Where is the Gap? Is it in More Reservoir Engineers or in Leveraging New Skills and Workflows that Enhances Individual Productivity?

摘要

The primary functions of a reservoir engineer include estimation of hydrocarbons in place, the evaluation of the recovery factor and the scheduling of the recovery. These roles are central in meeting the extremely complex challenges of the life- cycle development of hydrocarbon resources. These challenges have now assumed gargantuan proportions because of mature assets requiring more attention to squeeze the last drop of oil from them, complicated accumulation needing ingenious solutions to make them profitable, high development costs demanding greater attention to details to reduce costs and unprecedented prices that has made otherwise marginal fields more attractive to develop. Throw also in the mix the heightened sense of environmental issues, unconventional hydrocarbon resources, competing alternative sources of energy, strict compliance with regulatory bodies and uncertain political situations in most of the growth basins and you might have sympathy for those crying for more reservoir engineers in the industry. The fact is that our traditional deterministic approach of working is people intensive and it has started to fail the industry. Therefore, recruiting many more engineers to meet the present challenges can only fuel the vicious cycle of hire and fire approach that has made our industry less attractive in the past. We need to embrace new workflows based on established statistical concepts like neural networks and experimental designs that move the focus from people to computers. These new workflows also enable greater flexibility in data handling, ensure consistency in uncertainty quantification and give results in continuous distributions that can be sampled rapidly using statistical techniques like Monte Carlo. This paper describes how statistical techniques can help in many core reservoir engineering roles like surveillance, history match and reservoir management. In addition, it presents relevant examples that illustrate the successful application of these concepts in the industry including a possible automation of integrated reservoir studies.