Gas Reservoir Simulation Using Semi-Implicit Rates With Multiple Compressor Stations

1981 ◽  
Author(s):  
J.O. Baldwin ◽  
W.D. Kirchner ◽  
J.H. Kennedy
Keyword(s):  
Reservoir Simulation ◽  
Gas Reservoir

The Advantages of Having Dedicated Downhole Gauges and Wellhead Meter: A Reservoir Management Perspective from a Massive Tight Gas Reservoir in the Remote Area

2021 ◽  
Author(s):  
Ricko Rizkiaputra ◽  
Satrio Goesmiyarso ◽  
Jufenilamora Nurak ◽  
Krishna Pratama Laya ◽  
Dimmas Ramadhan ◽  
...  
Keyword(s):  
Reservoir Simulation ◽  
Transient Analysis ◽  
Management Practice ◽  
Reservoir Management ◽  
Remote Area ◽  
Tight Gas ◽  
Reservoir Properties ◽  
Gas Reservoir ◽  
Tight Gas Reservoir ◽  
Interference Test

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.

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 ◽  
...  
Keyword(s):  
Comparative Study ◽  
Reservoir Simulation ◽  
Gas Reservoir ◽  
Field Development ◽  
Development Scenarios ◽  
City Gas

Efficient Design of Reservoir Simulation Studies for Development and Optimization

2007 ◽  
Vol 10 (06) ◽  
pp. 629-637 ◽  
Author(s):  
Subhash Kalla ◽  
Christopher David White
Keyword(s):  
Experimental Design ◽  
Reservoir Simulation ◽  
Computer Experiments ◽  
Orthogonal Arrays ◽  
Economic Factors ◽  
Development Studies ◽  
Simulation Studies ◽  
Gas Reservoir ◽  
Response Models ◽  
Production Plans

Summary Development studies examine geologic, engineering, and economic factors to formulate and optimize production plans. If there are many factors, these studies are prohibitively expensive unless simulation runs are chosen efficiently. Experimental design and response models improve study efficiency and have been widely applied in reservoir engineering. To approximate nonlinear oil and gas reservoir responses, designs must consider factors at more than two levels—not just high and low values. However, multilevel designs require many simulations, especially if many factors are being considered. Partial factorial and mixed designs are more efficient than full factorials, but multilevel partial factorial designs are difficult to formulate. Alternatively, orthogonal arrays (OAs) and nearly-orthogonal arrays (NOAs) provide the required design properties and can handle many factors. These designs span the factor space with fewer runs, can be manipulated easily, and are appropriate for computer experiments. The proposed methods were used to model a gas well with water coning. Eleven geologic factors were varied while optimizing three engineering factors. An NOA was specified with three levels for eight factors and four levels for the remaining six factors. The proposed design required 36 simulations compared to 26,873,856 runs for a full factorial design. Kriged response surfaces are compared to polynomial regression surfaces. Polynomial-response models are used to optimize completion length, tubinghead pressure, and tubing diameter for a partially penetrating well in a gas reservoir with uncertain properties. OAs, Hammersley sequences (HSs), and response models offer a flexible, efficient framework for reservoir simulation studies. Complexity of Reservoir Studies Reservoir studies require integration of geologic properties, drilling and production strategies, and economic parameters. Integration is complex because parameters such as permeability, gas price, and fluid saturations are uncertain. In exploration and production decisions, alternatives such as well placement, artificial lift, and capital investment must be evaluated. Development studies examine these alternatives, as well as geologic, engineering, and economic factors to formulate and optimize production plans (Narayanan et al. 2003). Reservoir studies may require many simulations to evaluate the many factor effects on reservoir performance measures, such as net present value (NPV) and breakthrough time. Despite the exponential growth of computer memory and speed, computing accurate sensitivities and optimizing production performance is still expensive, to the point that it may not be feasible to consider all alternative models. Thus, simulation runs should be chosen as efficiently as possible. Experimental design addresses this problem statistically, and along with response models, it has been applied in engineering science (White et al. 2001; Peng and Gupta 2004; Peake et al. 2005; Sacks et al. 1989a) toMinimize computational costs by choosing a small but statistically representative set of simulation runs for predicting responses (e.g., recovery)Decrease expected error compared with nonoptimal simulation designs (i.e., sets of sample points)Evaluate sensitivity of responses to varying factorsTranslate uncertainty in input factors to uncertainty in predicted performance (i.e., uncertainty analysis)Estimate value of information to focus resources on reducing uncertainty in factors that have the most significant effect on response uncertainty to help optimize engineering factors.

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