Increasing yield by 40% in an unconventional way
ScienceDirect helps an E&P team create a predictive geological model for enhanced oil recovery in a limited amount of time and without significant capital investment
A team leader at an independent US-based oil and gas company explains how the vast wealth of field data on ScienceDirect enabled him to build a simulated model of an oilfield, predict the field’s future behavior, and generate actionable cost and profit estimates—all by having access to the research published by other teams around the world.
When a company’s Texas-based oilfield faced declining production and decreasing revenues, team members began to investigate the field and discovered their records were surprisingly out-of-date: they had no actual data from the core, no up-to-date logs of production and no pressure-volume-temperature (PVT) analyses of any well in the field. They needed new financing to drill more wells in the field, yet no one wanted to finance the project without clear data on the field’s performance.
Using ScienceDirect is a major part of my daily routine in the pre-execution phase of project. I’m able to discover actionable data in a fraction of the time I’d spend on other search engines—so I can deliver accurate predictions and recommendations before my competitors do.
The research director realized an instant cost savings of up to $50,000 by using ScienceDirect rather than hiring an outside consulting agency. More importantly, his simulation model predicted that the oilfield would generate a 40 percent yield improvement through the use of specific methods and techniques researched on ScienceDirect. Moreover, the model demonstrated that they could increase recovery from the field without the investment of significant capital expenses. The company was able to secure the financing needed to continue to develop the field, extending both the productivity and financial return from the field.
I’ve built my entire workflow around ScienceDirect. It’s far beyond any other resource in terms of data availability, so it’s always my number-one choice when I need to assemble a large data set in the shortest time possible.
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