<p>Y Field which located around 28 km south east of Jakarta was discovered in 1989. Three wells have been drilled and suspended. The initial gas ini place (IGIP) of the field is 40.53 BSCF. The field will be developed in 2011. In this study, reservoir simulation model was made to predict the optimum development strategy of the field. This model consisted of 1,575,064 grid cells which were built in a black oil simulator. Two field development scenarios were defined with and without compressor. Simulation results show that the Recovery Factor at thel end of the contract is 61.40% and 62.14% respectively for Scenarios I and II without compressor. When compressor is applied then Recovey Factor of Scenarios I and II is 68.78% and 74.58%, correspondingly. Based on the economic parameters, Scenario II with compressor is the most <br />attractive case, where IRR, POT, and NPV of the scenario are 41%, 2.9 years, and 14,808 MUS$.</p>
Abstract
Proper understanding of reservoir producing mechanism forms a backbone for optimal fluid recovery in any reservoir. Such an understanding is usually fostered by a detailed petrophysical evaluation, structural interpretation, geological description and modelling as well as production performance assessment prior to history matching and reservoir simulation.
In this study, gravity drainage mechanism was identified as the primary force for production in reservoir X located in Niger Delta province and this required proper model calibration using variation of vertical anisotropic ratio based on identified facies as against a single value method which does not capture heterogeneity properly. Using structural maps generated from interpretation of seismic data, and other petrophysical parameters from available well logs and core data such as porosity, permeability and facies description based on environment of deposition, a geological model capturing the structural dips, facies distribution and well locations was built. Dynamic modeling was conducted on the base case model and also on the low and high case conceptual models to capture different structural dips of the reservoir.
The result from history matching of the base case model reveals that variation of vertical anisotropic ratio (i.e. kv/kh) based on identified facies across the system is more effective in capturing heterogeneity than using a deterministic value that is more popular. In addition, gas segregated fastest in the high case model with the steepest dip compared to the base and low case models. An improved dynamic model saturation match was achieved in line with the geological description and the observed reservoir performance. Quick wins scenarios were identified and this led to an additional reserve yield of over 1MMSTB.
Therefore, structural control, facies type, reservoir thickness and nature of oil volatility are key forces driving the gravity drainage mechanism.
Abstract
Economic optimization of a reservoir can be extremely tedious and time consuming. It is particularly difficult with many wells, some of which can become non-economic within the simulated time period. These problems can be mitigated by: 1) analyzing the results of a simulation once it has run, or 2) applying injection or production constraints at the well level. An example of option 1 would be integration with a spreadsheet or economic simulation package after the simulation has run. An example of option 2 would be to set a maximum water cut, upon which the well constraints could be changed, or the well could be shut in within the simulation. Both of these methods have drawbacks. If the goal is to account for how changes in a well operating strategy affects other wells, then analysis after the fact requires many runs to sequentially identify and modify well constraints at the correct times and in the correct order. In contrast, applying injection and production constraints to wells is not the same as applying true economic constraints. The objective of this work was to develop an automated method which includes economic considerations within the simulator to decrease the amount of time optimizing a single model and allows more time to analyze uncertainty within the economic decision making process.
This study developed automated methods and procedures to include economic calculations within the context of a standard reservoir simulation. The method utilized modifications to available conditional logic features to internally include and export key economic metrics to support appropriate automatic field development changes. This method was tested using synthetic models with different amounts of wells and operating conditions. It was validated using after the fact calculations on a well by well basis to confirm the process.
People costs are always among the most significant associated with running a business. Therefore, it is imperative for people to be as efficient and productive as possible. The method presented in this study significantly reduces the amount of time and effort associated with tedious and manual manipulations of simulation models. These savings enable an organization to focus on more value-added activities including, but not limited to, accurately optimizing and estimating of uncertainty associated decisions supported by reservoir simulation.
Summary
Reservoir simulation is a valuable tool for performance prediction, production optimization, and field-development decision making. In recent years, significant progress has been made in developing automated workflows for optimization of production and field development by combining reservoir simulation with numerical optimization schemes. Although optimization under geologic uncertainty has received considerable attention, the uncertainty associated with future development activities has not yet been considered in field-development optimization. In practice, reservoirs undergo extensive development activities throughout their life cycle. Disregarding the possibility of future developments can lead to field-performance predictions and optimization results that might be far from optimal. This paper presents a stochastic optimization formulation to account for the uncertainty in future development activities while optimizing current decision variables (e.g., well controls and locations). A motivating example is presented first to demonstrate the significance of including the uncertainty in future drilling plans in oilfield-development optimization. Because future decisions might not be implemented as planned, a stochastic optimization framework is developed to incorporate future drilling activities as uncertain (random) variables. A multistage stochastic programming framework is introduced, in which the decision maker selects an optimal strategy for the current stage decisions while accounting for the uncertainty in future development activities. For optimization, a sequential approach is adopted whereby well locations and controls are repeatedly optimized until improvements in the objective function fall below a threshold. Case studies are presented to demonstrate the advantages of treating future field-development activities as uncertain events in the optimization of current decision variables. In developing real fields, where various unpredictable external factors can cast uncertainty regarding future drilling activities, the proposed approach provides solutions that are more robust and can hedge against changes/uncertainty in future development plans better than conventional workflows.
The West Kingfish Field in the Gippsland Basin, offshore Victoria, has been developed from the West King-fish platform by Esso Australia Ltd (operator) and BHP Petroleum.The structure is an essentially separate, largely stratigraphic accumulation that forms the western flank of the Kingfish feature. A total of 19 development wells were drilled from the West Kingfish platform between October 1982 and May 1984. Information provided by these wells was used in a West Kingfish post-development geologic study and a reservoir simulation study.As a result of these studies the estimated recoverable oil volume has been increased 55 per cent to 27.0 stock tank gigalitres (170 million stock tank barrels). The studies also formed the technical basis for obtaining new oil classification of the P-1.1 reservoir which is the only sand body that has been found in the Gurnard Formation in the Kingfish area.The simulation study was accomplished with an extremely high level of efficiency due to the extensive and effective use of computer graphics technology in model construction, history matching and predictions.Computer graphics technology has also been used very effectively in presenting the simulation study results in an understandable way to audiences with various backgrounds. A portable microcomputer has been used to store hundreds of graphic displays which are projected with a large screen video projector.Presentations using this new display technology have been well received and have been very successful in conveying the results of a complex reservoir simulation study and in identifying future field development opportunities to audiences with various backgrounds.
Economic Evaluation of Y Gas Field Development Using Reservoir Simulator