The oil industry has installed fiber optic sensing cables in holes to better understand why hydraulic fracturing does not release trapped oil at expected rates in shale reservoirs, but the massive flows of information received are difficult to analyze. . Today, a team of researchers from Texas A&M University and the Colorado School of Mines created an algorithm to clean up this underground data and provide a clear view of how and where these fracking processes succeed and fail.

“Our quantitative characterization retrieves more information about fracture geometries in a reservoir than just a qualitative analysis,” said Dr. Kan Wu, associate professor and faculty member of Chevron Corporation in Harold’s petroleum engineering department. Vance. “We have tested our algorithm and have already applied it in the field.

The results were published in the Society of Petroleum Engineers’ SPE production and operation newspaper.

Traditional methods of interpreting data, while incredibly useful to engineers, are based strictly on qualitative information or probabilities based on information models. In contrast, the algorithm was developed to collect countable quantitative data, such as changes in temperature, pressure or rock deformation in a reservoir. It recognizes the results that occurred to create the changes and accurately models how far and how fast the fractures moved, the directions they went and their size.

Low Frequency Distributed Acoustic Detection (DAS) data collection has only been around for five years, so not all information received from fiber-optic wells has been fully deciphered. In addition, each well has its own range of characteristics due to the huge variations in underground structures. This complexity is why Wu and his colleagues, Professor George Moridis, Professor and Robert L. Whiting Chair, and Dr Ge Jin, Assistant Professor of Geophysics at Mines, took a long time to develop. meticulously their algorithm.

First, the researchers tested the algorithm’s ability to clean data and interpret simple flows from known fracture processes. That way, they could go back or reverse the information to find the starting point for the growth of a fracture. As the algorithm has been extended to understand more complex information, they have improved its ability to think forward and predict how new and complex fractures start and develop.

Wu is an expert in rock mechanics, Jin an expert in geophysics and DAS technology, and Moridis is an expert in advanced numerical methods and high performance computing of coupled processes. Due to the multidisciplinary background of the project team, the algorithm has incredible flexibility to grow and adapt to the type of data it receives. For example, Yongzan Liu, the project’s graduate student for over two years, is now a postdoctoral researcher using similar methods and modeling on fiber optic data from hydrated sediments to monitor natural gas production for the Lawrence Berkeley. National Laboratory.

Liu, Wu, Moridis and Jin are the first to develop this type of algorithm and publish the results. The goal of their research is to eventually automate the algorithm so that feedback from fracturing events occurs in near real time on a drilling site. In this way, engineers can quickly tailor fracture design efforts to the particular composition of each well.

“The industry needs this type of tool to understand fracture geometry and monitor fracture propagation,” Wu said. “The more efficient it becomes, the better it will help optimize hydraulic fracture and completion designs and to maximize well production. “

– This press release was originally published on the Texas A&M University website