Artificial-Intelligence-Based, Automated Decline Curve Analysis for Reservoir Performance Management: A Giant Sandstone Reservoir Case Study

Abstract Decline curve analysis (DCA) is one of the most widely used forms of data analysis that evaluates well behavior and forecasts future well and field production and reserves. Usually, this practice is done manually, making analysis of assets with a large number of wells cumbersome and time-consuming. Moreover, results are subject to alternate interpretations, mostly as a function of experience and objectives of the evaluator. In this work, despite the common practice of the industry, i.e. manual DCA, we developed and deployed cutting-edge technologies that intelligently apply DCA methods to any number of wells in an unbiased, systematic, intelligent, and automated fashion. The tool reads production data, and multidisciplinary well information (e.g., drilling and completion data, geological data, artificial lift information, etc.). Then it performs cluster analysis using unsupervised machine learning and pattern recognition to partition the dataset into internally homogeneous and externally distinct groups. This cluster analysis is later used for type-curve generation for wells with short production history. For wells with long enough history, the tool first detects production events through a fully automated event detection algorithm without any human interference. Since production events are highly correlated with real-time events, it also cross-validates with the operating conditions. Next, the last event is selected, and a decline curve is fitted using advanced nonlinear optimization and minimization algorithms. This leads to a reliable and unbiased prediction. For each cluster, a type curve is computed that truly captures the underlying production behavior of the wells that belong to the same group or cluster, and then is applied to the wells with short production history within that cluster. To capture the probabilistic nature of such analysis and quantify the inherent uncertainty, we extended the method to a probabilistic DCA using quantile regression. We successfully deployed this technology/tool to a giant Middle Eastern reservoir, with more than 2,000 wells and 70 years of production. Our predicted aggregated field decline rate is in good agreement with the client's reservoir simulation results run under the "do-nothing" scenario. While performing traditional DCA for such a field would require several weeks and significant resources, our automated solution integrates all real-life events/information and provides a comprehensive analysis in field, cluster and well level. In addition, our results are "unbiased," as it is not subject to human errors or evaluator's interpretations. Our robust and intelligent DCA allows for exhaustive evaluation of production trends and opportunities in fields across time, production zones, well types, and any combinations of the above. The results demonstrate the effectiveness of the automated DCA to rapidly execute decline curve analysis for a large number of wells. The accuracy is improved significantly through automatic event detection, cross-validation of events, curve fitting optimization, quantile regression, and cluster-based type-curving.

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