Patacchini, Leonardo

The classic method for initializing reservoir simulation models in the presence of a transition zone, based on primary drainage capillary-gravity equilibrium, is extended to account for partial reimbibition post oil migration. This tackles situations where structural events, such as trap tilting or caprock leakage, caused the current free-water level (FWL) to rise above deeper paleo-contacts. A preliminary primary drainage initialization is performed with zero capillary pressure at the paleo (or deepest historical) FWL, to obtain a minimum historical water saturation distribution. From a capillary pressure hysteresis model, it is then possible to determine the appropriate imbibition scanning curve for each gridblock, which are used to perform a second initialization with zero capillary pressure at the current FWL. With the proposed method, log-derived saturation profiles can be honored using a physically meaningful capillary pressure model. Furthermore, when relative permeability hysteresis is active, it is possible as a byproduct of the initialization to assign the correct scanning curves at time zero to each gridblock, which ensures that initial phase mobilities (hence reservoir productivity) and residual oil saturation (hence recoverable oil to waterflood) are modeled correctly. This is demonstrated with a synthetic vertical 1D model. The method was implemented in a commercial reservoir simulator to support modeling work for a giant undeveloped carbonate reservoir, where available data suggest that more than 3/4 of the initial oil in place could be located between the current FWL and a dome-shaped paleo-FWL. This work is used as a case study to illustrate the elegance of the proposed method in the presence of multiple (or tilted) paleo-FWLs, as only one set of capillary pressure curves per dynamic rock-type is required to honor the complex logderived saturation distribution.

Summary

Short-term production and injection optimization are best approached from an integrated surface/subsurface perspective, recognizing that well performance is driven by competition for an existing network hydraulic capacity.

This paper presents a tool for real-time optimization (RTO) of water-injection systems at the scheduling time scale (i.e., days to months). Its development stemmed from the observation that operations such as pigging or shutting manifolds for rig activity might disrupt the injection network balance; hence, injectors would benefit from quick control readjustments. Furthermore, an existing network is not necessarily able to distribute available water where desired, and control compromises best found by an optimizer should be sought.

It is assumed that reservoir conditions are stationary, and injection targets at any level of granularity (well, reservoir segment, or field level) have been established based on subsurface requirements. By use of performance curves for each injector and either a simplified or a full-fledged network model, the algorithm finds a set of optimal well controls with a steepest-descent method implemented in Microsoft (2016) Visual Basic for Applications (VBA). The interface is spreadsheet-based, facilitating updates in well-performance data or changes in reservoir requirements. When needed by the algorithm, a third-party hydraulic-flow simulator able to balance the system from the injection modules down to the manifolds is called through an application programming interface.

A case study is presented, illustrating how the tool has been used to estimate the benefits of installing wellhead chokes on the currently more than 200 active injection strings of a giant oil field offshore Abu Dhabi.

While history matching of a reservoir simulation model principally aims at reproducing past shut-in pressure and fractional flow observations, transition from history to forecast further requires honoring the productivities (or injectivities) of wells, and implementing the current field operational strategy to avoid unwanted reallocation of rates. Well-level matching is particularly lengthy for giant oilfields with several hundreds to thousands of completions, hence sometimes not refined enough to warrant using observed flowing pressures as forecast constraints. Furthermore, breakthrough times are not necessarily replicated in all wells; this strongly increases discrepancies between measured and simulated flowing pressures, and can cause spurious shutting of wells infringing on reservoir management guidelines. Short-term forecast confidence can be improved by compensating well-level mismatches observed at the end of history for production and injection control purposes only, without solving the inherent model problems. The purpose of this paper is to describe a workflow followable with most commercial reservoir simulators to achieve such objective, forcing production and injection continuity at the onset of forecast while keeping the choked margin of wells producing below capacity. A successful application of the workflow is illustrated using the model of a giant Middle Eastern carbonate oilfield with more than 150 active production strings, using an eight-year (2015-2023) drilling schedule followed by no further activity. In this example, confidence in long-term field-level forecasts does not deteriorate after applying the proposed well-level compensations.

The building, calibration and validation of a coupled simulation model treating two giant subfields simultaneously with their common water injection facilities are presented. The primary objectives of the project were to develop a tool providing more accurate forecasts by consistently allocating injected volumes to both subfields, and to help identify network upgrades necessary to accommodate the long-term development plan. Simulation results presented in this paper are nevertheless based on an eight-years (2015-2023) drilling schedule, followed by no further activity.

Building of the model involved migration of separate reservoir and network models to a next generation simulator capable of treating both surface and subsurface flows fully implicitly. Coupled model operation without network (i.e., with wells constrained by guide-rates considering a common field injection target) and with network (i.e., with wells constrained by self-consistently calculated backpressures) are discussed and compared in detail. For the latter case, particular care is necessary in terms of modeling injection modules.

Calibration consisted of shifting the lift performance curves of more than 200 active injection strings, in order to make up for any mismatch in the flowing wellhead pressure and help ensure rate continuity at the transition from history to forecast. Tuning of the surface network, modeled "as is" according to its physical layout, was not necessary based on the good backpressure match obtained for the more than 40 active injection towers.

Validation was performed through a one-year blind test, from April 2015 (start of forecasts) to March 2016. The procedure first involved well-by-well comparison of injection rates, for which, because of allocation uncertainties, a qualitative match was accepted; it second involved a comparison of the injection split between the two subfields, for which less than a 1% mismatch was achieved.

Coupling is performed periodically at the wellhead, using a reservoir simulator in which the field manager controls the reservoir models by supplying well constraints and controls the network models by supplying well performance curves. Allocation strategies and pressure and flow constraints are imposed by the field manager, for which the different sub-models are black boxes; the models themselves are controlled hydraulically without embedded production or injection constraints. This explicit approach has been selected for its flexibility. In particular, by expressing rates at the surfacesubsurface interface at standard conditions, it is possible for the two reservoir models to have different equations of state and different treatments of injected water salinity, while the surface models use a blackoil fluid description. This project required ensuring rate continuity at the transition from history to forecast for over 600 active production and injection strings, even when the reservoir and network models are not perfectly historymatched. This was achieved by introducing pressure shifts in each vertical flow performance curve to ensure continuity of the choking margins (i.e., differences between wellhead pressures and backpressures) and by overriding the default guide rate flow allocation method of the field manager to prevent abrupt changes in the production split of wells currently producing below potential. The use cases described here are based on an eight-year (2015-2023) drilling schedule followed by no further activity. We focus on assessing the impact on production and injection arising from: replacing pipelines or changing network topologies; relaxing the constraint of producing at initial solution gas-oil ratio with and without reduction of separator pressures; and redistributing or increasing the water injection capacity. 2 SPE-183153-MS

The giant field considered in this paper, located offshore Abu Dhabi, has so far been developed with solely a pressure maintenance-oriented strategy despite having produced for nearly fifty years. Such achievement has been possible because the field, divided into three stacked isolated reservoirs, is relatively homogeneous with little-to-no flow barriers.

The purpose of this paper is two-fold. First, propose a review and critical discussion of the different pressure maintenance methods employed so far, which chronologically include gravity-driven dumpflood water injection, peripheral powered water injection, and down-flank as well as crestal gas injection.

Second, present a segmentation mainly based on pressure trends on which injection-production metrics can be computed to help reservoir management pinpoint areas lacking support. A key to such analysis in the presence of peripheral water injection is to account for efficient water only, i.e., discard losses to the aquifer. For this purpose, a workflow involving streamline based post-processing of reservoir simulations corroborated by material balance analysis is used.

Recent increasing trends in WCT and GOR indicate that a development solely based on pressure maintenance and reservoir energy (i.e., without artificial lift) is not sustainable anymore, and must transition to sweep-oriented. Plans for such migration are discussed in the companion paper of Nakashima et al. (SPE-177801).

Summary

Conventional miscible or near-miscible gasflooding simulation often overestimates oil recovery, mostly because it does not capture a series of physical effects tending to limit interphase compositional exchanges. Those can include microscopic bypassing of oil situated in dead-end pores or blocked by water films, as well as macroscopic bypassing caused by subgrid-size heterogeneities or fingering. We here present a new engineering solution to this problem in the near-miscible case, relying on our in-house research reservoir simulator. The principle is, while using a black-oil or an equation-of-state description, to dynamically decrease the K-value of heavy components and possibly increase the K-value of light components as the oil saturation reaches the desired residual limit; this enables changing the phase boundaries when needed while preserving the original fluid behavior during the initial production stages. The benefits of the proposed solution are demonstrated on a reservoir-conditions tertiary-gas-injection experiment, performed in our laboratories, for which residual saturations as well as oil phase and individual-component production rates have easily and successfully been history matched. Results are then compared with matches obtained by use of saturation exclusion and x-factors methods. As a proof of concept, the suitability of the new method to simulate incomplete revaporization of condensate during gas cycling is also illustrated, on the third SPE comparative solution-project case.

Abstract

Conventional miscible or near-miscible gas flooding simulation often overestimates oil recovery, mostly because it does not capture a series of physical effects tending to limit interphase compositional exchanges. Those can be for instance microscopic bypassing of oil situated in dead-end pores or blocked by water films, as well as macroscopic bypassing due to sub-grid size heterogeneities or fingering.

We here present a new engineering solution to this problem in the near-miscible case, relying on our in-house research reservoir simulator (IHRRS). The principle is, while using a black-oil or an equation of state description, to dynamically decrease the K-value of heavy components and possibly increase the K-value of light components as the oil saturation reaches the desired residual limit; this enables changing the phase boundaries when needed while preserving the original fluid behavior during the initial production stages.

The benefits of the proposed method are demonstrated on a reservoir conditions tertiary gas injection experiment, performed in our laboratories, for which residual saturations as well as oil phase and individual components production rate have easily and successfully been history matched. Results are then compared to matches obtained using saturation exclusion and α-factors methods. As a proof of concept, suitability of the method to simulate incomplete revaporization of condensate during gas cycling is also illustrated, on the third SPE comparative solution project case.

Introduction

Conventional reservoir simulation, where complete phase equilibrium is enforced in each cell at each time-step, often overestimates oil production when applied to gas flooding. In miscible conditions, a simulator will predict the presence of a single hydrocarbon phase, either in the entire computational domain or at least at miscibility front, depending on whether the miscibility is first-contact or multi-contact; after a limited amount of pore-volumes injected, complete recovery of the original oil is therefore predicted. In near-miscible conditions, although the oil phase becomes non-mobile at low saturation, its components including heavy ends will eventually vaporize; complete recovery is therefore predicted as well, albeit after a longer injection period. Full recovery is however never observed, be it at the field scale or in laboratory corefloods, mainly because of microscopic (sub-Darcean) as well as macroscopic (Darcean) phenomena tending to limit interphase mass exchange. This comes in addition to the fact that lumping heavy ends leads by itself to an anticipation of ultimate recovery, since the tail of the hydrocarbon distribution is less vaporizable than the heavier pseudo-component it belongs to.

Summary

The Microemulsion phase behavior model based on oleic/aqueous/surfactant pseudophase equilibrium, commonly used in chemical flooding simulators, is coupled to Gas/Oil/Water phase equilibrium in our new four-fluid-phase, fully implicit in-house research reservoir simulator (IHRRS) (Moncorge et al. 2012). The method consistsof splitting the equilibrium into two stages, in which all the components other than surfactant are equilibrated first - by use of a black-oil, K-value, or equation of state (EOS) model - and the resulting Gas, Oil, and Water phases are then lumped into pseudophases to be equilibrated by use of the Microemulsion model. This subdivision in stages is conceptual, and at each converged timestep the four phases (Gas, Oil, Water, and Microemulsion, when simultaneously present) will be in equilibrium with each other. The fluid properties (such as densities, viscosities, and interfacial tensions) and rock/fluid properties (such as relative permeabilities) required in the transport equations are evaluated with models from well-known industrial or academic simulators. Surfactant flooding being usually implemented as a tertiary recovery mechanism, on fields for which complete models that we do not wish to modify already exist, particular care is devoted to ensuring continuity of the physics at the onset of surfactant injection. Our code is first validated against a reference academic chemical- flooding simulator, on a 1D, three-fluid-phase (Oil/Water/ Microemulsion) coreflood. Second, as application examples where it is necessary to account for four phases in equilibrium, we consider a scenario where the chemical flood is preceded by a vaporizing Gas drive, as well as a scenario where dissolved gas is released by the Oil during the flooding process. Some aspects of our implementation, such as numerical dispersion vs. timestep length and nonlinear convergence, are also discussed; in particular, we show that numerical performance is not degraded by the four-phase equilibrium.

Abstract

The dynamic effect of pressure and Oil composition on Microemulsion phase behavior, complementing the key effect of variable salinity, has been implemented in our four-fluid-phase, fully implicit in-house research reservoir simulator. This has been achieved through self-consistent coupling of a traditional Gas/Oil/Water phase equilibrium model, either compositional or generalized black-oil,—providing phase fractions, oleic composition, and aqueous salinity—with a Microemulsion model based on oleic/aqueous/chemical pseudophase equilibrium.

As an application example and validation test case, we consider a hypothetical surfactant/polymer (SP) coreflood of a saturated Oil, interrupted by a progressive depressurization, during which dissolved gas is released, which shifts the Microemulsion phase state from Winsor Type III to Type II-. This proves the good functioning of our new option, and shows, yet on a simple case, that it does not degrade numerical performance, despite the introduction of additional nonlinear dependencies.

Introduction

Surfactant/polymer (SP) flooding is an enhanced oil recovery process aimed at mobilizing residual Oil to Water1 in mature conventional reservoirs through interfacial tension (IFT) reduction, and sweeping it through mobility ratio improvement (Lake 1989). While most commercial reservoir simulators have long had the capacity of treating polymer, modeled as an additional aqueous component, comprehensive treatment of surfactant has until recently been limited to academic codes. Indeed, above a critical micelle concentration (CMC), Water and Oil become partially soluble, leading to the formation of an additional Microemulsion phase, whose implementation is a rather complicated task. Appropriate modeling of Microemulsion is yet key, as the IFT’s reduction with the excess Oil and/or Water phases is function of the solubilization ratios, defined as the amount of dissolved oil and/or water per amount of surfactant (Huh 1979). In general, those depend on the chemical formulation, Water and Oil phase compositions, pressure, and temperature.