Reservoir characterization, machine learning and big data - An offshore California case study
Title
Reservoir characterization, machine learning and big data - An offshore California case study
Subject
Big data
Offshore oil well production
Information management
Reservoir management
Supervised learning
Conservation
Description
In order to robustly characterize a reservoir and make reservoir management decisions, it is paramount that an integrated and comprehensive study use all available static and dynamic data including petrophysical, geological, geophysical, engineering, and production data sets. These large vintage data sets are often available but are typically underutilized because of poor data management practices and lack of forward-looking data strategies. This paper presents the results of a supervised classification machine learning (ML) algorithm that accurately identifies reservoir quality associated with the most favorable production trends. The algorithm was trained and tested using log curves, seismic attributes, production, and sidewall core sample data sets. Lessons learned show the importance of managing data in a way that is complementary to machine learning. In addition, a flexible and forward-looking data strategy provides for rapid and efficient evaluation of reservoir characteristics. These quantitative machine learning results can be factored into field development strategies and help optimize efficiency and capital allocation. Integrating this machine learning workflow supports resource conservation efforts by ensuring optimal production of offshore hydrocarbon resources while minimizing impacts to the environment. Copyright 2020 Society of Petroleum Engineers.
Creator
Ojukwu, Chima
Smith, Kevin
Kadkhodayan, Nadia
Leung, Mark
Baldwin, Kimberly
Publisher
SPE Nigeria Annual International Conference and Exhibition 2020, NAIC 2020, August 11, 2020 - August 13, 2020
Date
2020
Type
conferencePaper
Identifier
10.2118/203642-MS
URL
http://dx.doi.org/10.2118/203642-MS
Citation
Ojukwu, Chima et al., “Reservoir characterization, machine learning and big data - An offshore California case study,” Lamar University Midstream Center Research, accessed May 18, 2024, https://lumc.omeka.net/items/show/28646.