Reservoir characterization, machine learning and big data - An offshorecalifornia case study

Reservoir characterization, machine learning and big data - An offshorecalifornia case study

Big data

Conservation

Information management

Natural resources management

Offshore oil well production

Reservoir management

Supervised learning

In order to robustly characterize a reservoir and make reservoir management decisions, it is paramount thatan 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 oftenavailable 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 thataccurately identifies reservoir quality associated with the most favorable production trends. The algorithmwas 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 machinelearning. In addition, a flexible and forward-looking data strategy provides for rapid and efficientevaluation of reservoir characteristics. These quantitative machine learning results can be factored into fielddevelopment strategies and help optimize efficiency and capital allocation. Integrating this machine learningworkflow supports resource conservation efforts by ensuring optimal production of offshore hydrocarbonresources while minimizing impacts to the environment. 2020 Society of Petroleum Engineers - SPE Nigeria Annual International Conference and Exhibition 2020, NAIC 2020.All right reserved.

Ojukwu, Chima

Smith, Kevin

Kadkhodayan, Nadia

Leung, Mark

Baldwin, Kimberly

SPE Nigeria Annual International Conference and Exhibition 2020, NAIC 2020, August 11, 2020 - August 13, 2020

2020

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