The technique of geosteering has been widely adopted in the petroleum industry for proper wellbore positioning. The benefits of geosteering have become more pronounced as drilling environments have become more complex. Geosteering-related technologies, such as downhole information-gathering tools and real-time data-transmission and data-analysis applications, have been continuously and significantly improved to support geosteering decisions. However, the full value of these advancements has yet to be realized.Because of uncertainties in predrill geological models, information gathered while drilling is applied in making real-time reservoirnavigation decisions. To achieve an optimal result requires a series of high-quality decisions regarding well-trajectory adjustment.Interestingly, geosteering-related literature does not demonstrate that the industry has applied a logically consistent approach for making geosteering decisions. Common practice, as described in the literature, does not clearly and quantitatively state measurable objectives, key underlying uncertainties, or relevance between underlying uncertainties and real-time information. Furthermore, it is unclear how a specific geosteering decision is being reached. Lacking a systematic and transparent framework for supporting geosteering decisions, the current approach is unlikely to result in optimal decisions for placing the well in the best possible location. 1 In this paper, we introduce and discuss a decision-driven approach to the geosteering process. The main contributions of the paper are threefold: (1) a review of 46 SPE papers on geosteering, including a discussion of the main features of current geosteering methods; (2) the development and discussion of a decision analytic framework to support high-quality geosteering decisions; and (3) implementation of Bayesian inference techniques to consistently update ahead-of-the-bit reservoir uncertainties while behind-the-bit data are gathered in real time. The new framework relates real-time information to the key reservoir uncertainties and provides an unbiased and consistent approach for making high-quality geosteering decisions.
Summary Geosteering techniques have been widely implemented in the oil and gas (O&G) industry for well-placement operations. These techniques allow the operators to apply real-time information in precisely controlling the wellbore direction to stay within desired reservoir zones. The industry has mainly focused on technological improvements of real-time technologies. Nonetheless, previous work has indicated that the conventional approach for making geosteering decisions leaves much to be desired. Normal geosteering operations involve drawing inferences from behind-the-bit information to ahead-of-the-bit reservoir uncertainties and making decisions under unresolved uncertainties to optimize a single objective or multiple objectives. On the basis of a large body of research, the conventional approach—which is heavily driven by intuitions, educated guesses, and approximate methods (rules of thumb)—is unlikely to identify the optimal courses of action. Geosteering decisions are sequential decisions made under dynamic uncertainties. A sequence of well-trajectory decisions arises as the well penetrates the formation and real-time data are gathered. To optimize decision making in such an environment requires considering future decisions and uncertainties, along with the flexibility to take action as new information is learned. In this work, we demonstrate how sequential geosteering decisions can be optimized by use of the discretized-stochastic-dynamic-programming approach (DSDP). DSDP exploits the benefits of stochastic dynamic programming to optimize multistage, interrelated decisions under uncertainties. At the same time, the computational time is minimized so it can be applied to geosteering decisions. Through case studies, we illustrate the application of DSDP in various reservoir structures. The results suggest that the technique could significantly improve the final results of the wells, especially if the reservoir boundaries change rapidly or in a faulted reservoir. The results are expressed as substantial increases in resulting reservoir contacts as well as reductions in well-construction costs. Finally, we illustrate and discuss the use of DSDP to assess the value of look-ahead information. We demonstrate that merely increasing the look-ahead capability (ahead-of-the-bit distance that the tool can measure) is not sufficient. The values created from look-ahead are also strongly affected by the measurement accuracy and the flexibility to respond rapidly by making large directional changes.
Due to escalated drilling costs, the petroleum industry has been attempting to access the largest possible hydrocarbon resources with the lowest achievable costs. Multiple well objectives are set prior to the start of drilling. Then, a geosteering approach is implemented to help operators achieve these objectives.A comprehensive literature survey has been performed on geosteering case histories, including many cases with multiple objectives. We found that the listed objectives are often conflicting and expressed in different measures. Furthermore, none of the cases from the reviewed literature has discussed a systematic approach for dealing with multiple objectives in geosteering contexts. Without implementing a well-structured approach, decision makers are likely to make judgments about the relative importance of each objective based on previous experiences or on approximate methods. Research shows that such decision-making approaches are unlikely to identify optimal courses of action.In this paper, we propose a systematic method for making multi-criteria decisions in geosteering context. The method is constructed such that it is applicable for real-time operations. Results show that different decision criteria can have significant impact on well success as measured by its trajectory, future production, cost, and operational efficiency.
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