Assessing the waterflood, monitoring the fluids front, and enhancing sweep with the uncertainty of multiple geological realisations, data quality, and measurement presents an ongoing challenge. Defining sweet spots and optimal candidate well locations in a well-developed large field presents an additional challenge for reservoir management. A case study is presented that highlights the approach to this cycle of time-lapse monitoring, acquisition, analysis and planning in delivery of an optimal field development strategy using multi-constrained optimisation combined with fast semi-analytical and numerical simulators. The multi-constrained optimiser is used in conjunction with different semi-analytical and simulation tools (streamlines, traditional simulators, and new high-powered simulation tools able to manage huge, multi-million-cell-field models) and rapidly predicts optimal well placement locations with inclusion of anti-collision in the presence of the reservoir uncertainties. The case study evaluates proposed field development strategies using the automated multivariable optimisation of well locations, trajectories, completion locations, and flow rates in the presence of existing wells and production history, geological parameters and reservoir engineering constraints, subsurface uncertainty, capex and opex costs, risk tolerance, and drilling sequence. This optimisation is fast and allows for quick evaluation of multiple strategies to decipher an optimal development plan. Optimisers are a key technology facilitating simulation workflows, since there is no ‘one-approach-fits-all’ when optimising oilfield development. Driven by different objective functions (net present value (NPV), return on investment (ROI), or production totals) the case study highlights the challenges, the best practices, and the advantages of an integrated approach in developing an optimal development plan for a brownfield.

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Adequate estimation of resources and reserves is critical for any prospect or field in the oil and gas industry. We propose improving the reliability of this estimation, first, by exploring the range of uncertainties through the use of scenario-based geological models; second, by ranking all generated models based on both static and dynamic responses; and, finally, by selecting few models representative of the uncertainty affecting the reservoir. In any field, the unknowns largely outnumber the known data, and, typically, some of these known data are biased due to preferential sampling. The classical approach controls the range of uncertainties based on multiple equiprobable realizations generated by using different random paths and by randomly sampling the data range. Our method improves that approach by considering additional realistic geological scenarios, which allows expanding the uncertainty space to sample from. A large number of models is generated as a result. Performing flow simulation on a large range of models would be computationally expensive. The common industry practice is to select models based on static volumetric measurements only. Our approach not only accounts for static volumes, but also adds computation of the flow-based connectivity using streamline simulation to account for the dynamic behavior of the models. A sensitivity analysis allows preserving only the most influential variables and discarding the negligible ones. Finally, static and dynamic responses from the remaining models are analyzed to identify scenarios with consistent low, mid, and high values for both responses. This results in the selection of few representative models that provide a greater assurance of recovering the probability distribution than would have been achieved by following common industry practices. Our methodology involves first enriching the uncertainty space by adding a dimension of geological scenarios. Then, it uses both static and dynamic responses to select a handful of representative models while still allowing adequate estimation of resources and reserves.

We present a framework that automatically generates an optimal well placement plan (WPP) based on a reservoir model. The proposed WPP comprises wells, their completions, and the drilling schedule. A suite of high-speed computational components allows this WPP to be generated in minutes. Greenfields and brownfields are supported. Brownfields require consideration of historical and ongoing production by existing wells along with collision avoidance when proposing new wells. The proposed wells can be producers or water injectors with vertical, deviated, or horizontal geometries. Different development strategies can be investigated that allow targets to be driven by geology or standard pattern such as an inverted five-spot. In addition to proposing new wells, existing wells may be sidetracked or recompleted. Optimization of the WPP uses a constrained downhill simplex approach. During a trial, WPPs proposed by the optimizer in earlier trials are extrapolated to propose a new WPP. The proposed WPP must satisfy a wide range of geometric, operational, contractual, and legal constraints on the surface and in the overburden and reservoir. Collision and hazard avoidance computation uses a geocomputation topology approach. When a feasible WPP is discovered, the production forecast is computed using a high-speed semi-analytical reservoir simulator, which renders a result within a few seconds, using an analytically computed pressure and explicitly computed saturation. This reservoir simulator is fully three-dimensional and discretizes the reservoir to represent the underlying heterogeneity. In addition to recovery, the framework allows a variety of objective functions including net present value, return on investment, and profitability index. Optimization in the presence of subsurface uncertainty is considered by using an ensemble of reservoir models. A proposed WPP will then have an uncertainty in the forecast value. For a specified aversion to risk, a conservative or aggressive WPP can then be optimized. The framework has been applied to a variety of workflows. These include rapid evaluation of the potential of different waterflooding strategies, drilling multiple infill wells from existing platforms, and identification of sidetrack candidates in mature fields. This new framework has many applications in the field development planning workflow, including rapid screening of multiple fields and development scenarios. The most promising scenarios can be used with detailed numerical simulation for further validation.

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