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Unlocking Unconventional Reservoirs With Data Analytics, Machine Learning, and Artificial Intelligence

The concept of the digital oil field has been around for many years and the industry is beginning to experience the benefits of data analytics, machine learning, and artificial intelligence (AI).

The upside of digital technology is it can improve operational efficiencies and reduce costs. The downside of digitization is that many companies have neither the resources nor the expertise to collect, analyze, and interpret a critical mass of data while maximizing its economic return.

Predictive Behavior for Unconventional Reservoirs

As North America shale plays are once again becoming economically viable, operators in all major basins have fast-adopted best practices to optimize drilling and completion processes to drive down the lifting costs.

Adoption of data-driven analytics to improve completion design, drive efficiency, and yield economic gains has been less swift. In years past, an operator would perform analysis on a single well completion and make decisions using publicly and privately available data, well logs, or sometimes nothing more than experience gained through the years.

As the industry moves to more complex multipad, multiwell completion designs, intelligent completion optimization will require more sophisticated algorithms to improve decision making. The question remains as to how operators can use the vast amount of data readily available in the public domain and private data to develop and train models that enable them to realize these operational efficiencies and increase estimated ultimate recoveries.

This is where the use of analytics and AI can directly drive efficiencies to unlock the production potential of unconventional reservoirs specifically during hydraulic fracturing. The growing volume and availability of completion and production public data creates a revolutionary situation in which pad planning and completion design becomes possible with pure AI methods.

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Unlocking Unconventional Reservoirs With Data Analytics, Machine Learning, and Artificial Intelligence

Dan Fu, Director of Technology, BJ Services

01 January 2019

Volume: 71 | Issue: 1

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