Machine Learning Models To Automatically Validate Petroleum Production Tests
- Maria Clara Machado de Almeida Duque (Universidade Federal do Rio de Janeiro) | Gabriela Souza Chaves (Universidade Federal do Rio de Janeiro) | Danielle de Oliveira Monteiro (Universidade Federal do Rio de Janeiro) | Luciana Velasco Medani (Universidade Federal do Rio de Janeiro) | Virgílio José Martins Ferreira Filho (Universidade Federal do Rio de Janeiro)
- Document ID
- Society of Petroleum Engineers
- SPE Latin American and Caribbean Petroleum Engineering Conference, 27-31 July, Virtual
- Publication Date
- Document Type
- Conference Paper
- 2020. Society of Petroleum Engineers
- 5.6 Formation Evaluation & Management, 5.6.4 Drillstem/Well Testing, 6.1.5 Human Resources, Competence and Training, 6 Health, Safety, Security, Environment and Social Responsibility, 5 Reservoir Desciption & Dynamics, 5.2 Fluid Characterization, 5.2.2 Fluid Modeling, Equations of State, 6.1 HSSE & Social Responsibility Management, 7.6.6 Artificial Intelligence, 2.1.3 Completion Equipment
- Production Tests, Petroleum Production, Machine Learning
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Well production in oil fields is a dynamic and complex activity. The patterns and characteristics inherent to the well, such as pressures and flow rates, are changing based on production time and the fluid composition – a complex multiphase mixture composed of oil, water, and gas. Thus, it is necessary to evaluate well behavior with periodic production tests. This paper proposes an automatic tool based on machine learning models to assist the production tests validation process in a quick manner. The developed methodology was applied to 13 representative wells of a Brazilian offshore oil field. For each examined well, a dataset is created with operation variables obtained from valid and invalid production tests. Six classification algorithms are analyzed, Logistic Regression, Naïve Bayes classifier, K-Nearest Neighbor, Decision Tree, Random Forest and Support Vector Machine (SVM) in reason to automatically label a new production test as valid or invalid, according to production historical data for the well. The dataset was divided into training and validation sets. The training set was used to perform feature selection, to calibrate and choose the proper model. The validation set was then used at the end of the procedure to evaluate obtained results, by comparing the model’s output with real test labels. From the results obtained in the case study, it was possible to identify that IGLR (Injection Gas/Liquid Ratio), oil flow rate and the pressure loss between wellhead and platform were representatives for most of the wells, which implies that these variables have a huge influence at the production well test validation. Furthermore, the validation set indicates that SVM and logistic regression were the models with the best performance. Besides that, accurate results were achieved, since the model correctly classified at least 5 of the 6 tests in 70% of wells analyzed, and for the remaining wells, 4 of 6 production tests.
|File Size||844 KB||Number of Pages||15|
BALAJI, K.; RABIEI, M.; SUICMEZ, V.; Status of Data-Driven Methods and their Applications in Oil and Gas Industry. 80th EAGE Conference and Exhibition - Society of Petroleum Engineers, 2018. doi: 10.2118/190812-MS.
BRUNI, T.; LENTINI, A.; VENTURA, S.; A Technically Rigorous and Fully Automated System for Performance Monitoring and Production Test Validation. SPE International Improved Oil Recovery Conference in Asia Pacific, 2003. Society of Petroleum Engineers. doi:10.2118/84881-MS.
OLSEN, S.; NORDTVEDT, J.-E. Experience from the Use of Automatic Well-Test Analysis. SPE Annual Technical Conference and Exhibition, 2006. Society of Petroleum Engineers. doi:10.2118/102920-MS.
SUBRAHMANYA, N.; XU, P.; EL-BAKRY, A.; REYNOLDS, C. Advanced Machine Learning Methods for Production Data Pattern Recognition. SPE Intelligent Energy Conference & Exhibition. p.9, 2014. Society of Petroleum Engineers. doi:10.2118/167839-MS.