Closed-Loop Field Development Under Uncertainty by Use of Optimization With Sample Validation

Abstract

In this work, we develop and apply a general methodology for optimal closed-loop field development (CLFD) under geological uncertainty. CLFD involves three major steps: optimizing the field-development plan on the basis of current geological knowledge; drilling new wells, and collecting hard data and production data; and updating multiple geological models on the basis of all the available data. In the optimization step, the number, type, locations, and controls for new wells (and future controls for existing wells) are optimized with a hybrid particle swarm optimization-mesh adaptive direct search algorithm. The objective here is to maximize expected (over multiple realizations) net present value (NPV) of the overall project. History matching is accomplished with an adjoint-gradient-based "randomized maximum likelihood" procedure. Because the CLFD history-matching component is fast relative to the optimization component, we generate a relatively large number of history-matched models. Optimization is then performed with a set of "representative" realizations selected from the full set of history-matched models. We introduce a systematic optimization with sample validation (OSV) procedure, in which the number of realizations used for optimization is increased if an appropriate validation criterion is not satisfied. The CLFD methodology is applied to 2D and 3D example cases. Results show that the use of CLFD increases the NPV for the "true" (synthetic) model by 10 to 70% relative to that achieved by optimizing over a large number of prior realizations. We also compare the results for CLFD with OSV to results that use a fixed number of geological realizations. These comparisons show that the use of too few realizations in the CLFD optimization step can result in lower true-model NPVs, whereas OSV provides a systematic approach for determining the proper number of realizations.

Closed-loop field development with multipoint geostatistics and statistical performance assessment

“…Optimizing the full development plan at each step is to avoid a greedy approach where one would only optimize the next n rig wells by either freezing or not considering later wells in the project horizon. A comparison between greedy (well-by-well) optimization and optimization of full development plan is discussed in Example 1 in Shirangi and Durlofsky (2015). CLFD workflow is shown in Fig.…”

“…Closed-loop field development (CLFD) optimization, introduced by Shirangi and Durlofsky (2015), is a comprehensive reservoir management framework. In the optimization step of CLFD, the number, type, location and controls of new wells (together with controls of the existing wells) are optimized.…”

“…Morosov and Schiozer (2016) applied the CLFD to a realistic example, while Hanea et al (2017) applied the CLFD framework in simpler settings where only drilling sequence is optimized. Compared with the original work (Shirangi and Durlofsky, 2015;Shirangi, 2013), they used different reservoir modeling and different model calibration and optimization approaches in their CLFD application. Both papers, reported that CLFD significantly improved truemodel NPV in most cases; however, in at least one case in each work, the true-model NPV decreased slightly after the application of CLFD.…”

Recent Developments in Closed-Loop Approaches for Real-Time Mining and Petroleum Extraction

“…Various publications describe theoretical studies involving CLRM (recently also referred to as closed-loop field development) using a variety of optimization and data assimilation techniques; see, e.g. , Naevdal et al (2006), Sarma et al (2006Sarma et al ( , 2008a, Chen et al (2009Chen et al ( , 2012, Chen and Oliver (2010), Wang et al (2009), Peters et al (2010, Foss and Jensen (2010), Capolei et al (2013), Shirangi and Durlofsky (2015) and Bukshtynov et al (2015). For further references, see Jansen et al (2005Jansen et al ( , 2008Jansen et al ( , 2009) and Hou et al (2015).…”

Response surface method for assessing energy production from geopressured geothermal reservoirs

“…To overcome this limitation, there are three alternatives as follows: (1) procedures that efficiently create the history matched models; (2) surrogate reduced order models; and (3) statistical proxy models. The first approach uses efficient gradient-based or gradient-free algorithms for history matching production data and efficiently makes field development plans (Shirangi and Durlofsky 2015;Shirangi 2014). The second approach is to build surrogate reduced order models using piecewise linearization algorithms.…”