Comparative Study of Field Development Scenarios of the Elk City Gas Reservoir using Reservoir Simulation

2020 ◽  
Author(s):  
Marshal E. Wigwe ◽  
Mohammad I. Basit ◽  
Fathi Elldakli ◽  
Samuel Dambani ◽  
Rosemary Mmuenu ◽  
...  
Author(s):  
Anita Theresa Panjaitan ◽  
Rachmat Sudibjo ◽  
Sri Fenny

<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>


2021 ◽  
Author(s):  
Obinna Somadina Ezeaneche ◽  
Robinson Osita Madu ◽  
Ishioma Bridget Oshilike ◽  
Orrelo Jerry Athoja ◽  
Mike Obi Onyekonwu

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.


PETRO ◽  
2018 ◽  
Vol 4 (4) ◽  
Author(s):  
Muhamad Taufan Azhari

<p>Reservoir simulation is an area of reservoir engineering in which computer models are used to predict the flow of fluids through porous media. Reservoir simulation process starts with several steps; data preparation, model and grid construction, initialization, history matching and prediction. Initialization process is done for matching OOIP or total initial hydrocarbon which fill reservoir with hydrocarbon control volume with volumetric method.</p><p>To aim the best encouraging optimum data, these development scenarios of TR Field Layer X will be predicted for 30 years (from 2014 until January 2044). Development scenarios in this study consist of 4 scenarios : Scenario 1 (Base Case), Scenario 2 (Base Case + Reopening non-active wells), Scenario 3 (scenario 2 + infill production wells), Scenario 4 (Scenario 2 + 5 spot pattern of infill injection wells).</p>


2021 ◽  
Author(s):  
Ricko Rizkiaputra ◽  
Satrio Goesmiyarso ◽  
Jufenilamora Nurak ◽  
Krishna Pratama Laya ◽  
Dimmas Ramadhan ◽  
...  

Abstract Even though the downhole gauges and wellhead meter (wet gas meter) have been invented decades ago, having them installed in every wells are still considered as a luxury for many companies. However, does this view still reasonable for a tight gas reservoir let alone located in a remote area? This study will describe the benefit of having both equipment for reservoir management practice in one of the biggest tight gas reservoirs in Indonesia. Generally, reservoir management is an iterative process that incorporates the analysis of reservoir characterization, development plan, implementation, and monitoring. There are many analyses from the reservoir management process that can be performed using above mentioned equipment. Several analyses have been performed, such as: (i) Interference Test and Pressure Transient Analysis (PTA) after well is completed; (ii) Evolution of connected volume since early production until present day using Dynamic Material Balance (DMB); (iii) Flow regime and reservoir properties using Rate Transient Analysis (RTA); and (iv) Reservoir simulation: regular model update and project opportunity identification. In this study, the above-mentioned analyses are performed in one of the massive tight gas reservoir in Indonesia that is located in the remote area. Having a complete reservoir surveillance tools such as downhole gauges and wellhead meter on each wells is beneficial for reservoir management practice. Precious subsurface data can be obtained anytime without having to wait for equipment mobilization to location. This is critical for managing tight gas reservoir which usually demands robust subsurface data to reduce its uncertainties. There are several findings based on the above mentioned analyses, such as: (i) The interference test indicates there is reservoir connectivity among the production wells; (ii) The PTA indicates that the reservoir has tight properties, although longer buildup/observation time is still needed to better understand the reservoir characteristics in wider scale; (iii) The DMB analysis can be performed even in daily basis to provide the insight on connected gas initial in place (GIIP) evolution through time, as in this case it still shows an increasing GIIP through time which is suspected due to the transient flow regime on the wells; (iv) The RTA can also be performed in similar fashion, if it is combine with other analyses, this analysis able to provide a multi-scale reservoir properties investigation from near wellbore to far field and flow period observation (boundary observation) through time, as in this case the reservoir properties is tight and flow is still in transient period; (v) It increases robustness of reservoir simulation update since it is supported by many analyses, as such, series of hopper can be confidently presented to management, as in this case a project of well stimulation (Acid Fracturing) has been performed successfully and opportunity of further field development plan can be identified. This paper shows that, for the tight reservoir in the remote location, having each well equipped with downhole gauges and dedicated wellhead meter is significantly increasing the robustness of reservoir management process. Thus, providing economic optimization for the managed asset. Regarding the capital that is invested at the beginning, it will simply pay out quickly, looking at the time and resources that need to be spent for having equipment on site.


2021 ◽  
pp. 1-18
Author(s):  
Shaoqing Sun ◽  
David A. Pollitt

Summary Benchmarking the recovery factor and production performance of a given reservoir against applicable analogs is a key step in field development optimization and a prerequisite in understanding the necessary actions required to improve hydrocarbon recovery. Existing benchmarking methods are principally structured to solve specific problems in individual situations and, consequently, are difficult to apply widely and consistently. This study presents an alternative empirical analog benchmarking workflow that is based upon systematic analysis of more than 1,600 reservoirs from around the world. This workflow is designed for effective, practical, and repeatable application of analog analysis to all reservoir types, development scenarios, and production challenges. It comprises five key steps: (1) definition of problems and objectives; (2) parameterization of the target reservoir; (3) quantification of resource potential; (4) assessment of production performance; and (5) identification of best practices and lessons learned. Problems of differing nature and for different objectives require different sets of analogs. This workflow advocates starting with a broad set of parameters to find a wide range of analogs for quantification of resource potential, followed by a narrowly defined set of parameters to find relevant analogs for assessment of production performance. During subsequent analysis of the chosen analogs, the focus is on isolating specific critical issues and identifying a smaller number of applicable analogs that more closely match the target reservoir with the aim to document both best practices and lessons learned. This workflow aims to inform decisions by identifying the best-in-class performers and examining in detail what differentiates them. It has been successfully applied to improve hydrocarbon recovery for carbonate, clastic, and basement reservoirs globally. The case studies provided herein demonstrate that this workflow has real-world utility in the identification of upside recovery potential and specific actions that can be taken to optimize production and recovery.


2021 ◽  
Author(s):  
Jim Browning ◽  
Sheldon Gorell

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.


SPE Journal ◽  
2019 ◽  
Vol 24 (04) ◽  
pp. 1526-1551
Author(s):  
Atefeh Jahandideh ◽  
Behnam Jafarpour

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.


1986 ◽  
Vol 26 (1) ◽  
pp. 447
Author(s):  
A.M. Younes ◽  
G.O. Morrell ◽  
A.B. Thompson

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.


Author(s):  
Abdulaziz S. Al-Qasim ◽  
Mohan Kelkar

Abstract To perform an optimization study for a green field (newly discovered field), one must collect the information from different parts of the field and integrate these data as accurately as possible in order to construct the reservoir image. Once the image, or alternate images, are constructed, reservoir simulation allows prediction of dynamic performance of the reservoir. As field development progresses, more information becomes available, enabling us to continually update and, if needed, correct the reservoir description. The simulator can then be used to perform a variety of exercises or scenarios, with the goal of optimizing field development and operation strategies. We are often confronted with important questions related to the most efficient well spacing and location, the optimum number of wells needed, the size of the production facility needed, the optimum production strategies, the location of the external boundaries, the intrinsic reservoir properties, the predominant recovery mechanism, the best time and location to employ infill drilling and the best time and type of the improved recovery technique we should implement. These are some of the critical questions we may need to answer. A reservoir simulation study is the only practical means by which we can design and run tests to address these questions in sufficient detail. From this perspective, reservoir simulation is a powerful screening tool. The magnitude, time and complexity of a reservoir simulation problem depends in part on the available computational environment. For instance, simple material balance calculations are now routinely performed on desktop personal computers, while running a field-scale three-dimensional simulator may call for the use of a supercomputer and may take many days to finish. We must also take into account the storage requirements and limitations, CPU time demand and the general architecture of the machine. The problem arises when there is a large amount of data available with a study objective that requires running several scenarios incorporating millions of grid cells. This will limit the applicability of reservoir simulation as it will be computationally very inefficient. For example, determining the optimum well locations in a field that will result in the most efficient production rate scenario requires a large number of simulation runs which can make it very inefficient. This is because one will have to consider multiple well scenarios in multiple realizations. The main purpose of this paper is to use a novel methodology known as the Fast Marching Method (FMM) to find the optimum well locations in a green oil field that will result in the most efficient production rate scenario. The concept of radius of investigation is fundamental to well test analysis. The current well test analysis relies on analytical solutions based on homogeneous or layered reservoirs. The FMM will enable us to calculate the radius of investigation or pressure front as a function of time without running any simulation and with a high degree of accuracy. The calculations can be done in a matter of seconds for multi-millions of cells.


Sign in / Sign up

Export Citation Format

Share Document