil and gas industry.
In line with ADNOC production growth strategy, water injection management is seen as one of the key field development strategies to achieve the mandated production target as it will maintain reservoir pressure as well as improve sweep efficiency and increase field recovery factor. In view of this, it is crucial to ensure an effective integrated water injection system with sufficient water supply capacity is in place. ADNOC Onshore has set-up unique digital framework to manage the system with increased levels of consistent production through increased uptime, while also reducing maintenance costs and lowering overall risk This digital framework integrated with three types of analytics that businesses use to drive their decision making. Real time monitoring has been implemented as a foundation layer for water injection system consisting of water supply wells, surface pumps and water injection wells. Descriptive Analytics uses data aggregation and data mining to provide insight into the past such as down time analysis & down time root causes. These analytics are useful because they allow us to learn from past behaviors and understand how they might influence future outcomes. The next layer is predictive Analytics, which uses statistical models and forecasting techniques to understand the future. The failure prediction models have been implemented to predict ESP failures in water supply wells and pump failures in surface pumps. This provides actionable insights based on data. The relatively new field of prescriptive analytics allows users to "prescribe" several different possible actions and guide them towards a solution. In a nutshell, these analytics are all about providing advice. Prescriptive analytics attempts to quantify the effect of future decisions to advise on possible outcomes before the decisions are made. This framework integrates all essential elements of water injection surveillance and analysis into a fully digitized intelligent system, it significantly reduces total operating costs, and substantially decreases production risks. This intelligent system has been implemented across multiple fields consisting of several hundred injectors and supply wells. Digital transformation is changing the way operates on a scale for managing water injection system. The comprehensive real-time and near-real-time reporting the system provides gives an unparalleled level of transparency into all the daily field operations that are carried out on their assets, directly and unfiltered from the sensors and digitized processes. This paper describes the unique digital framework aligned with data analytics for managing water injection system.
The standard operating procedure (SOP) requires a well test monthly for every active producer to assess performance behavior. With over 100 new well tests daily, a busy operation schedule can lead to delayed validation, causing high accumulated amounts throughout the month and spill over to the next month. If the well-test quality does not meet the expectation, it should be rejected and required to retest immediately to comply with SOP. The significant effort and delayed well-test validation will cause inaccuracy in production performance analysis, diagnostics, and potential issue detection. This solution aims to significantly reduce processing time from gathering enough historical information to validating with engineering models and limits human error by checking all available well tests and preparing detailed analysis for engineers to make the final decision. By having more updated accepted well tests to update well engineering models, the solution helps to improve accuracy and more confident outputs in other engineering workflows like production back allocation, well rate estimation, well and network model calibrations, and production optimization. The proposed solution leverages artificial intelligence (AI) capability learns from historical well test data with accepted and rejected flag to build a rule-based deterministic machine learning (ML) model to automatically validate and detect the possible rejected or accepted well test. The solution also considers well test comments or remarks provided by well-testing engineers which are processed via Natural Language Processing (NLP) engine. ML model can propose to accept a well test with confidence score to automate the validation and support engineer's decision. On the other hand, if the model detects a possible rejected well test, it suggests engineer to review the new well test information versus historical performance and takes actions, where early rejection triggers retesting requirement to the offshore team to prioritize the well to the test plan. Periodically, the ML model may require updates based on the most recent well test data in order to maintain its accuracy. The solution significantly reduces well test validation time from weeks to hours, improving the accuracy of other production performance analysis and optimizations. The data-driven approach can easily be adapted to different fields’ needs, offering a more flexible and efficient alternative to hard-coded rule-based well test validation.
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