Reservoir Modeling and Numerical Simulation Controlled by Flow Units

2013 ◽  
Vol 690-693 ◽  
pp. 3190-3193
Author(s):  
Yong Chao Xue ◽  
Lin Song Cheng

Geological model controlled by sedimentary microfacies can not able to accurately reflect the actual seepage characteristic, How to apply the static flow units to the dynamic reservoir numerical simulation is the advancing edge of petroleum industry. The method of reservoir geological modeling controlled by flow units is proposed. Firstly, the 3D models of flow unit should be build, secondly, the 3D porosity and permeability model are established controlled by the model of flow unit, thirdly, the 3D fluids saturation model is calculated by Leverett J function based on porosity and permeability mode. Selecting different relative permeability curves according to different flow units in history matching. which realizes the dynamic (development geological) and static (reservoir engineering) combination. The oilfield examples shown that the velocity and precision of history matching can be significantly improved by using the method mentioned. Flow units were proposed by Hearn in 1984[. Study on flow units, which could not only deepen the understanding of reservoir geology, make reservoir evaluation more reasonable and reduce the heterogeneity impact on oil development, but also could be of great significance to improve oil field development effect especially for tertiary oil recovery[. Previous studies mainly focused on defining the concept of flow units, divide method of flow units and so on, but few studies on how to apply the study results of static flow units to the dynamic reservoir engineering and reservoir simulation[3-. Our research aimed at how to connect the static flow units and the dynamic reservoir simulation closely so as to achieve "dynamic and static combination".

Author(s):  
Margarita A. Smetkina ◽  
◽  
Oleg A. Melkishev ◽  
Maksim A. Prisyazhnyuk ◽  
◽  
...  

Reservoir simulation models are used to design oil field developments, estimate efficiency of geological and engineering operations and perform prediction calculations of long-term development performances. A method has been developed to adjust the permeability cube values during reservoir model history-matching subject to the corederived dependence between rock petrophysical properties. The method was implemented using an example of the Bobrikovian formation (terrigenous reservoir) deposit of a field in the Solikamskian depression. A statistical analysis of the Bobrikovian formation porosity and permeability properties was conducted following the well logging results interpretation and reservoir modelling data. We analysed differences between the initial permeability obtained after upscaling the geological model and permeability obtained after the reservoir model history-matching. The analysis revealed divergences between the statistical characteristics of the permeability values based on the well logging data interpretation and the reservoir model, as well as substantial differences between the adjusted and initial permeability cubes. It was established that the initial permeability was significantly modified by manual adjustments in the process of history-matching. Extreme permeability values were defined and corrected based on the core-derived petrophysical dependence KPR = f(KP) , subject to ranges of porosity and permeability ratios. By using the modified permeability cube, calculations were performed to reproduce the formation production history. According to the calculation results, we achieved convergence with the actual data, while deviations were in line with the accuracy requirements to the model history-matching. Thus, this method of the permeability cube adjustment following the manual history-matching will save from the gross overestimation or underestimation of permeability in reservoir model cells.


2021 ◽  
Author(s):  
Libing Fu ◽  
Jun Ni ◽  
Yuming Liu ◽  
Xuanran Li ◽  
Anzhu Xu

Abstract The Zhetybay Field is located in the South Mangyshlak Sub-basin, a delta front sedimentary reservoir onshore western Kazakhstan. It was discovered in 1961 and first produced by waterflooding in 1967. After more than 50 years of waterflooding development, the reservoirs are generally in the mid-to-high waterflooded stage and oil-water distribution becomes complicated and chaotic. It is very difficult to handle and identify so much logging data by hand since the oilfield has the characteristics of high-density well pattern and contains rich logging information with more than 2000 wells. The wave clustering method is used to divide the sedimentary rhythm of the logging curve. Sedimentary microfacies manifested as a regression sequence, with four types of composite sand bodies including the composite estuary bar and distributary channel combination, the estuary bar connected to the dam edge and the distributing channel combination, the isolated estuary bar and distributing channel combination, and the isolated beach sand. In order to distinguish the flow units, the artificial intelligence algorithm-support vector machine (SVM) method is established by learning the non-linear relationship between flow unit categories and parameters based on developing flow index and reservoir quality factor, summarizing permeability logarithm and porosity degree parameters in the sedimentary facies, and analyzing the production dynamic. The flow units in Zhetybay oilfield were classified into 4 types: A, B1, B2 and B3, and the latter three are the main types. Type A is distributed in the river, type B1 is distributed in the main body of the dam, type B2 is mainly distributed in the main body of the dam, and some of B2 is distributed in the dam edge, and B3 is located in the dam edge, sheet sand and beach sand. The results show that the accuracy of flow unit division by support vector machines reaches 91.1%, which clarifies the distribution law of flow units for oilfield development. This study is one of the significant keys for locating new wells and optimizing the workovers to increase recoverable reserves. It provides an effective guidance for efficient waterflooding in this oilfield.


2002 ◽  
Vol 5 (02) ◽  
pp. 135-145 ◽  
Author(s):  
G.R. King ◽  
W. David ◽  
T. Tokar ◽  
W. Pape ◽  
S.K. Newton ◽  
...  

Summary This paper discusses the integration of dynamic reservoir data at the flow-unit scale into the reservoir management and reservoir simulation efforts of the Takula field. The Takula field is currently the most prolific oil field in the Republic of Angola. Introduction The Takula field is the largest producing oil field in the Republic of Angola in terms of cumulative oil production. It is situated in the Block 0 Concession of the Angolan province of Cabinda. It is located approximately 25 miles offshore in water depths ranging from 170 to 215 ft. The field consists of seven stacked, Cretaceous reservoirs. The principal oil-bearing horizon is the Upper Vermelha reservoir. This paper discusses the data acquisition and integration for this reservoir only. The reservoir was discovered in January 1980 with Well 57- 02X. Primary production from the reservoir began in December 1982. The reservoir was placed on a peripheral waterflood in December 1990. Currently, the Upper Vermelha reservoir accounts for approximately 75% of the production from the field. Sound management of mature waterfloods has been identified as a key to maximizing the ultimate recovery and delivering the highest value from the Block 0 Asset.1 Therefore, the objective of the simulation effort was to develop a tool for strategic and dayto- day reservoir management with the intent of managing and optimizing production on a flow-unit basis. Typical day-to-day management activities include designing workovers, identifying new well locations, optimizing injection well profiles, and optimizing sweep efficiencies. To perform these activities, decisions must be made at the scale of the individual flow units. In general, fine-grid geostatistical models are developed from static data, such as openhole log data and core data. Recent developments in reservoir characterization have allowed for the incorporation of some dynamic data, such as pressure-transient data and 4D seismic data, into the geostatistical models. Unfortunately, pressure-transient data are acquired at a test-interval scale (there are typically 3 to 4 test intervals per well, depending on the ability to isolate different zones mechanically in the wellbore), while seismic data are acquired at the reservoir scale. The reservoir surveillance program in the Takula field routinely acquires data at the flow-unit scale. These data include openhole log and wireline pressure data from newly drilled wells and casedhole log and production log (PLT) data from producing/injecting wells. Because of the time-lapse nature of cased-hole log and PLT data, they represent dynamic reservoir data at the flow-unit scale. To achieve the objectives of the modeling effort and optimize production on a flow-unit basis, these dynamic data must be incorporated into the simulation model at the appropriate scale. When these data are incorporated into a simulation model, it is typically done during the history match. There are, however, instances when these data are incorporated during other phases of the study. The objective of this paper, therefore, is to discuss the methods used to integrate the dynamic reservoir data acquired at the flow-unit scale into the Upper Vermelha reservoir simulation model. Reservoir Geology The geology of the Takula field is described in detail in Ref. 2. The aspects of the reservoir geology that are pertinent to this paper are elaborated in this section. Reservoir Stratigraphy. The Takula field consists of seven stacked reservoirs. The principal oil-bearing horizon is the Upper Vermelha reservoir. This reservoir contains an undersaturated, 33°API crude oil. For reservoir management purposes, 36 marker surfaces have been identified in the reservoir. Flow units were then identified as reservoir units separated by areally pervasive vertical flow barriers (nonreservoir rock). This resulted in the identification of 20 flow units. The thickness of these flow units ranges from 5 to 15 ft. Reservoir Structure. The reservoir structure is a faulted anticline that is interpreted to be the result of regional salt tectonics. Closure to the reservoir is provided by faults on the southwestern and northern flanks of the structure and by an oil/water contact (OWC) on the eastern, western, and southern flanks of the structure. A structure map of the reservoir is presented in Fig. 1. Data Acquisition in the Takula Field Openhole Log Program. Most original development wells were logged with a basic log suite of resistivity/gamma ray and density/ neutron logs. In addition, the vertical wells drilled from each well jacket were logged with a sonic log and, occasionally, velocity surveys. All wells drilled after 1993 were logged with long spacing sonic and spectral gamma ray logs. In many wells drilled after December 1997, carbon/oxygen (C/O) logs have been run in open hole to distinguish between formation and injected water.3 A few recent wells have been logged with nuclear magnetic resonance (NMR) logs. The NMR log data, when integrated with data from other logs, have been of value in distinguishing free water from bound water, formation water from injection water, and reservoir rock from nonreservoir rock.


2012 ◽  
Vol 518-523 ◽  
pp. 4376-4379
Author(s):  
Bao Yi Jiang ◽  
Zhi Ping Li

With the increase in computational capability, numerical reservoir simulation has become an essential tool for reservoir engineering. To minimize the objective function involved in the history matching procedure, we need to apply the optimization algorithms. This paper is based on the optimization algorithms used in automatic history matching.


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.


2013 ◽  
Vol 340 ◽  
pp. 867-870
Author(s):  
Quan Hou Li ◽  
Chun Yu Zhang ◽  
Yuan Feng Zhang

The research of visualization will be carried out in the aspects of parameters distribution, dynamic history matching and geological models and numerical simulation of reservoirs respectively. In terms of the research on reservoir parameters, we can improve the accuracy of the prediction through comparing, analyzing and modeling the data of old wells and the secondary explained data. As to dynamic history matching and numerical simulation, by combination of dynamic and statistic methods, we can modify the deficiency of the traditional method. As for geological modeling, we can utilize the characters of logging responses and current mathematical theory to establish modeling. Only by combination of the dynamic and static methods, we can achieve actual visualization of oil-field development.


Author(s):  
Sufia Khatoon ◽  
Jyoti Phirani ◽  
Supreet Singh Bahga

Abstract In reservoir simulations, model parameters such as porosity and permeability are often uncertain and therefore better estimates of these parameters are obtained by matching the simulation predictions with the production history. Bayesian inference provides a convenient way of estimating parameters of a mathematical model, starting from a probable range of parameter values and knowing the production history. Bayesian inference techniques for history matching require computationally expensive Monte Carlo simulations, which limit their use in petroleum reservoir engineering. To overcome this limitation, we perform accelerated Bayesian inference based history matching by employing polynomial chaos (PC) expansions to represent random variables and stochastic processes. As a substitute to computationally expensive Monte Carlo simulations, we use a stochastic technique based on PC expansions for propagation of uncertainty from model parameters to model predictions. The PC expansions of the stochastic variables are obtained using relatively few deterministic simulations, which are then used to calculate the probability density of the model predictions. These results are used along with the measured data to obtain a better estimate (posterior distribution) of the model parameters using the Bayes rule. We demonstrate this method for history matching using an example case of SPE1CASE2 problem of SPEs Comparative Solution Projects. We estimate the porosity and permeability of the reservoir from limited and noisy production data.


2011 ◽  
Vol 271-273 ◽  
pp. 275-280
Author(s):  
Yan Ming An ◽  
Lian Qin He ◽  
Guo Zhong Zhao ◽  
Ming Fei An ◽  
Yue Zhen

On the basic of studying the present technical situation of home and abroad about computer aided History Matching of the reservoir numerical simulation and digesting, absorbing technology, we studied and optimized highly effective algorithm fit for the aided History Matching. At the same time, we designed the software interface and frame and function module, developed the independent aided History Matching software named CAPHE, thus formed aided history methods suitable for our independent reservoir simulator-PBRS. By using of the software in practical some oil simulation blocks, CAPHE can significantly increase History Matching efficiency in the reservoir simulation.


2011 ◽  
Vol 14 (2) ◽  
pp. 25-37
Author(s):  
Hung Quoc Nguyen ◽  
Lan Cao Mai

Geological model is normally built with huge number of grid cells (106 – 107 cells) in order to model in details the geological heterogeneity of an oil field. Although increasingly developed nowadays, modern computers would be faced with substantial problems size in reservoir simulation. Upscaling the geological model before running numerical simulation, therefore, is greatly important to reduce the simulation model size while retaining the geological heterogeneity of the oil field. This paper presents an overview of fundamental background on upscaling methods and reports research results in applying various upscaling methods as well as suggests the most suitable methods for Te giac trang oilfield.


GeoArabia ◽  
2010 ◽  
Vol 15 (4) ◽  
pp. 49-76
Author(s):  
Basim Al-Qayim ◽  
Fuad M. Qadir ◽  
Fawzi Albeyati

ABSTRACT The Khabbaz Field in northern Iraq produces oil and gas from the Albian Upper Qamchuqa Formation, which corresponds to the Mauddud Formation of southern and central Iraq and the Arabian Gulf. The Formation is layered into Units A, B and C, of which Unit A is the main reservoir zone characterized by correlatable flow units and barriers/baffles. Units B and C generally have lower overall reservoir quality compared to Unit A. A detailed examination of cuttings, cores, and wireline logs from ten wells in the field revealed an important link between sedimentary facies, dolomitization, and heterogeneity of reservoir characteristics. The wide range of dolomite fabrics include microcrystalline, planar-e, planar-s, planar-p, non-planar as well as saddle and cement types. These fabrics imply successive phases of dolomitization, which profoundly influence the enhancement of reservoir character. Intercrystalline, micromoldic, and microvuggy porosity are the most influential byproducts of this dolomitization. Fracturing and stylolitization, in addition to the uniform network of intercrystalline pore systems, especially of the fine- to medium-crystalline dolomite, effectively contributed to the collective porosity and permeability of the reservoir.


Sign in / Sign up

Export Citation Format

Share Document