Closed-Loop Feedback Control in Intelligent Wells: Application to a Heterogeneous, Thin Oil-Rim Reservoir in the North Sea

Abstract

Summary Important challenges remain in the development of optimized control strategies for intelligent wells, particularly with respect to incorporating the impact of reservoir uncertainty. Most optimization methods are model-based and are effective only if the model or ensemble of models used in the optimization captures all possible reservoir behaviors at the individual-well and -completion level. This is rarely the case. Moreover, reservoir models are rarely predictive at the spatial and temporal scales required to identify control actions. We evaluate the benefit of the use of closed-loop control strategies, on the basis of direct feedback between reservoir monitoring and inflow-valve settings, within a geologically heterogeneous, thin oil-rim reservoir. This approach does not omit model predictions completely; rather, model predictions are used to optimize a number of adjustable parameters within a general direct feedback relationship between measured data and inflow-control settings. A high-resolution sector model is used to capture reservoir heterogeneity, which incorporates a locally refined horizontal grid in the oil zone, to accurately represent the horizontal-well geometry and fluid contacts, and capture water and gas flow. Two inflow-control strategies are tested. The first is an open-loop approach, using fixed inflow-control devices to balance the pressure drawdown along the well, sized before installation. The second is a closed-loop, feedback-control strategy, using variable inflow-control valves that can be controlled from the surface in response to multiphase-flow data obtained downhole. The closed-loop strategy is optimized with a base-case model, and then tested against unexpected reservoir behavior by adjusting a number of uncertain parameters in the model but not reoptimizing. We find that closed-loop feedback control yields positive gains in net-present value (NPV) for the majority of reservoir behaviors investigated, and higher gains than the open-loop strategy. Closed-loop control also can yield positive gains in NPV even when the reservoir does not behave as expected, and in tested scenarios returned a near optimal NPV. However, inflow control can be risky, because unpredicted reservoir behavior also leads to negative returns. Moreover, assessing the benefits of inflow control over an arbitrarily fixed well life can be misleading, because observed gains depend on when the calculation is made.

Three-dimensional modeling of clinoforms in shallow-marine reservoirs: Part 1. Concepts and application

“…However, to date, the heterogeneity associated with clinoforms has not been explicitly included in reservoir or flow-simulation models of the Sognefjord Formation in the Troll Field. Dilib et al (2015) created a sector model of the Sognefjord Formation (dimensions: 3200 × 750 × 150 m [10,499 × 2461 × 492 ft]) to investigate production optimization using intelligent wells for a range of uncertainty in geologic parameters and their model, extracted and refined from the existing full field geological model, was used here as the context in which to apply the clinoform-modeling algorithm.…”

“…The bounding surfaces are offset by two postdepositional faults that are oriented northwest-southeast across the model volume. The faulted parasequence-bounding flooding surfaces were extracted from the existing reservoir model (Dilib et al, 2015). The faulted parasequence boundaries were used to construct the final Troll West sector model but, as a quality control step for applying the clinoform-modeling algorithm, these boundaries were adjusted so that they were horizontal.…”

“…The faulted parasequence boundaries were used to construct the final Troll West sector model but, as a quality control step for applying the clinoform-modeling algorithm, these boundaries were adjusted so that they were horizontal. Each parasequence also contains a surface that represents the facies-association boundary between m sands below and c sands above; these surfaces were extracted from the model of Dilib et al (2015) and are laterally continuous across the clinoforms modeled here, because they were extracted from a model that omits clinoforms. Consequently, facies interfingering across clinoforms is not captured here, and this may further increase the impact of modeling clinoforms on flow ).…”

“…As a first step, the insertion point of the first clinoform (p o ) was arbitrarily selected in the center of the proximal model boundary, and consistent west-northwest progradation of clinoforms (Dreyer et al, 2005;Patruno et al, 2015) was used to define a θ of 320°. The faciesassociation boundary surfaces extracted from the model of Dilib et al (2015) were then used to create zones of m sands and c sands within each clinothem. The application of the clinoform-modeling algorithm yields a model containing 100 clinoforms.…”

“…In the gas cap and aquifer, the vertical layering is stratigraphic, conforming to the flooding surfaces that bound the parasequences and with a single grid layer representing each facies association zone. In an interval of the reservoir that contains the oil column, from 3 m (10 ft) above the gas-oil contact (GOC) to 3 m (10 ft) below the oilwater contact (OWC), the grid is horizontal and regular, with finer layering (0.25-2 m [0.82-7 ft]) parallel to the initial GOC and OWC (Dilib et al, 2015). Very fine grid resolution is required to capture the geometry of clinoforms in this regular, orthogonal part of the grid.…”