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
Well integrity has become a crucial field with increased focus and being published intensively in industry researches. It is important to maintain the integrity of the individual well to ensure that wells operate as expected for their designated life (or higher) with all risks kept as low as reasonably practicable, or as specified. Machine learning (ML) and artificial intelligence (AI) models are used intensively in oil and gas industry nowadays. ML concept is based on powerful algorithms and robust database. Developing an efficient classification model for well integrity (WI) anomalies is now feasible because of having enormous number of well failures and well barrier integrity tests, and analyses in the database. Circa 9000 dataset points were collected from WI tests performed for 800 wells in Gulf of Suez, Egypt for almost 10 years. Moreover, those data have been quality-controlled and quality-assured by experienced engineers. The data contain different forms of WI failures. The contributing parameter set includes a total of 23 barrier elements. Data were structured and fed into 11 different ML algorithms to build an automated systematic tool for calculating imposed risk category of any well. Comparison analysis for the deployed models was performed to infer the best predictive model that can be relied on. 11 models include both supervised and ensemble learning algorithms such as random forest, support vector machine (SVM), decision tree and scalable boosting techniques. Out of 11 models, the results showed that extreme gradient boosting (XGB), categorical boosting (CatBoost), and decision tree are the most reliable algorithms. Moreover, novel evaluation metrics for confusion matrix of each model have been introduced to overcome the problem of existing metrics which don't consider domain knowledge during model evaluation. The innovated model will help to utilize company resources efficiently and dedicate personnel efforts to wells with the high-risk. As a result, progressive improvements on business, safety, environment, and performance of the business. This paper would be a milestone in the design and creation of the Well Integrity Database Management Program through the combination of integrity and ML.
Well integrity (WI) impairments in oil and gas (O&G) wells are one of the most formidable challenges in the petroleum industry. Managing WI for different groups of well services necessitates precise assessment of risk level. When WI classification and risk assessment are performed using traditional methods such as spreadsheets, failures of well barriers will result in complicated and challenging WI management, especially in mature O&G fields. Industrial practices, then, started moving toward likelihood/ severity matrices which turned out later to be misleading in many cases due to possibility of having skewness in failure data. Developing a reliable model for classifying level of WI impairment is becoming more crucial for the industry. Artificial intelligence (AI) includes advanced algorithms that use machine learning (ML) and computing powers efficiently for predictive analytics. The main objective of this work is to develop ML models for the detection of integrity anomalies and early recognition of well failures. Most common ML algorithms in data science include; random forest, logistic regression, quadratic discriminant analysis, and boosting techniques. This model establishment comes after initial data gathering, pre-processing, and feature engineering. These models can iterate different failure scenarios considering all barrier elements that could contribute to the WI envelope. Thousands of WI data arrays can be literally collected and fed into ML models after being processed and structured properly. The new model presented in this paper can detect different WI anomalies and accurate analysis of failures can be achieved. This emphasizes that managing overall risks of WI failures is a robust and practical approach for direct implementation in mature fields. It also, creates additional enhancement for WI management. This perspective will improve efficiency of operations in addition to having the privilege of universality, where it can be applicable for different well groups. The rising wave of digitalization is anticipated to improve field operations, business performance, and production safety.
Well integrity (WI) is a growing concern in the oil and gas (O&G) industry as fields mature and WI problems increase. In project management, maturity denotes having perfect condition to attain the organization's objectives. Applying the same principle to O&G industry, provides a pathway and basis to achieve excellence in WI management in O&G fields. Maturity of WI management has a direct impact on performance, assurance, compliance, and most importantly operation safety. A case study from a brown field has been conducted to evaluate the effect of WI management maturity on field performance. The application of well integrity management system (WIMS) in an offshore brown oilfield has been studied. The journey of WI maturity has been presented in detail. All maturity phases have been analyzed starting from program initiation, up to predicting WI failures using machine learning (ML) algorithms. Three maturity models, that have been developed by different organizations and individuals, were selected for benchmarking of measured maturity level in the WIMS applied. They were selected as they show independency from industry/organization's type. The three models are; organizational project management maturity model (Opm3), capability maturity model integration (CMMI), and Kerzner project management maturity model (KPMMM). The attained results indicated that K-PMMM provides the best description and level determination of maturity level with WIMS applied. Moreover, it is highly recommended to implement maturity models by including all processes and subsystems in the WIMS, to have better resolution of system gaps.
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