Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Numerical reservoir simulation can be defined as the the process of constructing and running a model that mimic the appearance and flow dynamics of an actual reservoir system, including the subsurface porous and permeable reservoir and its other physical components to produce (wellbore) and process the reservoir fluids (surface facilities). The model properties are typically populated from a detailed geological model to capture the heterogeneity of the reservoir system and its effects on the flow. The formulation includes the discretized forms of the flow equations that describe multiphase fluid flow in porous media. These equations are linearized either at the partial differential equation level or at the discretized level to obtain a linear system of equations. The solution is represented at both time and space domains to solve for the time dependent characteristics such as pressures, fluid saturations/compositions and temperatures, which are representative of the performance of a reservoir. Because of the benefits that have been made possible with developments in both theory/formulation/solution-related aspects and computational technologies, numerical reservoir simulation has become a standard tool for decision-making purposes in the petroleum industry. Although these advantages are quite evident, it is still critically important to understand the strengths and weaknesses of the approach similar to any other available approach to forecast the reservoir performance. Objectives and scope of a reservoir simulation study must be carefully determined after considering available resources. This chapter gives an overview of numerical modeling of hydrocarbon reservoirs for those who are not familiar with the subject. Therefore, theoretical foundations are kept such that to describe the general characteristics of a three-phase (black-oil formulation) model in rectangular coordinates, which is the most common type of modeling approach used in most of the reservoir-modeling studies. After summarizing the black-oil formulation and its theoretical foundation, application and practical aspects are presented to highlight the important characteristics of a typical reservoir-simulation study by following the most common guidelines and practical rules followed in the petroleum industry.
Numerical reservoir simulation can be defined as the the process of constructing and running a model that mimic the appearance and flow dynamics of an actual reservoir system, including the subsurface porous and permeable reservoir and its other physical components to produce (wellbore) and process the reservoir fluids (surface facilities). The model properties are typically populated from a detailed geological model to capture the heterogeneity of the reservoir system and its effects on the flow. The formulation includes the discretized forms of the flow equations that describe multiphase fluid flow in porous media. These equations are linearized either at the partial differential equation level or at the discretized level to obtain a linear system of equations. The solution is represented at both time and space domains to solve for the time dependent characteristics such as pressures, fluid saturations/compositions and temperatures, which are representative of the performance of a reservoir. Because of the benefits that have been made possible with developments in both theory/formulation/solution-related aspects and computational technologies, numerical reservoir simulation has become a standard tool for decision-making purposes in the petroleum industry. Although these advantages are quite evident, it is still critically important to understand the strengths and weaknesses of the approach similar to any other available approach to forecast the reservoir performance. Objectives and scope of a reservoir simulation study must be carefully determined after considering available resources. This chapter gives an overview of numerical modeling of hydrocarbon reservoirs for those who are not familiar with the subject. Therefore, theoretical foundations are kept such that to describe the general characteristics of a three-phase (black-oil formulation) model in rectangular coordinates, which is the most common type of modeling approach used in most of the reservoir-modeling studies. After summarizing the black-oil formulation and its theoretical foundation, application and practical aspects are presented to highlight the important characteristics of a typical reservoir-simulation study by following the most common guidelines and practical rules followed in the petroleum industry.
Summary In general, successful applications of horizontal wells have been limited to high-permeability reservoirs and unconventional formations such as coal, chalk, and shale. Conversely, few tight-gas-sandstone reservoirs that require stimulation have realized sustained success with horizontal completions. One example of such success is the Cleveland Sand of north Texas and the Oklahoma Panhandle. Very recently, some success with horizontals has been observed in the Bossier and Cotton Valley Sands of East Texas and north Louisiana. Horizontal wells are commonly two to four times more expensive to drill and complete than offset vertical wells, yet they are theoretically capable of up to three to five times the production. Higher gas prices have lead to potentially better economics for horizontal wells (Mulder et al. 1992). However, research shows that in practice, many of these wells typically produce only 10 to 30% more than offset vertical wells. With costs more than double those of vertical wells, the economics is obviously unfavorable. This paper discusses ways to identify and manage risks when planning, drilling, and completing horizontal wells in tight-sandstone formations to improve success. Evidence has shown that shortcuts and blanket approaches do not work usually in these completion environments. A multitude of lithological and depletion possibilities exist as risks that need to be identified and managed through appropriate application of integrated drilling and completion technologies. Each risk may require different drilling and completion considerations in order to succeed. There is simply no recipe for repeat success. A detailed method is presented to identify, understand, and manage risk associated with horizontal wells drilled in tight-gas-sandstone reservoirs. The method will address all of the complex subjects that need to be considered for the successful placement and completion of a horizontal well, including reservoir description (both static and dynamic), well design, drilling, stimulation, and production. It will also illustrate consequences of what may happen if these issues are not considered properly. Through this method, horizontal-well feasibility and economic results can be determined. If a horizontal well has been determined to be viable economically, this method can consistently provide a solution as to what the best completion type (vertical or horizontal) is to recover reserves and enhance recovery efficiency in tight-gas-sandstone reservoirs.
Within the research framework of the "Integrated System Approach Petroleum Production" (ISAPP) knowledge center of TNO, TU Delft and Shell, the necessity of taking the interaction between dynamic reservoir and dynamic well behavior into account when optimizing a producing asset is investigated. To simulate dynamic phenomena in the well and in the reservoir, a dynamic multiphase well simulation tool (OLGA) and a dynamic multiphase reservoir simulator (MoReS) have been used. Both simulators have been coupled using an explicit scheme. The dynamic well simulator, the dynamic reservoir simulator and the coupled dynamic well-reservoir simulator have been used to simulate a realistic test case which consists of a horizontal well with three inflow sections located in a thin oil rim. A number of scenarios are investigated that play a crucial role during different stages of the well's lifetime: naturally occurring phenomena, e.g. coning, and production dynamics, e.g. shut-in. The results of dynamic well simulations, dynamic reservoir simulations and coupled well-reservoir simulations are presented and an overview is given of the cases where the results of the coupled simulations are significantly more accurate in comparison to stand-alone well or reservoir simulations. For gas coning it is shown that the coupled simulator has much faster pressure transients after gas breakthrough than the dynamic reservoir simulator. Therefore, the coupled well-reservoir simulator should be used to simulate gas breakthrough and to optimize production using gas coning control. For small time scale phenomena, order of less then one day, the well and reservoir transients overlap. Simulations show that the coupled simulator is essential for an accurate prediction of the well-reservoir interaction during these small time scale phenomena. Introduction Production instabilities are undesirable and play a crucial role in the production lifetime and ultimate recovery of any reservoir. These instabilities can arise from or be governed by the interaction between the well and the reservoir.1 Production instabilities can be subdivided into two groups. Firstly, the naturally occurring dynamical phenomena, such as coning and slugging. Secondly, the production dynamical phenomena, such as shut-in, clean-up and gas lift heading. Figure 1 displays the time and spatial scales for different naturally occurring and production dynamical phenomena. The values of the time and spatial scales are indicative and based on experience. There are several phenomena which have a certain amount of overlap. In these areas it is expected that the well dynamics are strongly influenced by the reservoir dynamics and visa versa. Simulations are widely used to predict oil and gas production. The current status of these simulations is to either use a dynamic well model combined with some analytical reservoir model2 or to use a dynamic reservoir model combined with either lift tables or a steady state well model.3, 4 The disadvantage of these models is the fact that they underestimate the pre-mentioned well-reservoir interactions and therefore give non-realistic production forecast in cases where well-reservoir interactions play a crucial role.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.