Artificial Intelligence Unleashes Potential of Relative Permeability

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 207855, “Unleashing the Potential of Relative Permeability Using Artificial Intelligence,” by Abdur Rahman Shah, SPE, Schlumberger; Kassem Ghorayeb, SPE, American University of Beirut; and Hussein Mustapha, Schlumberger, et al. The paper has not been peer reviewed. _ To unlock the potential of large relative permeability (Kr) databases, the work flow proposed in the complete paper integrates data analysis, machine learning (ML), and artificial intelligence (AI). The work flow allows for the automated generation of a clean database and a digital twin of Kr data, using AI to identify analog data from nearby fields by extending the rock-typing scheme across multiple fields for the same formation. Introduction Accurate Kr curves are critical because they can improve reservoir characterization, reduce uncertainty in history matching and production forecasting, and provide robust and reliable field development plans. However, preparing special core analysis (SCAL) data, particularly Kr curves, as an input for reservoir simulation traditionally has been a highly manual, time-consuming, labor-intensive process. Furthermore, no automated tools are available currently that perform a full quality review of Kr data using analytical or numerical methods. As a result, the quality and representativeness of Kr data used in reservoir simulation models is frequently inadequately checked. Furthermore, no tools exist to generate Kr curves based on analog field data in fields where Kr data is either missing or scarce. Integrated Work Flow The solution discussed in this paper aims to address challenges and automate processes using a combination of conventional algorithms and ML. It begins with a thorough quality check and cleanup of Kr laboratory data before using analog data to build Kr curves for a specific field or reservoir. The latter is especially important considering the challenges that arise when attempting to build on analog field data in the presence of large, corporate, regional, or country-scale databases. The approach classifies rocks using ML-proxy rock typing, which is subsequently used to find Kr equivalents. After that, these analogs can be used to fill up any data gaps for reservoir simulation in any discipline.