A controller providing a link between reservoir and network simulators has been developed to facilitate reservoir and production management. An optimizer included in the network simulator ensures that optimal management of the coupled system is achieved. The utility of the tool is first tested using a very simple reservoir model with one injector and a single smart producer, and subsequently demonstrated for water-alternating-gas (WAG) injection in two North Sea field cases. For the synthetic test case, various optimization scenarios are explored with the coupled system and compared to standalone flow simulation results. In the North Sea Field 1, optimum oil production is achieved by adjusting the settings of the surface valves as well as the downhole intelligent completion valves (ICVs); in Field 2, gas lift is optimized over time. The coupled system has given more accurate and realistic results in all cases. For the synthetic test case, the coupling gives a significantly higher production than the standalone reservoir simulator run whatever the optimization scenario. For the Field 1 smart well case, the coupled results match historic performance more closely because the pressure drop across the flow lines and surface facilities, the interaction among the wells in the production network, and the boundary conditions are all accounted for. For the gas lift optimization case, the coupled system gives more realistic results with respect to the potential for increased oil production and recovery than the standalone reservoir and network models. Introduction Achieve Reservoir and Production Management Goals Reservoir management is normally achieved using numerical simulation to model the performance of the reservoir under different scenarios of well placement, number of wells, and production and/or injection profiles. However, reservoir simulators do not generally model the production downstream of the wellhead, and so the production network effects on the behavior of the overall system are not fully acknowledged. Flow simulation of the reservoir system also does not account for all the boundary conditions set at the surface, such as the suction pressure of the separator. This may have a direct impact on the evaluation of the production targets that will actually be achieved. On the other hand, production management typically uses surface network nodal analysis tools that fully account for those effects but can only model the reservoir as a homogeneous ‘tank’ of uniform properties. Moreover, reservoir management aims at optimizing the reservoir performance over the field life by maximizing the recovery factor at the minimum cost, while production management is concerned with optimizing the production system capabilities on a day-to-day basis. Thus, reservoir and production management have complementary goals in field development, but on different time scales, and by using separate tools there is no guarantee that one will achieve a solution that satisfies both aims. Therefore, the integration of the capabilities of both reservoir and production system simulators appears to be a critical technology for field development and optimization.
We present results of modeling studies of injection schemes with wells using new completions technology, and address how these are applied or envisaged for the fulfillment of field development strategies in offshore environments. Introduction A common theme in offshore developments is the use of fewer wells to achieve the production targets of the field. In mature fields, the limited availability of well slots is a severe constraint. In new developments, the drive is towards fewer and smaller platform installations. In subsea developments, particularly in deepwater, the objective is minimization of wellheads. There are clear financial, environmental, and technical reasons for these trends. The use of advanced completions technology to reduce well requirements has been widely practiced in relation to production wells. Commingling of production in layered, compartmentalized, and dispersed geological settings has been achieved with multilateral wells, and conventional wells with flow control technology.1–4 The application of this technology to injection problems has received relatively little attention.5–6 The objective of this study is to analyze a number of injection problems that arise from actual field development scenarios. Four cases will be presented. The first two concern achieving proper partitioning of injected water into multi-zone structures. The third case concerns achieving the total target injection rate and partitioning. The fourth case concerns the alternating injection of water and gas. Two-Zone Injection This is a deepwater field, being developed by subsea wells. The structure consists of two partially overlapping formations of high permeability (3–5 Darcies), separated by non-reservoir rock. There is no external drive mechanism in the reservoir (e.g. an aquifer). Injection is to commence at initial production. Vertical injectors completed in both formations are to be used. The lower formation has about 1.5 times the injectivity of the upper formation because of its larger effective thickness. The objective is to inject into the upper and lower formations with a split of about 45% and 55% respectively, in line with the estimated distribution of reserves. The operator plans to install fixed and/or variable flow control devices in the injectors. The study analyzed injection options with and without valves. As Figure 1 shows, when no valves are used, the water will spontaneously partition between the two zones in the ratio of about 20% into the upper zone and 80% into the lower zone at relatively low injection rates. As the injection rate increases, so does the frictional pressure loss and this changes the ratio between the upper and lower zones to about 35% and 65%. This is shown in Figure 2. Note, however, that the fracture gradient of the formation imposes a constraint on the injection rate. Figure 3 shows that the desired partitioning can be achieved when both formations are equipped with variable flow control valves. By adjusting the valve apertures, the partitioning of the injectivity can be adjusted as reservoir conditions evolve. Three-Zone Injection This example concerns injection into three zones simultaneously, prompted by the limited number of well slots available on the platform from which this reservoir is being developed. In comparison to the previous example, there is greater contrast in the reservoir properties among the three zones, so that the middle zone has the highest injectivity index. The operator has initiated a water injection program for pressure maintenance, using vertical dual-string injection wells (two tubing strings), with the short string injecting into Zone A and the long string into Zones B and C. With this configuration, the approximate partitioning in injectivity is 25%, 50%, and 25% (Figure 4).
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractA multi-discipline integrated full field review (FFR) was conducted for Betty field, offshore Malaysia, in order to build a set of 3D predictive numerical models from multi-scale geological, seismic, petrophysical, reservoir and production engineering data. This study reassessed field stock tank oil initially in-place (STOIIP) volumes and remaining reserves, determined infill drilling potential and identified opportunities to improve both short-term and long-term field performance. Betty field comprises of multiple stacked, laterally continuous, vertically heterogeneous reservoirs. Some reservoirs have experienced relatively high recovery factor (RF) to date (i.e., >69%), while others have underperformed (RF<15%). One of the priorities of this study was to resolve these anomalies.Detailed evaluation of the core identified five predominant lithofacies in each reservoir. Horizons, which were interpreted from 3D seismic and tied to the well logs, formed the framework for the static model structure. The neural networkbased lithofacies analysis for all the wells enabled distribution of the lithofacies data in a 3D geocellular model, which significantly improved the accuracy of the rock property distribution in the reservoirs. Vertical trend functions based on electrofacies logs were input to control the facies content of each layer, and the resultant facies model was used to control the porosity distribution using Sequential Gaussian Simulation (SGS) throughout a fine grid (11 million cells, 1-ft. layers). SGS was also used as co-simulation for permeability, coupled with vertical and horizontal variograms honoring the appropriate facies proportion in each layer. The static model was upscaled (to 600,000 cells, 6-ft layers) after correlating the lithofacies from well to well with over 2800 geological markers to preserve vertical heterogeneity. The initial saturation distribution determined from gravity-capillary equilibrium and a single J-function anchored at the base of the water-oil transition zone, was essentially corroborated by petrophysical analyses. Representative drainage/imbibition relative permeability curves were established from the available data and deployed for dynamic modeling.As a testament to the integrity of the data, the technical interpretation and overall approach that was used, it was found that almost 75% of the 66 historical completions were essentially matched for 27 years of history after making trial runs of the dynamic model without any adjustments to the static reservoir description. Betty Field and Its UncertaintiesBetty field is located 40 km offshore Miri in the Baram Delta area of Sarawak, Malaysia. Discovered in 1968, its first production was in 1978. The average water depth is 225 ft. There are 22 stacked reservoirs of varying size and thickness between depths of 7,200 ft sub-sea (ss) and 9650 ft ss. The field is operated by 1 platform that contains 24 production wells with 48 tubing strings. There have been 66 historical completion...
In this paper we present the results of a material balance study for a mature field in East Malaysia. The field consists of several stacked sands and is highly faulted, resulting in a complex system of several compartmentalized reservoirs. The drive mechanisms of these reservoirs range from strong gas cap drive to strong water influx or combinations of these. Fourteen material balance models were built and the results analyzed. This study shows that proper integration of all pressure, production and geological data is critical in defining reservoir compartmentalization and in analyzing the results of material balance (MB) analysis. In particular, analysis of the reservoir production behavior and the fluid contact movement over time is essential in narrowing the uncertainty in the parameters used in the model. In building the MB model, two new techniques were proposed and successfully used:moving linear regression for generating the input pressure for the MB modelproduction-derived relative permeability data for MB prediction. Applying these techniques resulted in a well-behaved model and a realistic production profile. Introduction The Samarang field is located offshore, in Sabah, East Malaysia. The field was discovered in 1972 and began production in mid-1975. By the end of 2004, nearly 400 MMSTB of oil had been produced from 18 sand sequences with the main reservoirs being the J, K, and M sands (Fig. 1). The major sand bodies were deposited in a shallow near-shore marine environment during the late Miocene to early Oligocene eras. The field is situated at the crest of a rollover anticline and is characterized by a series of northeast-southwest trending faults. This fault system divides the main producing area into sub-parallel fault blocks. The fluid properties in the field vary vertically with oil density gradually increasing from 35–37 ºAPI in the deeper reservoirs to 19 ºAPI in the shallow reservoirs. Most reservoirs have an initial gas cap with the ratio of gas cap volume to oil volume ranging from 0.1 to 3.0. The production history shows that most reservoirs experienced strong water influx. In 2004 a field review project was initiated to investigate the potential of the field and to look for opportunities to increase oil and gas recovery. Material balance analysis is a part of that integrated study. The objective of the material balance analysis is to investigate the drive mechanism and parameters of each reservoir unit and their effect on the fluid recovery. Some results of the MB analysis will be used in the 3D full field modeling. Fourteen MB models were built and analyzed using the MBAL program. For all models, we applied a workflow (Fig. 2) paying particular attention to getting a representative MB model and reducing the uncertainty. The following section will examine, in detail, the steps applied to our MB models. Model Preparation Data Inspection and Exclusion. For a complex reservoir with wells in different compartments, identifying those wells and determining the main reservoir area to be modeled is crucial. As a one-cell simulation model, the MB model should include only production and pressure data of the wells in the same hydraulic unit. The first task in building the MB model is a careful examination of all wells and a determination of the model scope. RFT data, static pressure data and the geological interpretation of the fault system in the field are the main data sources for identifying the compartment. Fig. 3 illustrates an example of one of the sand units in the field - the K5/7 sands. We plotted RFT pressure on top of the static pressure data for all wells intercepting this sand unit. The majority of the static pressure survey formed a clear trend, which represents the reservoir pressure of the main reservoir unit. Pressure data, especially the RFT data for several wells, show significant deviation from this trend. These wells, after being checked against fault geometry and location, were removed from the MB model. After data inspection and exclusion, only wells in the same production area for which the pressure data indicated one hydraulic unit were included in the MB model. The resulting pressure data for this model is shown in Fig. 4. The information gained from this step also helped us to determine the geometry of the aquifer. In this case, the rightmost fault in Fig. 3 is identified as a sealing fault. Therefore, as an initial estimate of the aquifer geometry, it is best to assume a radial system with an encroachment angle of about 180 deg.
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