Skip Nav Destination
Filter
Filter
Filter
Filter
Filter

Update search

Filter

- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number

- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number

- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number

- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number

- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number

- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
- Paper Number

### NARROW

Format

Subjects

Date

Availability

1-7 of 7

Keywords: simulation model

Close
**Follow your search**

Access your saved searches in your account

Would you like to receive an alert when new items match your search?

*Close Modal*

Sort by

Proceedings Papers

Publisher: Petroleum Society of Canada

Paper presented at the Annual Technical Meeting, June 7–9, 1998

Paper Number: PETSOC-98-34

... displacement upstream oil & gas saturation reservoir enhanced recovery porosity sandstone blowdown gas saturation

**simulation****model**oil leg THE PETROLEUM SOCIETY PAPER 98-34 Gas Cap Blowdown of the Virginia Hills Belloy Reservoir F.C. Kuppe, S. Chugh Epic Consulting Services Ltd. J.D. Kyles...
Abstract

Abstract Concurrent gas and oil production from the Virginia Hills Belloy Shunda Unit #I Unit wan considered. A full field simulation study was used to evaluate the impact of initiating early blowdown of the gas cap. An excellent match of the production, pressures and two-phase interface movements was achieved. The model predicts that early blowdown and delayed blowdown cases achieve comparable ultimate oil and gas recoveries. The role of the weil established water fence between the gas cap and oil leg, unique reservoir characteristics and reservoir management strategy, which contribute to this result, are discussed Introduction The Virginia Hills Belloy field is located approximately 165 km northwest of Edmonton, Alberta in Townships 63 and 64, Range 13 and 14, W5M (see Figure 1). The field was discovered in 1970 during the development of the nearby Virginia Hills field and was initially developed as a gas pool. Updip movement of the oil column towards the gas cap led to the discovery of the oil leg in 1976. By 1978, the oil column was delineated with 21 oil wells. The gas hap was shut-in by 1980 after having produced approximately 800 E6m3. A waterflood was implemented as an updip line drive while also creating a water fence between the gas cap and oil reserves in 1981 (Figure 2). Downdip water injection began in 1991. Since 1994, a total of seven horizontal (five successful, two wet) wells have been drilled to recover bypassed oil. One of the five successful horizontal wells was drilled on the basis of this simulation study. With the field maturing, a simulation study was commissioned to determine the ultimate recovery and whether oil & gas recovery would be impacted by early blowdown of the gas cap. With the existing gas plant approaching turndown and economic limits, decommissioning the plant in 1999 would be followed by capital expenditures to re-inject the sour gas and eventual blowdown of the gas cap. Early blowdown would conserve capital and make efficient use of existing resources. In addition, concurrent production (early blowdown) would result in a reduction of the operating life by 12 years, and thereby also reduce associated expenses. From a regulatory perspective, approval for concurrent production is normally not considered unless the pool is in the final stages of depletion (greater than 90 % of ultimate recoverable oil) and the ultimate recovery is not impacted significantly. Depending on the recovery estimate used, current recovery Tom Belloy is between 80 % and 90 % of ultimate oil recovery. This study will show that concurrent production of oil and gas from the Belloy pool does not adversely affect hydrocarbon recovery. In fact, the Belloy field presents a unique opportunity where a well established water fence can be maintained to provide continued effective separation ofoil and gas production by maintaining the voidage replacement ratio. Geology Hydrocarbon production is from the Permian Belloy sandstone with a minor contribution Tom the underlying Mississippian Shunda carbonates. The sealing formation is the Nordegg shales of lower Jurassic age.

Proceedings Papers

Publisher: Petroleum Society of Canada

Paper presented at the Annual Technical Meeting, April 20–23, 1991

Paper Number: PETSOC-91-53

... Abstract An analysis of the response of naturally fractured reservoirs to thermal recovery processes is presented, utilizing a suite of dual continuum reservoir

**simulation****models**(dual porosity, multiple interacting continua, vertical matrix refinement and dual permeability). The effects of...
Abstract

Abstract An analysis of the response of naturally fractured reservoirs to thermal recovery processes is presented, utilizing a suite of dual continuum reservoir simulation models (dual porosity, multiple interacting continua, vertical matrix refinement and dual permeability). The effects of the different models as well a, many fracture and matrix properties on aspects of stearn cycling, steam drive and gravity drainage processes are discussed in some detail. While some factors are consistent with the isothermal response of naturally fractured reservoirs (in particular fracture spacing and the primary effect of matrix permeability), thermal phase behavior and heat flow effects in these reservoirs impart significantly different more complex behavior. Most of the naturally fractured reservoirs which are produced by using thermal processes contain very low mobility oil and therefore heat conduction plays a very important role at the initial stages of production. With increasing oil mobility, convective gravity and capillary forces lake over if the matrix permeability is fairly high or the reservoir is fractured extensively. During a production cycle in a stearn stimulation process, heal is conducted from matrix rock to fracture fluid which can increase the fluid's energy tremendously. Depending on the fracture fluid (water/oil) volatility, the additional energy can cause different phase behavior responses. Introduction Fractured reservoirs occur worldwide in the Middle East, Iran, Iraq, France, USA, Venezuela, Canada (Saidi (1987), van Golf-Racht, (1982)), and hold extensive hydrocarbon reserves. The presence of a large number of fractures throughout the reservoir provides extended area, of high permeability, where the fluid flows more easily. However, the productivity of these reservoirs depends on the porosity and permeability of the matrix, which stores most of the fluids in place. Production will cease in reservoirs with very Light matrix rock after the fracture network is depleted, because fluids arc not able to flow at reasonable rates from the matrix to the fracture. Reservoirs with fair matrix permeability will sustain production, because fluids from the matrix will flow into and replenish the fractures. Although fractured reservoirs have been known and produced for decades, a wide variety of production levels and reservoir responses have been observed. This, in turn has given impetus to more recent indepth analysis (both experimental and theoretical) of the underlying mechanisms. The utility of reservoir simulation models in decoupling and quantifying contributing factors has been recognized. Initially, the behavior of fractured reservoirs was simulated by "single porosity" models with two different approaches: Fraclure and matrix properties were averaged. With this approach the oil recovery is usually overpredicted, (Chen, et al (1987), Dean and Lo (1986)) especially in situations where the fracture spacing is large. Fracture and matrix were represented by separate grid blocks. This case has two major drawbacks for field simulation studies: a very large number of grid blocks is needed to represent the whole reservoir numerical difficulties arise due to great differences between fracture and matrix properties Later, Barenblalt, et al (1960) and Warren and Root (1963) introduced a simple dual continuum concept (the dual porosity model) for single phase flow.

Proceedings Papers

Publisher: Petroleum Society of Canada

Paper presented at the Annual Technical Meeting, January 1, 1989

Paper Number: PETSOC-89-40-57

... associated with GORcorrelation development, i.e. resolving rapid GAR change with minor changes inoil rate, can be overcome with this new approach. Once developed, the correlation can in turn be used with either conventionalmaterial balance models or coarsely gridded reservoir

**simulation****models**topredict...
Abstract

Abstract In Alberta there are many carbonate pools with large vertical relief havingsignificant gas caps or solvent zones above a diminishing oil leg. Productivityforecasting of the oil wells as gas or solvent coning becomes dominant, is ofprime concern. This paper describes a technique to develop a two parameterconing correlation from available production data, via a plotting procedure.The nature of the correlation lends itself to the development of an independentcrossplot which checks the consistency of the correlation with observed wellperformance. In addition, the difficulties often associated with GORcorrelation development, i.e. resolving rapid GAR change with minor changes inoil rate, can be overcome with this new approach. Once developed, the correlation can in turn be used with either conventionalmaterial balance models or coarsely gridded reservoir simulation models topredict future performance. Examples from the Westerose D-3 Pool will be usedto illustrate important aspects of the correlation development and productionforecasting procedure. Introduction There are many high relief carbonate reef pools throughout Alberta which arereaching the final stages of oil production. Historically, the pools have beenundergoing gravity stable miscible or immiscible displacement throughout theirproductive lives. For many of these pools the remaining oil sandwich hasreached the state, where coning of gas, solvent and/or water is unavoidable.There is still a need to optimize oil production from the remaining sandwichprior to these pools being placed on blowdown to recover the remaining solventor gas. The situation where significant coning takes place has always lead to problemswhen field production forecasts are necessary. This is as a result of thereservoir simulation tools available to engineers. Typically, one of twoapproaches has been undertaken. The first involves performing single wellconing studies for each well or group of wells and somehow extrapolatingperformance to the field. The weakness of this approach is the the lack ofinteraction of the single well model with the rest of the reservoir. A second approach is to model the system with a conventional large rectilineargrid system. Since these models cannot properly account for the localized nearwellbore drawdowns, separate single well radial models are used to generatevertical pseudo-relative permeability curves. These curves are then used asinput to the larger model. This is a time consuming process which involves aconsiderable number of runs with both sets of models with no guarantees ofsuccess. In practice this second approach has not been used due to thedifficulties of separating coning behavior from overall reservoir performance.An added complication is the need to continually re-run the single well radialflow model to update the vertical pseudo-relative permeability curvesthroughout the history matching process. Recently, advances in state of the art for reservoir simulation have been madewhich allow for local grid refinement around wells 2,3,4 . In this waythe entire study can be run from one model without recourse to artificialdevices. However, no practical guidelines for the degree of local gridrefinement necessary to adequately model coning are available in theliterature.

Proceedings Papers

Publisher: Petroleum Society of Canada

Paper presented at the Annual Technical Meeting, June 7–10, 1986

Paper Number: PETSOC-86-37-14

... production, and the interference between infill and offset wells. A reservoir

**simulation****model**is used to predict production forecast for the infill wells, and to estimate the incremental recovery versus the accelerated production from infill drilling. Introduction Infill drilling can increase...
Abstract

Abstract The estimation of incremental oil or gas recovery from infill drilling is essential to substantiate the recommendation to drill development wells or to evaluate the benefit of reducing the well spacing in a developed field. The purpose of this paper is to discuss four different techniques that can be used to quantify incremental oil recovery and accelerated production from infill drilling. These methods are summarized below. A reservoir continuity model illustrates the concept that infil1 drilling improves the continuity between wells, this in turn will improve the reservoir sweep efficiency and ultimate recovery. A plot of water-oil ratio on semi log scale versus cumulative oil production on linear scale is used to demonstrate the incremental oil recovered from infill drilling. Decline curve analysis is used to estimate the incremental recovery and accelerated production, and the interference between infill and offset wells. A reservoir simulation model is used to predict production forecast for the infill wells, and to estimate the incremental recovery versus the accelerated production from infill drilling. Introduction Infill drilling can increase ultimate gas or oil recovery but the incremental recovery from infill drilling varies for each pool because of difference in reservoir heterogeneity and fluid properties 1–3 . Consequently, determination of incremental recovery from infill drilling is a challenging task in reservoir study. A further complication is to quantify and differentiate the incremental recovery from the accelerated production. The benefits of incremental recovery and accelerated production from infi11 drilling have been extensively debated and documented 4,5 . This paper presents four methods that can be applied to estimate or evaluate both incremental recovery and accelerated production from infill drilling, however no one method is necessarily more accurate than another. Very often, the selection of a specific method depends on the availability of the reservoir performance data and the time constraint. If possible, all four methods should be used to determine incremental recovery. This would enhance the confidence of the results calculated by each method and to complement each other. To determine or evaluate incremental recovery from infill drilling, the applications of reservoir continuity model, water-oil ratioplot, decline curve analysis. and numerical simulation model are presented based on the field performance data and the experience that we have gained in our reservoir studies in Alberta. CONTINUITY MODEL Numerous reservoir studies have revealed that, in a continuous porous reservoir, drainage of fluids can occur effectively over large area in formation of either low or high permeability. The major factor that appears to restrict the drainage area by a well is the lack of continuity within the formation due to heterogeneity. The theory and case history indicating that infill drilling will increase reservoir continuity and hence improve waterflood pattern conformance in heterogeneous West Texas carbonate reservoirs have been published in literature 6–11 . Reservoir continuity is defined as the portion of the net pay that can be correlated and onnected between two or more well s at a particular well spacing.

Proceedings Papers

Publisher: Petroleum Society of Canada

Paper presented at the Annual Technical Meeting, June 10–12, 1975

Paper Number: PETSOC-75-01

... changes which would result from the use of different production schemes. An allied discipline within the Energy Engineering field that of Nuclear Reactor Power Plant design makes use of similar

**simulation****models**for prediction of system responses. While significant differences between the time spans...
Abstract

Abstract Reservoir Engineers are taking good advantage of the power of Digital, High Speed, Large Memory computers to process Mathematical Reservoir Models. These are used to explain the behaviour of, and describe existing reservoirs, through use of field data. They also predict production changes which would result from the use of different production schemes. An allied discipline within the Energy Engineering field that of Nuclear Reactor Power Plant design makes use of similar simulation models for prediction of system responses. While significant differences between the time spans studied by these two engineering sections exist, similar mathematical equations, numerical approximations, error minimization methods and presentations of results prevail. The paper briefly discusses Mathematical Reservoir simulation techniques and their shortcomings. It describes certain specific Nuclear Engineering Modelling techniques in greater depth. Comparisons between the techniques of the two disciplines are drawn. 1 t is suggested that confidence level estimation approaches used in Nuclear Engineering could be applied to Reservoir modelling. It is further suggested that simulation procedures are available from other disciplines, such as Nuclear Engineering, which can be employed by the Petroleum engineer as the scope of systems covered by simulation expands. Introduction It is often difficult to test, directly, hypotheses which seek to explain or predict complex processes. The systems to be studied may be inherently dangerous, sensitive, or have unmanageable physical dimensions, time and cost requirements. One useful method of exploring relationships and assumptions and analyzing and explaining in such cases, is to simulate the process with a model. In simulation modelling descriptions or analogies are used to develop logical representations of the object of study. These approximate its behaviour or characteristics. They ease visualization and can be more conveniently manipulated than the actual process or object. Such models may be theorized concepts, or physical emulations, either scaled or analogous, or they may be symbolic mathematical descriptions. Physical models are descriptive and the least abstract. They resemble the real prototype. Analog models are more abstract and use a set of properties which are different from but correlative with those of the process in question. Models which characterize a system through the use of mathematical relationships are the most abstract or symbolic or dissociated in form from the object of inquiry. They are, nevertheless, general, precise and can be manipulated exactly by utilizing the laws of mathematics. Their functions are designed empirically or conceptually to apply appropriate transformations to input data. The results are steered to match the process or object under study or to reconstitute options under varying conditions. Further distinctions can be made among models by degrees of linearity, stability, constraint and transience, etc. In the case of mathematical models, if the processes described are simple and precise they are Deterministic. If they are complex and predictable only within degrees of probability they are Stochastic or Hybrid. They may also be static or dynamic and within these categories steady and non-steady cases will be considered.

Proceedings Papers

Publisher: Petroleum Society of Canada

Paper presented at the Annual Technical Meeting, May 6–9, 1974

Paper Number: PETSOC-374025

... equations which describe reservoir performance. The problem can be avoided by employing mathematical techniques which calculate reservoir pressures with a high degree of precision while maintaining an accurate material balance computation. Some of the reservoir

**simulation****models**in current use do not have...
Abstract

Abstract The jupiter fluid problem can be defined as the computation of negative total compressibilities during mathematical simulation of the behavior of a hydrocarbon reservoir. This condition will cause diagonal dominance to be lost in the matrix of coefficients of the finite difference equations which describe reservoir performance. The problem can be avoided by employing mathematical techniques which calculate reservoir pressures with a high degree of precision while maintaining an accurate material balance computation. Some of the reservoir simulation models in current use do not have this capability, however, and a technique is presented for overcoming the jupiter fluid problem which occurs in these models. Introduction A jupiter fluid may be defined as a hypothetical gas-liquid system which has the peculiar property of expanding when subjected to increased pressure. Although no such fluid can actually exist in a hydrocarbon reservoir, the mathematical simulation of an oil reservoir can generate equations which imply a negative system compressibility at pressures slightly below the bubble point. This problem often arises in simulation models which do not maintain a high degree of accuracy in the pressure calculations. These inaccuracies are frequently due to the use of a computational technique which is inadequate for the specific problem being solved. The jupiter fluid problem can usually be avoided by reducing the time step size or by using a more accurate computational method for calculating pressure. However, the problem is still troublesome for users of some types of simulation models. The technique described below has been found to be effective in modifying these types of mathematical models so that stability of the calculation can be maintained at pressures near the bubble point. PRESSURE EQUATION Pressure distribution within a hydrocarbon reservoir may be described by the differential equation Equation (1) (Available in Full Paper). The transmissibilities and potentials for each phase p, and the flow term. q', are defined by Equation (2) (Available in Full Paper). Equation (3) (Available in Full Paper). and Equation (4) (Available in Full Paper). for two-dimensional simulation. In equation (3), P is. the oil phase pressure, p is average density, and P c is capillary pressure (which is positive between oil and gas and negative between oil and ater), The symbols z and g represent depth below a reference datum and gravitational acceleration, respectively. The subscript gf denotes free gas. Mathematical representation of reservoir performance is accomplished by writing equation (1) (with appropriate boundary conditions) in finite difference form for each mesh point in the reference grid. For example, the equations required for a two-dimensional simulation, implicit in pressure, may be represented by Equation (5) (Available in Full Paper). where the superscript denotes the n + 1 time level, and the subscripts i and j identify the x and y coordinate positions, respectively. If straight forward finite difference approximations are used (without consideration of the jupiter fluid problem) the matrix elements of equation (5) may be written Equation (6) (Available in Full Paper).

Proceedings Papers

Publisher: Petroleum Society of Canada

Paper presented at the Annual Technical Meeting, June 1–4, 1971

Paper Number: PETSOC-7110

... reservoir's future performance will be beneficial, one must determine whether or not these predictions are feasible. At this point one can begin to establish the degree of sophistication of the

**simulation****model**and the reservoir characterization necessary to accomplish the objectives. Here is where the...
Abstract

Abstract The purpose of reservoir simulation is to evaluate and analyze the future performance of a field under given operating conditions with the hope of being able to improve this. performance. Whilethe purpose of reservoir simulation has not changed since the beginning of the producing industry, the tools available for simulating a reservoir's performancehave increased significantly in sophistication. The combination of fast computers, improved numerical techniques, and a better understanding of the physics of fluid flow, has made it possible to develop extremely sophisticated reservoirsimulation models. However, the degree of sophistication which should be employed in redicting the future performance of a reservoir depends heavily upon the nature of the questions being asked regarding this performance and the dependence ofthis performance upon the reservoir rock and fluid description data. The advanced technology in reservoir simulation has been a valuable tool forthe reservoir engineer but its use is not without problems. One of the important problems created by the availability of such a wide range of sophistication is that of selecting the proper tool for the job and then finding an adequate reservoir description to fit the chosen model. An important consideration in solving this problem is to obtain a consistency between the degree of sophistication of the simulation, the reservoir characterization and the problem being analyzed. While there is no simple formula to follow to obtain such a consistency, this paper attempts to il1u5trate with specific field examples how this should be accomplished. Introduction Since the purpose of reservoir simulation is to predict the future reservoir performance under a variety of operating schemes, the utility of any simulationmodel depends on how well it can accomplish this objective. However, being able to pred1ct the future is an extremely broad concept and needs to be refined before anything, can be said about what degree of sophistication is need in either the model or the reservoir characterization. Therefore the first step in any type of reservoir simulation should be to decide what one would like to know about the future performance of a given reservoir and what can be done with this information if it is known. Once a problem has been clearly defined and it is evident that being ableto predict certain information about a reservoir's future performance will be beneficial, one must determine whether or not these predictions are feasible. At this point one can begin to establish the degree of sophistication of the simulation model and the reservoir characterization necessary to accomplish the objectives. Here is where the complex problem of model selection und reservoir characterization begins. The problem of model selection and reservoir characterization can only be solved by completely understanding the complex interaction between the problem being analyzed, the reservoir and fluid characterization, and the degree of sophistication of the reservoir simulation model. While a complete understanding of this interaction is beyond the scope of this paper we will attempt to consider some of the more important aspects of this problem.