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Machine-Learning Approach Identifies Wolfcamp Reservoirs

This paper discusses a project with the objective of leveraging prestack and poststack seismic data in order to reconstruct 3D images of thin, discontinuous, oil-filled packstone pay facies of the Upper and Lower Wolfcamp formation. The classification results were created by neural networks, which can be used as a substitute for traditional amplitude-vs.-offset, inversion, and cross-plotting techniques for seismic reservoir characterization.

Introduction

The problem encountered by the operator in this oil field is that the reservoir, an oil-filled packstone, is thin and laterally discontinuous. Despite having collected a high-resolution, state-of-the-art 3D seismic survey with usable frequencies up to 138 Hz, and despite having generated seismic attribute volumes in order to assist interpretation, the operator was unable to generate an interpretation manually that matched the rock-type interpretation at the wells. Therefore, the decision was taken to supplement the human interpretation with a machine-learning methodology.

Geological Setting

The East Soldier Mount (ESM) study area is approximately 200 km northeast of Midland, Texas, USA, on the eastern shelf of the Permian Basin. The study area contains both Upper and Lower Wolfcamp oil-filled packstones, which are thin and laterally discontinuous. Bioturbation and oolitic shoals caused the initial porosity; however, much of the porosity was occluded by cementation after burial. The porosity was enhanced by fracturing that occurred after burial, caused by differential compaction beneath and tectonic faulting in the deeper formations. Many millions of years after burial, oil leaked into the Tannehill sand (Middle Wolfcamp) detrital, then migrated up the detrital zone into the delta, which is located 2 km west of the study area. Then, the oil migrated out of the delta and into the study area itself. Middle Wolfcamp deltaic sands are not collocated with the seismic survey and thus are not part of this study. They were, however, the conduit for oil migration into the ESM wells.

The total drilled depth for these vertical wells is approximately 1500 m. Each successful well produces approximately 3–4 million bbl of reserves. These wells flow naturally, without the need for hydraulic fracturing.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 193002, “Exploring for Wolfcamp Reservoirs, Eastern Shelf of the Permian Basin, Using a Machine-Learning Approach,” by Bruno de Ribet and Peter Wang, Emerson; Monte Meers, independent geologist; Howard Renick, independent geophysicist; Russ Creath, Hardin International; and Ryan McKee, RAM Imaging, prepared for the 2018 SPE Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11–14 November. The paper has not been peer reviewed.
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Machine-Learning Approach Identifies Wolfcamp Reservoirs

01 March 2019

Volume: 71 | Issue: 3

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