Here we report on in-depth water diversion using sodium silicate to increase oil recovery at the Snorre field, offshore Norway. A comprehensive qualification program revealed that the onset of gelation can be controlled; this was demonstrated in realistic core flood experiments as well as in a single well injection pilot. This paper highlights key design, response measurement plan and operational experiences from a large scale interwell field pilot of sodium silicate injection in a reservoir segment at the Snorre field on the Norwegian Continental Shelf. The operation of injecting 113 000 m 3 preflush, 240 000 m 3 sodium silicate and 49 000 m 3 postflush was performed from June to October 2013. The goal is to create an in-depth restriction between a subsea water injection well and a platform oil producer with approximately 2 000 m well spacing, and thereby improve the reservoir sweep by water injection.To perform the field pilot a 35 000 ton shuttle tanker was converted to a well stimulation vessel with the necessary equipment to accommodate a higher number of people, a desalination plant, storage and mixing equipment and high pressure pumps. The vessel was connected directly to a subsea water injection well and injected during a period of 5 months. The chosen design was to (a) soften the formation water by a KCl preflush, (b) control the gelation kinetics using HCl acid as activator, mixed into the diluted silicate solution and (c) displace the silicate solution by a KCl postflush followed by seawater injection.The pilot injection operation is completed, and the displacement of the sodium silicate with following seawater injection to the planned position for in-depth plugging is still on-going. The operational success criteria of proving ability to perform large scale transport, mixing and injection of sodium silicate from a shuttle tanker directly into a subsea well with no near wellbore plugging is met. Future production response will reveal if the success criteria of in-depth plugging, improved reservoir sweep and decrease in water cut will be met.
A particularly efficient flow solver can be obtained by combining a recent mixed multiscale finite-element method for computing pressure and velocity fields with a streamline method for computing fluid transport. This multiscale-streamline method has shown to be a promising approach for fast flow simulations on high-resolution geologic models with multimillion grid cells. The multiscale method solves the pressure equation on a coarse grid while preserving important fine-scale details. Fine-scale heterogeneity is accounted for through a set of generalized, heterogeneous basis functions that are computed numerically by solving local flow problems. When included in the coarse-grid equations, the basis functions ensure that the global equations are consistent with the local properties of the underlying differential operators. The multiscale method offers a substantial gain in computation speed, without significant loss of accuracy, when the multiscale basis functions are updated infrequently throughout a dynamics simulation. In this paper we propose to combine the multiscale-streamline method with a recent ‘generalized travel-time inversion’ method to derive a fast and robust method for history matching high-resolution geologic models. A key point in the new method is the use of sensitivities that are calculated analytically along streamlines with little computational overhead. The sensitivities are used in the travel-time inversion formulation to give a robust quasilinear method that typically converges in a few iterations and generally avoids much of the subjective judgments and time-consuming trial-and-errors in manual history matching. Moreover, the sensitivities are used to control a procedure for adaptive updating of the basis functions only in areas with relatively large sensitivity to the production response. The sensitivity-based adaptive approach allows us to selectively update only a fraction of the total number of basis functions, which gives a substantial savings in computation time for the forward flow simulations. We demonstrate the power and utility of our approach using a simple 2D model and a highly detailed 3D geomodel. The 3D simulation model consists of more than one million cells with 69 producing wells. Using our proposed approach, history matching over a period of 7 years is accomplished in less than forty minutes on an ordinary workstation PC. Introduction It is well known that geomodels derived from static data only - such as geological, seismic, well-log and core data - often fail to reproduce the production history. Reconciling geomodels to the dynamic response of the reservoir is critical for building reliable reservoir models. In the past few years, there have been significant developments in the area of dynamic data integration through the use of inverse modeling.1–12 Streamline methods have shown great promise in this regard.7–12 Streamline-based methods have the advantages that they are highly efficient "forward" simulators and allow sensitivities of production responses with respect to reservoir parameters to be computed analytically using a single flow simulation.7–10 Sensitivities describe the change in production responses due to small perturbations in reservoir properties such as porosity and permeability and are a vital part of many dynamic data-integration processes. Our recent works on streamline-based integration of production data were based on so-called ‘generalized travel time inversion’.7,8 There are several advantages associated with travel-time inversion of production data. First, it is robust and computationally efficient. Unlike conventional ‘amplitude’ matching, which can be highly non-linear, it has been shown that the travel-time inversion has quasilinear properties.7,13 As a result; the minimization proceeds rapidly even if the initial model is not close to the solution. Second, the travel time sensitivities are typically more uniform between wells compared to ‘amplitude’ sensitivities that tend to be localized near the wells. This prevents over-correction in the near-well regions.13 Finally, in practical field applications, the production data are often characterized by multiple peaks (for example, tracer responses). Under such conditions, the travel-time inversion can prevent the solution from converging to secondary peaks in the production response.7
Summary A particularly efficient reservoir simulator can be obtained by combining a recent multiscale mixed finite-element flow solver with a streamline method for computing fluid transport. This multiscale-streamline method has shown to be a promising approach for fast flow simulations on high-resolution geologic models with multimillion grid cells. The multiscale method solves the pressure equation on a coarse grid while preserving important fine-scale details in the velocity field. Fine-scale heterogeneity is accounted for through a set of generalized, heterogeneous basis functions that are computed numerically by solving local flow problems. When included in the coarse-grid equations, the basis functions ensure that the global equations are consistent with the local properties of the underlying differential operators. The multiscale method offers a substantial gain in computation speed, without significant loss of accuracy, when basis functions are updated infrequently throughout a dynamic simulation. In this paper, we propose to combine the multiscale-streamline method with a recent "generalized travel-time inversion" method to derive a fast and robust method for history matching high-resolution geocellular models. A key point in the new method is the use of sensitivities that are calculated analytically along streamlines with little computational overhead. The sensitivities are used in the travel-time inversion formulation to give a robust quasilinear method that typically converges in a few iterations and generally avoids much of the time-consuming trial-and-error seen in manual history matching. Moreover, the sensitivities are used to enforce basis functions to be adaptively updated only in areas with relatively large sensitivity to the production response. The sensitivity-based adaptive approach allows us to selectively update only a fraction of the total number of basis functions, which gives substantial savings in computation time for the forward flow simulations. We demonstrate the power and utility of our approach using a simple 2D model and a highly detailed 3D geomodel. The 3D simulation model consists of more than 1,000,000 cells with 69 producing wells. Using our proposed approach, history matching over a period of 7 years is accomplished in less than 20 minutes on an ordinary workstation PC. Introduction It is well known that geomodels derived from static data only—such as geological, seismic, well-log, and core data—often fail to reproduce the production history. Reconciling geomodels to the dynamic response of the reservoir is critical for building reliable reservoir models. In the past few years, there have been significant developments in the area of dynamic data integration through the use of inverse modeling. Streamline methods have shown great promise in this regard (Vasco et al. 1999; Wang and Kovscek 2000; Milliken et al. 2001; He et al. 2002; Al-Harbi et al. 2005; Cheng et al. 2006). Streamline-based methods have the advantages that they are highly efficient "forward" simulators and allow production-response sensitivities to be computed analytically using a single flow simulation (Vasco et al. 1999; He et al. 2002; Al-Harbi et al. 2005; Cheng et al. 2006). Sensitivities describe the change in production responses caused by small perturbations in reservoir properties such as porosity and permeability and are a vital part of many methods for integrating dynamic data. Even though streamline simulators provide fast forward simulation compared with a full finite-difference simulation in 3D, the forward simulation is still the most time-consuming part of the history-matching process. A streamline simulation consists of two steps that are repeated:solution of a 3D pressure equation to compute flow velocities; andsolution of 1D transport equations for evolving fluid compositions along representative sets of streamlines, followed by a mapping back to the underlying pressure grid. The first step is referred to as the "pressure step" and is often the most time-consuming. Consequently, history matching and flow simulation are usually performed on upscaled simulation models, which imposes the need for a subsequent downscaling if the dynamic data are to be integrated in the geomodel. Upscaling and downscaling may result in loss of important fine-scale information.
Structural updates for a complex reservoir model require time-consuming manual work, therefore, updates are rarely performed. This leads to an outdated model that gradually loses its predictability. Eventually, this results in model breakdown, and a new model must be built from scratch. Continuously updatable reservoir models avoid this and increase the value of models as a tool in decision making. In addition, easily updateable structural surfaces enable several structural realizations for spanning the uncertainty. We present the use of a method for fast and robust updates of structural surfaces in reservoir models. We will focus on updates using zone data from horizontal wells (zone-log conditioning), since this traditionally has been a bottleneck that needs tedious manual work prone to error. In zone-log conditioning, we try to generate horizon surfaces that honor the geological zonation along the well paths. This is important for property modeling, and is crucial for fluid-flow simulations. Our method is robust, fully automated, and is built on a consistent mathematical framework that includes specified input-data uncertainties. It has provided satisfactory results for large real-world reservoir models where standard methods and work processes have failed. The field example presented shows a reduction from 22.9 % to 0.9 % in incorrectly honoring of the zone logs by applying this method rather than the standard approach. The remaining 0.9 % is due to conflicting data, gridding errors, and is difficult to get rid of even with manual editing. We consider this a large step forward with respect to providing an up-to-date basis for decisions that also can account for structural uncertainties.
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