Advances in Tightly Coupled Reservoir/ Wellbore/Surface-Network Simulation

2001 ◽  
Vol 4 (02) ◽  
pp. 114-120 ◽  
Author(s):  
V.J. Zapata ◽  
W.M. Brummett ◽  
M.E. Osborne ◽  
D.J. Van Nispen

Summary One of the most perplexing and difficult challenges in the industry is deciding how to develop a new oil or gas field. It is necessary to estimate recoverable reserves, design the most efficient exploitation strategy, decide where and when to drill wells and install surface facilities, and predict the rate of production. This requires a clear understanding of energy distribution and fluid movements throughout the entire system, under any given operational scenario or market-demand situation. Even after a reservoir-development plan is selected, there are many possible facility designs, each with different investment and operating costs. An important, but not always considered, fact is that each facility scheme could result in different future production rates owing to various types, sizes, and configurations of fluid-flow facilities. Selecting the best design for the asset requires the most accurate production forecasts possible over the forecast life cycle. No other single technology has the ability to provide this insight, as well as tightly coupled reservoir and facility simulation, because it combines all pertinent geological and engineering data into a single, comprehensive, dynamic model of the entire oilfield flow system. An integrated oilfield simulation system accounts for all dynamic flow effects and provides a test environment for quickly and accurately comparing alternative designs. This paper provides a brief background of this technology and gives a review of a major development project where it is currently being applied. Finally, we describe some recent significant advances in the technology that make it more stable, accurate, and rigorous. Introduction Finite-difference reservoir simulation is widely used to predict production performance of oil and gas fields. This is usually done in a "stand-alone" mode, where individual well performance is commonly calculated from pregenerated multiphase wellbore flow tables that cover various ranges of wellhead and bottomhole pressures, gas/oil ratios (GOR's) and water/oil ratios (WOR's). The reservoir simulator determines the predicted production rate from these tables, normally assuming a fixed wellhead pressure and using a flowing bottomhole pressure calculated by the reservoir simulator. With this scheme it is not possible to consider the changing flow-resistance effects of the piping system as various fluids merge or split in the surface network. Neglecting this interaction of the surface network can, in many cases, introduce substantial errors into predicted performance. Basing multimillion- (in some cases, billion-) dollar exploitation designs on performance predictions that are suboptimal can be very detrimental to the asset's long-range profitability. To help eliminate this problem, considerable attention is being given to coupling reservoir simulators and multiphase facility network simulators to improve the accuracy of forecasting. Landscape Surface-network simulation technology was first introduced in 1976.1 Although successfully applied in selected cases, the concept was not widely adopted because of the excessive additional computing demands on computers of that era. In those earlier applications, the time consumed by the facility calculations could actually exceed the reservoir calculations.2,3 As computer performance has increased by orders of magnitude, this has become less of an issue. Reservoir model sizes have increased dramatically with much finer grids that take advantage of the increased computer power, but there was no need for a corresponding increase in the size of the facility models. Today, with tightly coupled reservoir/wellbore/surface models, the facility calculations are a fairly small part of the overall computing time and there is considerable effort in the industry to build these types of systems.4,5 Chevron's current tightly coupled oilfield simulation system is CHEARS®***/PIPESOFT-2™****. CHEARS® is a fully implicit 3D reservoir simulator with black-oil, compositional, thermal, miscible, and polymer formulations. It has fully implicit dual porosity, dual permeability options, and unlimited multiple-level local grid refinement. PIPESOFT-2™ is a comprehensive multiphase wellbore/surface-network simulator. It has black-oil, compositional, CO2, steam, and non-Newtonian fluid capabilities. It can solve any type of complex nested looping, both surface and subsurface. The coupling is done at the wellbore completion interval, which is the natural domain boundary between the flow systems. We refer to our implementation as "tightly coupled" because information is dynamically exchanged directly between the simulators without any intermediate intervention. A simple representation of the interaction between the simulators is shown in Fig. 1. Gorgon Field Example The following is an example of how this technology is currently being used. The Gorgon field is a Triassic gas accumulation estimated to contain over 20 Tscf of gas, located 130 km offshore northwest Australia in 300 m of water (Fig. 2). It is currently undergoing development studies for an LNG project. Field and Model Description. The field is 45 km long and 9 km wide, and it comprises more than 2000 m of Triassic fluvial Mungaroo formation in angular discordance with a Jurassic-age unconformity. It has been subdivided into 11 vertical intervals (or zones) on the basis of regional sequence boundaries and depositional systems. These 11 zones were first modeled individually with an object-based modeling technique before being stacked into a 715-layer full-field geologic model. This model was subsequently scaled up to a 46-layer reservoir simulation model, reducing the size of the model from 4.5 million cells to 290,000 cells. While the scaleup process preserved the original 11 zone boundaries, the majority of the layers were located in regions identified as key flow units. In addition to vertical subdivision, seismic and appraisal well data suggest structural compartmentalization, resulting in six major fault blocks. After deactivating appropriate cells, the final simulation model contained 50,000 active cells and was initialized with 35 independent pressure regions. Each of these regions corresponds to a single zone in a single fault block.

2002 ◽  
Vol 5 (01) ◽  
pp. 11-23 ◽  
Author(s):  
A.H. Dogru ◽  
H.A. Sunaidi ◽  
L.S. Fung ◽  
W.A. Habiballah ◽  
N. Al-Zamel ◽  
...  

Summary A new parallel, black-oil-production reservoir simulator (Powers**) has been developed and fully integrated into the pre- and post-processing graphical environment. Its primary use is to simulate the giant oil and gas reservoirs of the Middle East using millions of cells. The new simulator has been created for parallelism and scalability, with the aim of making megacell simulation a day-to-day reservoir-management tool. Upon its completion, the parallel simulator was validated against published benchmark problems and other industrial simulators. Several giant oil-reservoir studies have been conducted with million-cell descriptions. This paper presents the model formulation, parallel linear solver, parallel locally refined grids, and parallel well management. The benefits of using megacell simulation models are illustrated by a real field example used to confirm bypassed oil zones and obtain a history match in a short time period. With the new technology, preprocessing, construction, running, and post-processing of megacell models is finally practical. A typical history- match run for a field with 30 to 50 years of production takes only a few hours. Introduction With the development of early parallel computers, the attractive speed of these computers got the attention of oil industry researchers. Initial questions were concentrated along these lines:Can one develop a truly parallel reservoir-simulator code?What type of hardware and programming languages should be chosen? Contrary to seismic, it is well known that reservoir simulator algorithms are not naturally parallel; they are more recursive, and variables display a strong dependency on each other (strong coupling and nonlinearity). This poses a big challenge for the parallelization. On the other hand, if one could develop a parallel code, the speed of computations would increase by at least an order of magnitude; as a result, many large problems could be handled. This capability would also aid our understanding of the fluid flow in a complex reservoir. Additionally, the proper handling of the reservoir heterogeneities should result in more realistic predictions. The other benefit of megacell description is the minimization of upscaling effects and numerical dispersion. The megacell simulation has a natural application in simulating the world's giant oil and gas reservoirs. For example, a grid size of 50 m or less is used widely for the small and medium-size reservoirs in the world. In contrast, many giant reservoirs in the Middle East use a gridblock size of 250 m or larger; this easily yields a model with more than 1 million cells. Therefore, it is of specific interest to have megacell description and still be able to run fast. Such capability is important for the day-to-day reservoir management of these fields. This paper is organized as follows: the relevant work in the petroleum-reservoir-simulation literature has been reviewed. This will be followed by the description of the new parallel simulator and the presentation of the numerical solution and parallelism strategies. (The details of the data structures, well handling, and parallel input/output operations are placed in the appendices). The main text also contains a brief description of the parallel linear solver, locally refined grids, and well management. A brief description of megacell pre- and post-processing is presented. Next, we address performance and parallel scalability; this is a key section that demonstrates the degree of parallelization of the simulator. The last section presents four real field simulation examples. These example cases cover all stages of the simulator and provide actual central processing unit (CPU) execution time for each case. As a byproduct, the benefits of megacell simulation are demonstrated by two examples: locating bypassed oil zones, and obtaining a quicker history match. Details of each section can be found in the appendices. Previous Work In the 1980s, research on parallel-reservoir simulation had been intensified by the further development of shared-memory and distributed- memory machines. In 1987, Scott et al.1 presented a Multiple Instruction Multiple Data (MIMD) approach to reservoir simulation. Chien2 investigated parallel processing on sharedmemory computers. In early 1990, Li3 presented a parallelized version of a commercial simulator on a shared-memory Cray computer. For the distributed-memory machines, Wheeler4 developed a black-oil simulator on a hypercube in 1989. In the early 1990s, Killough and Bhogeswara5 presented a compositional simulator on an Intel iPSC/860, and Rutledge et al.6 developed an Implicit Pressure Explicit Saturation (IMPES) black-oil reservoir simulator for the CM-2 machine. They showed that reservoir models over 2 million cells could be run on this type of machine with 65,536 processors. This paper stated that computational speeds in the order of 1 gigaflop in the matrix construction and solution were achievable. In mid-1995, more investigators published reservoir-simulation papers that focused on distributed-memory machines. Kaarstad7 presented a 2D oil/water research simulator running on a 16384 processor MasPar MP-2 machine. He showed that a model problem using 1 million gridpoints could be solved in a few minutes of computer time. Rame and Delshad8 parallelized a chemical flooding code (UTCHEM) and tested it on a variety of systems for scalability. This paper also included test results on Intel iPSC/960, CM-5, Kendall Square, and Cray T3D.


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>


2007 ◽  
Vol 10 (01) ◽  
pp. 50-59 ◽  
Author(s):  
Josef R. Shaoul ◽  
Aron Behr ◽  
George Mtchedlishvili

Summary This paper describes the development and capabilities of a novel and unique tool that interfaces a hydraulic fracture model and a reservoir simulator. This new tool is another step in improving both the efficiency and consistency of connecting hydraulic fracture engineering and reservoir engineering. The typical way to model hydraulically fractured wells in 3D reservoir simulators is to approximate the fracture behavior with a modified skin or productivity index (PI). Neither method captures all the important physics of flow into and through the fracture. This becomes even more critical in cases of multiphase flow and multilayered reservoirs. Modeling the cleanup phase following hydraulic fracture treatments can be very important in tight gas reservoirs, and this also requires a more detailed simulation of the fracture. Realistic modeling of horizontal wells with multiple hydraulic fractures is another capability that is needed in the industry. This capability requires more than an approximate description of the fracture(s) in the reservoir-simulation model. To achieve all the capabilities mentioned above, a new tool was developed within a commercial lumped 3D fracture-simulation model. This new tool enables significantly more accurate prediction of post-fracture performance with a commercial reservoir simulator. The automatically generated reservoir simulator input files represent the geometry and hydraulic properties of the reservoir, the fracture, the damaged zone around the fracture, and the initial pressure and filtrate fluid distribution in the reservoir. Consistency with the fracture-simulation inputs and outputs is assured because the software automatically transfers the information. High-permeability gridblocks that capture the 2D variation of the fracture conductivity within the reservoir simulator input files represent the fracture. If the fracture width used in the reservoir model is larger than the actual fracture width, the permeability and porosity of the fracture blocks are reduced to maintain the transmissibility and porous volume of the actual fracture. Both proppant and acid fracturing are handled with this approach. To capture the changes in fracture conductivity over time as the bottomhole flowing pressure (BHFP) changes, the pressure-dependent behavior of the fracture is passed to the reservoir simulator. Local grid refinement (LGR) is used in the region of the wellbore and the fracture tip, as well as in the blocks adjacent to the fracture plane. Using small gridblocks adjacent to the fracture plane is needed for an adequate representation of the filtrate-invaded zone using the leakoff depth distribution provided by the fracture simulator. The reservoir simulator input can be created for multiphase fluid systems with multiple layers and different permeabilities. In addition, different capillary pressure and relative permeability saturation functions for each layer are allowed. Introduction Historically, there have been three basic approaches commonly used for predicting the production from hydraulically fractured wells. First, analytic solutions were most commonly used, based on an infinite-conductivity or, later, a finite-conductivity fracture with a given half-length. This approach also was extended to cover horizontal multiple fractured wells (Basquet et al. 1999). With the development of reservoir simulators, two other approaches were developed. For complicated multiwell, multilayer, multiphase simulations (i.e., full-field models), the fracture stimulation was usually approximated as a negative skin. This is the same as increasing the effective wellbore radius in the simulation model. An alternate approach, developed initially for tight gas applications, was to develop a special-purpose numeric reservoir simulator that could explicitly model the flow in the fracture and take into account the special properties of the proppant, such as the stress-dependent permeability or the possibility of non-Darcy flow. Such models typically were limited to a single-layer, single-phase (oil or gas) situation.


2016 ◽  
Vol 8 (6) ◽  
pp. 971-991
Author(s):  
Zheng Li ◽  
Shuhong Wu ◽  
Jinchao Xu ◽  
Chensong Zhang

AbstractIn this paper, we focus on graphical processing unit (GPU) and discuss how its architecture affects the choice of algorithm and implementation of fully-implicit petroleum reservoir simulation. In order to obtain satisfactory performance on new many-core architectures such as GPUs, the simulator developers must know a great deal on the specific hardware and spend a lot of time on fine tuning the code. Porting a large petroleum reservoir simulator to emerging hardware architectures is expensive and risky. We analyze major components of an in-house reservoir simulator and investigate how to port them to GPUs in a cost-effective way. Preliminary numerical experiments show that our GPU-based simulator is robust and effective. More importantly, these numerical results clearly identify the main bottlenecks to obtain ideal speedup on GPUs and possibly other many-core architectures.


2010 ◽  
Author(s):  
Saad Menahi Al-Mutairi ◽  
Ehtesham M. Hayder ◽  
Ahmad Tariq Al-Shammari ◽  
Nayif Abdullah Al-Jama ◽  
Alberto Munoz

Author(s):  
Eric Flauraud ◽  
Didier Yu Ding

In the last two decades, new technologies have been introduced to equip wells with intelligent completions such as Inflow Control Device (ICD) or Inflow Control Valve (ICV) in order to optimize the oil recovery by reducing the undesirable production of gas and water. To optimally define the locations of the packers and the characteristics of the valves, efficient reservoir simulation models are required. This paper is aimed at presenting the specific developments introduced in a multipurpose industrial reservoir simulator to simulate such wells equipped with intelligent completions taking into account the pressure drop and multiphase flow. An explicit coupling or decoupling of a reservoir model and a well flow model with intelligent completion makes usually unstable and non-convergent results, and a fully implicit coupling is CPU time consuming and difficult to be implemented. This paper presents therefore a semi-implicit approach, which links on one side to the reservoir simulation model and on the other side to the well flow model, to integrate ICD and ICV.


Author(s):  
Athanasios Donas ◽  
Ioannis Famelis ◽  
Peter C Chu ◽  
George Galanis

The aim of this paper is to present an application of high-order numerical analysis methods to a simulation system that models the movement of a cylindrical-shaped object (mine, projectile, etc.) in a marine environment and in general in fluids with important applications in Naval operations. More specifically, an alternative methodology is proposed for the dynamics of the Navy’s three-dimensional mine impact burial prediction model, Impact35/vortex, based on the Dormand–Prince Runge–Kutta fifth-order and the singly diagonally implicit Runge–Kutta fifth-order methods. The main aim is to improve the time efficiency of the system, while keeping the deviation levels of the final results, derived from the standard and the proposed methodology, low.


Sign in / Sign up

Export Citation Format

Share Document