Prediction of Surface Oil Rates for Volatile Oil and Gas Condensate Reservoirs Using Artificial Intelligence Techniques

Abstract

Allocated well production rates are crucial to evaluate the well performance. Test separators and flow meters were replaced with choke formulas due to economic and technical issues special for high gas-oil ratio (GOR) reservoirs. This study implements Adaptive network-based fuzzy logic (ANFIS), and functional networks (FN) techniques to predict the oil rate through wellhead chokes. A set of data containing 1,200 wells was obtained from actual oil fields in the Middle East. The dataset included GOR, upstream and downstream pressure, choke size and actual oil and gas rates based on well test. GOR varied from 1,000 to 9,265 scf/stb, while oil rates ranged between 1,156 and 7,982 stb/d. Around 650 wells were flowing under critical flow conditions, while the rest were subcritical. Seventy percent of the data was used to train the AI models, while thirty percent of the data was used to test and validate these models. The developed AI models were then compared against the previous formulas. For subcritical flow conditions, rate prediction was correlated to both upstream and downstream pressures. While at critical flow conditions, changes in the downstream pressure did not affect the prediction of the production rates. For each AI method, two models were developed for subcritical flow and critical flow conditions. The average absolute percent error (AAPE) in the case of subcritical flow for ANFIS and FN were 0.88, and 1.01%, respectively. While in the case of critical flow, the AAPE values were 1.07, and 1.3% for ANFIS and FN models, respectively. All developed AI models outperform the published formulas, where the AAPE values for published formulas were higher than 34%. The results from this study will greatly assist petroleum engineers to predict the oil and gas rates based on available data from wellhead chokes in real-time with no need for additional operational costs or field intervention.

Applications of AI in oil and gas projects towards sustainable development: a systematic literature review

“…The high-risk environment creates a negative impact on the construction of oil and gas projects and further has a critical association with economic and environmental concerns. Most of the time, when these projects are initiated under the control of large construction companies, it is entirely possible that the projects may go through substantial delays, eventually disturbing sustainability (Al Dhaif et al 2022 ). The societal impact can also not be ignored because it is one of the prominent elements of sustainability where construction projects are initiated.…”

“…AI is considered a human-friendly and also environmentally friendly technology which is commonly adopted in the field of civil engineering as it has shown a significant positive impact on the overall industry of construction (Al Dhaif et al 2022 ; Ibrahim and Elkatatny 2022 ). The world is now moving towards a direction where the concept of energy saving is currently appreciated.…”

“…Artificial Intelligence (AI) is one of the leading technologies in development. Still, it is being adopted in the oil and gas industry to gain a competitive advantage and reduce the challenges that various oil and gas projects face worldwide (Al Dhaif et al 2022 ). The technology has improved the automation in the oil and gas projects, further enhancing reliability and maintaining the incorporation of digital technology in the overall process.…”

Prediction of Water Saturation in Tight Gas Sandstone Formation Using Artificial Intelligence

“…AI has been applied in different problems in the petroleum field. Several AI systems including random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), function networks (FN), artificial neural network (ANN), and support vector machine (SVM) can be applied to predict specific factors from available logging data without adding more cost or well intervention. − …”

Application of Machine Learning to Predict Estimated Ultimate Recovery for Multistage Hydraulically Fractured Wells in Niobrara Shale Formation

“…Machine learning (ML) has been used for diverse applications in oil and gas and environmental problems. Different ML tools such as artificial neural network (ANN), random forest (RF), function networks (FNs), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) can be applied to predict specific parameters from readily accessible data [6,[23][24][25][26].…”