An integrated-asset-model (IAM) approach has been implemented for the Alpine field and eight associated satellite fields on the western Alaskan North Slope (WNS) to maximize asset value and recovery. The IAM approach enables the investigation of reservoir- and facilities-management options under existing and future operating constraints. The technology used for managing the fields consists of full-field compositional reservoir-simulation models for each reservoir integrated with a pipeline-surface-network model and a process facility model.
Developing an IAM
Reservoir-Management Needs. As with the construction of any reservoir or surface model, the first step in developing an IAM is to define with as much clarity as possible the objectives in undertaking such an endeavor.
A detailed list of all possible objectives for creating an IAM covering all possible development and operational situations that might occur would be quite extensive. As is often the case, however, simply identifying the “big rocks” will allow the finer details of those leveraging aspects to be identified and planned for in the development of the IAM.
The Alpine field anchors the westernmost oil-production and processing facility on Alaska’s North Slope (Fig. 1). Discovered in 1994, the Alpine field is in the Colville River delta, 6 miles south of the Arctic Ocean and approximately 70 miles west of the Trans-Alaska Pipeline. The Alpine field began production in November 2000 and continues development today. Subsequently, satellite fields, including the Fiord-Nechelik, Fiord-Kuparuk, Nanuq-Kuparuk, Nanuq-Nanuq, Qannik, and Alpine-Kuparuk, have been brought on line and continue to be developed. Additionally, several fields in the National Petroleum Reserve have the potential to be developed. The common theme across all these developments is that they are or will be produced through the Alpine Central Facility (ACF).
The ACF is a single-train processing facility. The only significant fluid that leaves the ACF is the sales oil. All other gas not used for fuel or lift must either be blended for injection as miscible injectant (enriched lean-gas injectant) or be injected as lean gas into two black-start gas-injection wells. All produced water must be reinjected and, for pipeline-integrity reasons, must be segregated from imported makeup sea-water used for injection.
With the ever-growing mix of fluid types, gas/oil ratios, and water production, the first question an IAM must answer is how to optimize production given the constraints on the surface network and production facilities and the large disparity in the produced fluids. There are many details that come into play. First and foremost, daily optimization of oil production can typically be achieved by means of standard gas lift optimization procedures under a constrained volume of lift gas. However, this considers only the instantaneous optimization of production. Optimizing production fully, then, necessarily requires that the injection-fluid type and placement also be considered—the second major question to be answered through an IAM.
Current development in the WNS is entirely within the environmentally sensitive area of the Colville River delta. This has required the development to meet the strictest of environmental challenges. Therefore, the major needs for the WNS IAM include the capability to optimize both daily and long-term production, optimize gas and water injection, and include a planning capability for all future reservoir development and facility expansion.
Model Requirements and Tool Selection. While integration of the individual reservoir models with a pipeline-surface-network model and facility model was deemed critical, it was recognized that being able to run the individual reservoir models outside the IAM was still valuable. Therefore, the first criterion for selecting a tool was that it must have the ability to run the individual models outside the IAM.
A number of criteria arise within the IAM tool itself. First, global facility limits on both production phases and injection phases can be honored only with a truly integrated model. The IAM would need to understand and visualize model results easily. Ease of use or ease of construction was deemed critical if the IAM tool were to survive and become a perpetual tool within WNS. Having the capability to incorporate operating strategies unique to WNS was also considered critical to realize the true potential at WNS. Last, the IAM tool was viewed as being valuable only if run times were low enough to allow different scenarios to be run quickly to enable management decisions in a timely manner.
Because the composition of injection gas was to change continually either by design or simply because of the physical system, it was important that the IAM tool be capable of passing compositional information between the models.
The WNS team evaluated a number of options for an IAM. In late 2006, the decision was made to go with an IAM solution consisting of a commercial parallel reservoir simulator, surface-network simulator, facility simulator, and integration tool that satisfied nearly all of our criteria for assembling an IAM.
This integration tool is an end-to-end software solution that integrates reservoir, wells, surface infrastructure, and process facilities as well as the asset’s operating parameters, financial metrics, and economic conditions into a single production and reservoir management environment.
The WNS IAM
Reservoir-Simulation Models. All reservoir-simulation models for the WNS fields are fully compositional. To keep run times reasonable while capturing all important aspects of the recovery scheme, fluid descriptions within the models are captured with tuned eight-component equations of state (EOS). Without exception, the eight-component description for each field is represented using four pseudocomponents for the C7+ fractions. This commonality to the EOS description provides a significant benefit and simplification when it comes to integrated modeling.
Surface Pipeline Network. To account for the pressure drop through the surface pipeline network and its effect on the production at each drill pad, a surface-pipeline-network model has been integrated into the WNS IAM. Because the goal of the WNS IAM is the long-term optimization of rate and recovery, the creators were not as concerned with the detailed prediction of pressure losses down to very tight pressure tolerances as they were with capturing longer-term (monthly) pressure trends at each drillsite or each production manifold.
Process-Facility Model, ACF. The primary purpose of including a process-facility model in the WNS IAM is not to impose the fluid-processing constraints upon the total fluid and phase production; these are easily handled elsewhere within the WNS IAM. Rather, it is to capture the effects of extracting condensates from the gas stream for shipment down the sales line and the resulting effect of having less condensate to blend in the gas stream for reinjection into the reservoir for enhanced-oil-recovery benefits.
IAM Controller, Production and Injection Optimization, and Reservoir-Management Controls. The heart of the WNS IAM must be the IAM controller. It is this IAM controller that enables the seamless integration of so many models and allows the WNS group to design and build such a comprehensive and flexible reservoir-management tool. Fig. 2 displays the complete integrated model and some of the flow connections between the various pieces of the IAM. Other than the reservoir-simulation models, pipeline model, and facility model, a variety of Excel spreadsheets are incorporated within the model. These spreadsheets contain much of the control logic for optimizing production and injection as well as the operational and reservoir-management controls. In keeping with the philosophy of building a model that will be easily maintainable and not require a programming expert to enhance, the vast majority of allocation methods have been incorporated directly by use of Excel cell computations. Only two rather small Visual Basic routines have been incorporated into the model for the purpose of gas lift optimization and water-injection optimization. However, almost any reservoir engineer with a familiarity with Excel should be able to understand the computations and allocation methods being used and feel confident that he or she can easily modify the IAM as the need arises.
In developing the WNS IAM, a design philosophy of starting simple and building in complexity with time was used to minimize potential problems and allow for incremental testing and evaluation of the model. A second design philosophy incorporated into the WNS IAM was that more-involved allocation methods should be as close to identical as possible to those used within the reservoir-simulation models. If done properly, the optimal group-level or field-level production- and injection-rate allocations calculated at the IAM level (whether using field-level or rolled-up well-level allocations) will be the only information that needs to be passed back down to the reservoir-simulation models for the next timestep.
The field-level surface oil rates and oil compositions from each simulation model (or spreadsheet model) are passed through the IAM controller to the facility model. The facility model then passes the lean-gas composition and the stabilizer tops (blend-gas) composition and volume back to the IAM controller for computing the gas balance and the composition of the gas-injection streams for the next synchronization timestep.
Future Work
Future work planned for the WNS IAM centers primarily on incorporation of an additional gas-injection-optimization method. The main thrust of the methodology is that any particular production well would have two different curves on a plot of cumulative oil vs. hydrocarbon pore volumes injected, one for enriched gas and one for lean gas. Because all injection wells connected to any gas-injection manifold must be on the same gas service, there would be an optimal determination of gas service to each gas-injection manifold. This would be directly akin to a water-injection optimization for produced water vs. seawater. A simple combinatorial optimization should therefore suffice to optimize the gas injection at any point in time.
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 158497, “Integrated Asset Modeling for Reservoir Management of a Miscible WAG Development on Alaska’s Western North Slope,” by R.D. Roadifer, SPE, ConocoPhillips Alaska; R. Sauvé, Schlumberger; R. Torrens, SPE, Schlumberger Middle East; H.W. Mead, SPE, N.P. Pysz, SPE, and D.O. Uldrich, SPE, ConocoPhillips Alaska; and T. Eiben, ConocoPhillips Canada, prepared for the 2012 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 8–10 October. The paper has not been peer reviewed.