Abstract

The Permian Basin is a hotspot for oil and gas investment, attracting numerous operators who seek to define optimal development strategies for unconventional reservoirs. To tackle the complexity of this goal, we propose an Artificial Intelligence (AI) driven reservoir engineering workflow that enhances the efficiency and quality of decision-making in field development. This workflow integrates geological, production, and reservoir data to evaluate and construct optimal development strategies tailored to specific business objectives and constraints.

At the core of the developed workflow is an integrated big data warehouse, equipped with on-demand capabilities for the efficient visualization and analysis of diverse data sets. It includes a series of purpose-built AI models that operate concurrently. Large language models (LLMs) autonomously extract vital information from various sources, such as well reports and communication documents. Machine learning models are employed for production forecasting based on historical data, geological characterization, and development strategies.

This integrated workflow significantly improves the efficiency of numerous reservoirs engineering tasks, including resource and reserve evaluation, field surveillance, development planning, and project decision-making. The creation of purpose-built machine learning models, informed by engineering principles and physics, represents an innovative, efficient, and dependable approach that could be advantageous for researchers and practitioners in the broader field of machine learning.

Introduction

The Permian Basin boasts significant potential due to its economically viable, multi-bench geological layers. However, the complexity arising from its diverse geological characteristics also demands tailored and optimized development strategies. Currently, it produces more than 6MMBO/D and more than 25 BCF/D, making it the largest producing region in the United States (Error! Reference source not found.). Many operators in this basin have capitalized on the use of proven technology, the prolific nature of the basin, and in-place volumes across multiple benches to maximize production and value. Nonetheless, the challenge to unlock additional locations and mitigate depletion and well communication persists and needs to be addressed and quantified.

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