Predictive Analytics: Development and Deployment of Upstream Data Driven Models

摘要

The oil and gas industry is engulfed by a plethora of disparate data collated across multiple geoscientific and siloed disciplines. Moreover, the data are growing exponentially as digital oilfields are being implemented in some fashion to manage conventional and unconventional assets. Performing exploratory data analysis and generating data marts tailored to specific advanced analytical workflows are cornerstones to enable development and deployment of predictive models that are data driven, both in real-time and across historical data sets. To build data driven models that can predict under uncertainty is essential to rapidly identify multi-dimensional parameters in a multivariate environment and thus surface hidden patterns and relationships in data that subsequently reduce time and resources in the critical decision-making cycles. With improved workflows and advances in High Performance Computing, it is now possible to ascertain risk and quantify uncertainty for very large populations of data without sampling and losing knowledge garnered by predictive models driven by the data and not by empirical petroleum engineering algorithms or deterministic methodologies. By marrying the stochastic with the interpretive school of thought, the upstream community can maintain robust data driven models that are kept current as new data are introduced. This paper draws upon two case studies that ameliorate the path from raw data to invaluable knowledge. We shall look at a suite of predictive models driven by real-time data that were built upon patterns surfaced in historical data. These models have been implemented to identify optimized drilling and production strategies in the North American tight gas plays and acid stimulation strategies in the Gulf of Mexico.