Artificial intelligence (AI) and machine learning (ML) are transforming industries, where low-cost, big data can utilize computing power to optimize system performance. Oil and gas (O&G) fields are getting mature, where well integrity (WI) problems become more common and field operations are now more challenging. Hence, they are good candidates for transformation due to the low cost of data storage, highlighting the oil market decline, along with dynamic risk posed during operations. This paper is presenting a comprehensive compilation of different ML applications in diverse disciplines of the petroleum industry. The pool of AI and ML with respect to different areas of applications along with publication years has been categorized. The main focus of this study is classifying well integrity failures where the authors found that the potential of AI and ML in predicting well integrity failures has not been efficiently tapped, and there is an explicit gap in the literature. First, the applications of AI, ML, and data analytics in the O&G industry are discussed thoroughly, so this paper can be a comprehensive reference for readers and future researchers. Then data preprocessing is explained. This includes data gathering, cleaning, and feature engineering. Next, the different ML models are compared and discussed. Finally, model performance evaluation and best model selection are described. This study would be a concrete foundation in the design and construction of ML programs that can be deployed for WI risk management. The developed model can be simply used for any well stock, providing quick and easy assessment instead of subjective and tedious assessment. The layout can be simply adjusted to reflect the risk profile of any well type or any field.
Nowadays, oil and gas (O&G) fields are maturing and creating new threats. This urged the operating companies and industry researchers to have intensive focus on well integrity (WI). Building Well Integrity Management System (WIMS) establishes standardized criteria to guarantee that integrity of all wells is preserved during their lifespan, functions properly in healthy condition, and is able to operate consistently to fulfill the expected production/injection demands. Moreover, exploration and production (E&P) companies put Health, Safety, and Environment (HSE), assets, production, local and public image as top priority in their businesses. Having effective WIMS at all times and throughout all well phases reduces the frequency of major integrity failures and thus helps companies to be on track regarding the aforementioned considerations. In this paper, we present a comprehensive review on the system structure and maturity of WIMS in mature fields. This state-of-the-art review highlights the efforts made by different O&G operators all over the world to develop and start application of WIMS, which varies widely due to differences in the main WI challenges that are recurring in each field or concession. Moreover, it lists the goals and expounds the stages of launching effective WIMS. In addition, the key elements, around which the WI program is structured, are discussed and presented for various O&G operators. The major five elements of accountability and responsibility, well operations procedures, well intervention procedures, tubing and casing integrity program, and wellhead and X-tree maintenance are overviewed. Furthermore, this paper assesses WIMS sustainability through demonstration of WI maturity models, scrutiny of maturity levels, and analysis of transformative elements to convert WIMS into strategic framework. Risk management systems as well as application of analytics in WIMS are also covered and thoroughly discussed. In reviewing the literature covering different assets—all over the world for the last 15 years—it was found that real progress was made in WI area, and WIMS established in many operating companies through different approaches. However, the introduced systems lack universality and few of them are applying artificial intelligence as powerful tool for boosting the system. The most obvious finding to emerge from the analysis is that WIMS is crucial system that must be implemented and matured for well lifecycle. The findings of this study can help operating companies for better framing of key pillars to have robust and operable WIMS throughout different fields and concessions, hence improving the well integrity performance worldwide.
Gas lift has been applied successfully worldwide to increase well production. Artificial lift methods include pump assisted lift and gas lift. Gas lift is an artificial lift technique used to increase flow rate of oil wells. In this method, high pressure gas is injected into the well oil column to reduce its average density and make it flow to the surface. The main objectives of this study are to (1) investigate the effects of injection gas gravity, and reservoir temperature on the performance of gas lift, (2) develop a total-system production-optimization model using PROSPER and GAP simulation programs, and (3) increase oil production through optimization of gas injected/fluid-produced oil ratio in an Egyptian oil field utilizing the existing compression capacity. The developed production model has been used for production optimization and allocation of lift gas in a multi-well network, as well as for prediction of future system requirements and identification of any bottlenecking opportunities. The attained results indicated that (1) artificial lift using gas lift is a complex process including several variables, which have been considered for optimization, (2) the optimization of gas injection rate increased the attained oil production, (3) gas gravity of injected gas has an important effect on attained oil production while reservoir temperature has shown minimal effect on oil production, and (4) gas lift optimization in Egyptian field can help in overcoming the high pressure gas (HPG) constraints, saving gas for the nearby oil field and increasing the total production for both fields. The developed production model is completely implemented, field wide optimization is pursued and multi-well networked model is established. The application of the attained results is expected to have real impact in improving the gas lift performance in similar fields in the Middle-East and worldwide.
O. A novel machine learning model for autonomous analysis and diagnosis of well integrity failures in artificial-lift production systems. Advances in
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