A data-driven automatic well planner procedure is implemented to develop complex well trajectories by efficiently adapting to near-well reservoir properties and geometry. The procedure draws inspiration from geosteering drilling operations, where modern logging-while-drilling tools enable the adjustment of well trajectories during drilling. Analogously, the proposed procedure develops well trajectories based on a selected geology-based fitness measure using an artificial neural network as the decision maker in a virtual sequential drilling process within a reservoir model. While neural networks have seen extensive use in other areas of reservoir management, to the best of our knowledge, this work is the first to apply neural networks on well trajectory design within reservoir models. Importantly, both the input data generation used to train the network and the actual trajectory design operations conducted by the trained network are efficient calculations, since these rely solely on geometric and initial properties of the reservoir, and thus do not require additional simulations. Therefore, the main advantage over traditional methods is the highly articulated well trajectories adapted to reservoir properties using a low-order well representation. Well trajectories generated in a realistic reservoir by the automatic well planner are qualitatively and quantitatively compared to trajectories generated by a differential evolution algorithm. Results show that the resulting trajectories improve productivity compared to straight line well trajectories, both for channelized and geometrically complex reservoirs. Moreover, the overall productivity with the resulting trajectories is comparable to well solutions obtained using differential evolution, but at a much lower computational cost.

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Tackling emissions from hydrocarbon production is a necessity because hydrocarbon production will last for a prolonged time. As a popular hydrocarbon production method, waterflooding operation is energy-intensive and accounts for significant CO2 emissions. This article investigates the effect of CO2 tax level on recovery process of already producing fields with waterflooding. Our methodology is waterflooding optimization in reservoir simulation models, specifically optimizing well-controls. Unlike traditional studies, our optimization objective comprises two components: the profitability of hydrocarbon production and an additional tax proportional to CO2 emissions. The associated CO2 emissions is estimated using a scheme developed upon an integrated model of reservoir, surface network, and topside facility. We examine our methodology on two cases with heterogeneous reservoir models. In each case, we optimize multi-scenarios enforcing different CO2 tax rates. The solutions indicate that imposing a higher CO2 tax rate reduces both emissions and hydrocarbon production. The fractional reduction of oil produced is however smaller than the emission reduction. While an increased tax rate drives the topside equipment to operate at higher efficiencies, the main effects of a higher tax rate are reduced water injection and more efficient subsurface drainage. There is a non-linear relationship between the reduced production and emissions. For increasing tax levels there are diminishing returns on lower emissions, reflecting reduced opportunities for emission reduction by changes in the drainage strategy. Some increments on the tax rate will therefore have negligible impacts on the optimal drainage strategy, and hence an adverse effect on the profitability with negligible emission reduction.

An Automatic Well Planner (AWP) is used to efficiently adjust pre-determined well paths to honor near-well properties and increase overall production. AWP replicates modern geosteering decision-making where adjustments to pre-programmed well paths are driven by continuous integration of data obtained from logging-while-drilling and look-ahead technology. In this work, AWP is combined into a robust optimization scheme to develop trajectories that follow reservoir properties in a more realistic manner compared to common well representations for optimization purposes. Core AWP operation relies on an artificial neural network coupled with a geology-based feedback mechanism. Specifically, for each well path candidate obtained from an outer-loop optimization procedure, AWP customizes trajectories according to the particular geological near-well properties of each realization in an ensemble of models. While well placement searches typically rely on linear well path representations, AWP develops customized trajectories by moving sequentially from heel to the toe. Analog to realistic drilling operations, AWP determines subsequent trajectory points by efficiently processing neighboring geological information. Studies are performed using the Olympus ensemble. AWP and the two derivative-free algorithms used in this work, Asynchronous Parallel Pattern Search (APPS) and Particle Swarm Optimization (PSO), are implemented using NTNU’s open-source optimization framework FieldOpt. Results show that, with both APPS and PSO, the AWP solutions outperform the solutions obtained with a straight-line parameterization in all the three tested well placement optimization scenarios, which varied from the simplest scenario with a sole producer in a single-realization environment to a scenario with the full ensemble and multiple producers.