In 2017-2019 Petroleum Industry was focused on the Unconventional projects onshore USA and engaged in #digitaltransformation programs. While process optimization and automation of repetitive workflows are obvious candidates for cost reduction initiatives in the Unconventional play and in established assets, the approach to exploration business development in Petroleum Industry needs to solve a different set of challenges. Opportunity assessment in Upstream Oil and Gas requires management to make decisions with partial data and limited time while the relationships between variable in the available datasets are non-unique.
Exploration is always short on time and is an innovation in itself because every basin is different,every play is different. The job of an explorer is to find a proper context for an evaluation of each new opportunity and to answer the questions: What is common between different play assessment projects and what is different, what do we know or can assume from our previous experience? How can we quickly look at a large volume of data and put it into an appropriate setting? With a growing number of variables required in the analysis of the investment opportunity in a rapidly changing business landscape people naturally are needing to simplify the task. Where could these simplifications be made?
Clearly, poorly selected exploration blocks will not become profitable if artificial intelligence applied to the data analysis after the blocks are acquired. The skills in using the method of Play-Based opportunity selection are coming from experience in working multiple sedimentary basins and sharing the stories of tested models. Digital methods should be designed to help explorers to “remember” the vast number of completed projects, speedup computations, and highlight the dependencies between seemingly different parts of the business.
Large regional 2D seismic dataset and a prospect evaluation using multiple volumes of 3D seismic data represent distinctly different examples of Big Data challenges. The reduction of time spent on the analysis of 2D data set comes from computer aided techniques in prescreening of all 2D data available for identification of the “areas of interest”. This will focus the deployment of the experienced interpreters. In prospect evaluation, the reduction of Big data without information loss will come from the techniques of co-rendering and correlating multiple seismic attributes in real-time.
And lastly, we can save time during decision making process itself when all individual threads of analysis are combined into a multi-threaded assessment of dependencies, risks and uncertainties we have to face.
The key is to remain focused on the value. To do that, we need to understand and let go the differences between the disciplines,help the team of geoscientists and engineers find a common language and agree on what is important to the economic success and let management make a decision based on a collaborative evaluation.
