Augmented Artificial Intelligence Improves Data Analytics in Heavy-Oil Reservoirs

Abstract

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 193650, “Augmented-Artificial-Intelligence Solutions for Heavy-Oil Reservoirs: Innovative Work Flows That Build From Smart Analytics, Machine Learning, and Expert-Based Systems,” by David Castineira, Xiang Zhai, and Hamed Darabi, Quantum Reservoir Impact Group, prepared for the 2018 SPE International Heavy Oil Conference and Exhibition, Kuwait City, Kuwait, 10–12 December. The paper has not been peer reviewed. Recently, many heavy-oil fields have seen exponentially higher volumes of data made available as a result of omnipresent connectivity. Existing data platforms have focused traditionally on solving the problem of data storage and access. The more-complex problem of true knowledge discovery and systematic value creation from the massive amount of data is less frequently addressed. The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields. Introduction Optimal reservoir management for heavy-oil reservoirs requires systematic solutions that combine both engineering ability and advanced analytics. The authors believe that this requirement is addressed by what they call augmented artificial intelligence (AAI), a process inspired by the intelligence-amplification concept in which machine learning and human expertise are combined to improve solutions derived by systems that learn without any type of input from engineers or geoscientists. Practical deployment of AAI will involve automated work flows that use solid technical expertise and proven processes to transform field data into more-effective reservoir-management solutions. Even with rapid data-preprocessing solutions in place, developing an optimal reservoir-management framework for heavy-oil assets is inherently complex. Identifying key recovery obstacles (KROs) and field-development plans (FDPs) typically takes many months, involving a large team of experts and the construction of sophisticated full-field simulation models. The recommendation is that automated work flows and AAI solutions are combined to identify those KROs rapidly and prepare robust FDPs that increase production and optimize current operations. Perhaps the less-intuitive step in developing systematic solutions for heavy-oil fields is the process of developing a quantitative reservoir diagnostic framework. This process must build from big-data analytics platforms and an array of analytical, numerical, and empirical models combined to deliver a catalog of KROs affecting field performance. To this end, the entire historical set of well, field, and reservoir data must be processed and input into this diagnostics platform. Once the KROs are understood, the next step is to translate the diagnostics into detailed action plans in the field that can generate production, reserves, or capital-efficiency improvements. This paper aims to offer an alternative approach to traditional work flows that identify recovery obstacles and development opportunities in heavy-oil fields by labor-intensive solutions. In contrast, the authors propose a systematic framework that provides three key advantages: Execution time is fast, and an initial opportunity inventory can be generated. The user can choose from multiple algorithms and methods to customize the technology to unique field/reservoir complexities. The core algorithms are data-driven, integrate multidisciplinary data sets, and leave little room for the biases of the user, which allows for a consistent and repeatable analysis.