Finding Your Next Sweet Spot Using Dirty DNA

Abstract

Biodentify is a Netherlands-based startup that wants to help oil and gas explorers zero in on sweet spots using microbial DNA. Unlike another new DNA analytical method, which uses drill cuttings as samples to learn about unconventional productivity drivers, the approach this firm is focused on uses soil samples taken just a foot below the surface to search for good drilling locations. The company’s technology is borrowed from a still emerging medical science breakthrough that uses saliva to test for tumors as opposed to a much more invasive biopsy. In looking for alternative uses, it was thought the same process could be used to predict oil and gas deposits based on microbial reactions to micro-seepages of gas molecules. Through oxidation, certain microbes will thrive on gas seepages, while others will find the rising gases to be toxic and die. Either outcome provides a usable signal that a machine learning model can turn into an easy-to-interpret prediction map. “Many say it’s too good to be true—what’s the link between the surface and what’s down there,” said Chris Te Stroet, the director of technology and operations at Biodentify. “The causal link is that you have a hydrocarbon accumulation—the sweet spots—and a highly mobile area, probably because of a good natural fracture network.” Te Stroet, who has led commercialization efforts of new oil and gas technologies since the mid-1990s, said a single sample may contain as many as 300,000 microbial species—most of which are newly discovered. But Biodentify has found that only 50 to 200 of them serve as key indicators of a positive or negative signal. With hundreds of samples in hand a map is then produced to help explorers derisk potential drilling locations. The company says its analytical method is 70–90% accurate; the high end of that range is achievable if information can be gleaned from existing wells in the target area. Validation Study Hits Close to the Mark Despite a limited track record, the company is confident in its accuracy thanks in part to a blind study it performed with an unconventional operator in the Haynesville Shale of western Louisiana. The work was done in a producing field and was 72% accurate in predicting sweet spots. When actual production data were included in a final modeling exercise, that figure jumped to 86% and 31 “virtual” drilling locations were identified within a sweet spot where the operator had drilled 27 high-producing wells.