AI-Based Decline-Curve Analysis Manages Reservoir Performance

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 197142, “Artificial-Intelligence-Based, Automated Decline-Curve Analysis for Reservoir Performance Management: A Giant Sandstone Reservoir Case Study,” by Amir Kianinejad, Rami Kansao, and Agustin Maqui, Quantum Reservoir Impact, et al., prepared for the 2019 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11-14 November. The paper has not been peer reviewed. Decline-curve analysis (DCA) is one of the more widely used forms of data analysis that evaluates well behavior and forecasts future well and field production and reserves. In the complete paper, the authors develop and deploy technologies that apply DCA methods to wells in an unbiased, systematic, intelligent, and automated fashion. This method contrasts with manual DCA, the common practice of the industry. Introduction DCA is used commonly to estimate reservoir and well productivity and ultimate recovery and evaluate reserves. Such analyses are usually performed manually through a curve-fitting process by reservoir and production engineers using their best judgement and experience. Subjectivity is often a large component of such estimations, as are the experience and objectives of the evaluator. The authors provide an alternative by developing and leveraging an augmented artificial-intelligence (AI), data-driven approach. Geological and engineering features are considered as well as operational conditions to bring domain knowledge into the analysis, resulting in more-accurate and -reliable predictions. In addition, the method is ex tended to conduct DCA probabilistically using quantile regression techniques.