Why we need Filtered Drill Cuttings Analysis

January 11, 2019

“AI and Machine Learning: Propagating Rock Properties Using Filtered Drill Cuttings Analysis”

BACKGROUND: Within the oil and gas industry, artificial intelligence (AI) and machine learning to analyze large data sets generated by past activity are providing many advancements in exploration and production. Often this is simply referred to as "Big Data", which encompasses large amounts of data that are manipulated using AI and machine learning algorithms. At present this is primarily aimed at correlating well production with various factual data. However one refers to the subject, the end result is that large amounts of data can be used to make predictions, provide understandings of various phenomena and to solve complicated problems.

"Big Data" analysis currently most often considers clear observations such as lateral well drilling information, completion records, and well performance, with relatively little information of the details of the rock properties considered. To a large extent this is because details on the rock--particularly the rock heterogeneity--are not well known or understood, and yet can have profound influence on production. This makes integrating rock properties and heterogeneity into the "Big Data" workflow very important. Hence, propagation of formation measured rock properties to predict the rock where only limited rock data are available could be valuable and could be truly game changing.

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by Sidney Green, Enhanced Production, Inc., Patrick Gathogo, Alexander Nadeev, Rock Microscopy LLC.