Reducing Risk in the Petroleum Industry

Reducing Risk in the Petroleum Industry: Machine Data and Human Intelligence

Introduction

To the buzzword-weary, Big Data has become the latest in the infinite series of technologies that “change the world as we know it.” But amidst the hype, there is an epochal shift: the current exponential growth in data is unprecedented and is not showing any signs of slowing down.

Compared to the short timelines of technology startups, the long history of the petroleum industry provides stark examples to illustrate this change. Seismic research happens early in the exploration and extraction stages. In 1990, one square kilometer yielded 300 megabytes of seismic data. In 2015, this was 10 petabytes—33 million times more, according to Satyam Priyadarshy, chief data scientist at Halliburton. First principles, intuition, and manual arts are overwhelmed by this volume and variety of data. Data-driven models, however, can derive immense value from this data flood. This report gathers highlights from Strata+Hadoop World conferences that showcase the use of data science to minimize risk in the petroleum industry.

In the short term, data can be used to mitigate operational risk. Given good data, machine learning can be used to optimize well completion parameters such as the amount and type of proppant used. Ben Hamner, chief technology officer at the data science startup, Kaggle, says these are the biggest drivers of well cost and the biggest expense when drilling the well. They also have a proportionate ...