Automated Geologically-Consistent History Matching of Facies Distribution and Reservoir Properties in Inter-Well Space by Adjoint Methods (Russian)

2017 ◽  
Author(s):  
E. S. Zakirov ◽  
O. V. Lyubimova ◽  
I. M. Indrupsky ◽  
D. P. Anikeev ◽  
E. Yu. Arkhipova
Keyword(s):  
History Matching ◽  
Reservoir Properties ◽  
Adjoint Methods ◽  
Facies Distribution

A Fast Gridding Method for Capturing Geological Complexity and Uncertainty

2021 ◽  
Author(s):  
Yifei Xu ◽  
Priyesh Srivastava ◽  
Xiao Ma ◽  
Karan Kaul ◽  
Hao Huang
Keyword(s):  
Efficient Method ◽  
Reservoir Simulation ◽  
History Matching ◽  
Mapping Method ◽  
Reservoir Properties ◽  
Structural Framework ◽  
Assisted History Matching ◽  
Grid Topology ◽  
Geological Complexity ◽  
Complex Channel

Abstract In this paper, we introduce an efficient method to generate reservoir simulation grids and modify the fault juxtaposition on the generated grids. Both processes are based on a mapping method to displace vertices of a grid to desired locations without changing the grid topology. In the gridding process, a grid that can capture stratigraphical complexity is first generated in an unfaulted space. The vertices of the grid are then displaced back to the original faulted space to become a reservoir simulation grid. The resulting reversely mapped grid has a mapping structure that allows fast and easy fault juxtaposition modification. This feature avoids the process of updating the structural framework and regenerating the reservoir properties, which may be time-consuming. To facilitate juxtaposition updates within an assisted history matching workflow, several parameterized fault throw adjustment methods are introduced. Grid examples are given for reservoirs with Y-faults, overturned bed, and complex channel-lobe systems.

Significance of The Sedimentology and Stratigraphy for Identification of Low Contras Low Resistivity zones in a Clastic Outcrop of The Upper Talang Akar Formation, Musi Banyuasin, Indonesia

2021 ◽  
Author(s):  
A. F. H. Surbakti
Keyword(s):  
Clay Minerals ◽  
Coarse Grained ◽  
The South ◽  
Reservoir Properties ◽  
Low Contrast ◽  
Facies Distribution ◽  
Low Resistivity ◽  
Different Sources

The Talang Akar Formation is one of the hydrocarbon-producing reservoirs of the South Sumatra Basin. This basin is filled from two different sources in the Eastern part and Western part paleo-high. The bottom Talang Akar consists of coarse-grained sandstone, and the upper part constrains intercalation of sandstone and shale, known as low resistivity low contrast zone (LRLC). The Talang Akar Formation from Air Batu and Sukomoro confers an excellent probability to observe and define LRLC zones over systematic approaches. This paper will provide an analogue of the LRLC reservoir zone by analyzing the relation between facies distribution and reservoir properties, including detailed shale structure. Facies distribution was obtained from the outcrop stratigraphic profile. The reservoir properties are identified by the Thomas Stieber plot and the petrographic section. Seven facies of Talang Akar Formation had been identified, which are: 1) planar cross-bedded sandstone (PCBS), 2) trough cross-bedded sandstone (TCBSS), 3) laminated sandstone (LSS), 4) heterolytic sandstone (HSS), 5) clay-rich sandstone (CSS), 6) mudstone (MS), 7) scour conglomeratic sandstone (SCSS). There are several types of shale distribution: structural shale, dispersed shale, and laminar shale. The laminar and dispersed shale consists of most of the reservoir and fills the pore. The clay structure deduces the disparity in the facies-porosity correlation. The finding of this study revealed that the LRLC zones are caused by lamination structures, thin intercalation layers, heterolytic and clay minerals.

Integrated reservoir characterization: Beyond tomography

Geophysics ◽  
1995 ◽  
Vol 60 (2) ◽  
pp. 354-364 ◽  
Author(s):  
Larry Lines ◽  
Henry Tan ◽  
Sven Treitel ◽  
John Beck ◽  
Richard Chambers ◽  
...  
Keyword(s):  
History Matching ◽  
Preferential Flow ◽  
Reservoir Characterization ◽  
Seismic Anisotropy ◽  
Flow Direction ◽  
Geophysical Methods ◽  
Reservoir Properties ◽  
Traveltime Tomography ◽  
Sonic Log ◽  
Primary Water

In 1992, there was a collaborative effort in reservoir geophysics involving Amoco, Conoco, Schlumberger, and Stanford University in an attempt to delineate variations in reservoir properties of the Grayburg unit in a West Texas [Formula: see text] pilot at North Cowden Field. Our objective was to go beyond traveltime tomography in characterizing reservoir heterogeneity and flow anisotropy. This effort involved a comprehensive set of measurements to do traveltime tomography, to image reflectors, to analyze channel waves for reservoir continuity, to study shear‐wave splitting for borehole stress‐pattern estimation, and to do seismic anisotropy analysis. All these studies were combined with 3-D surface seismic data and with sonic log interpretation. The results are to be validated in the future with cores and engineering data by history matching of primary, water, and [Formula: see text] injection performance. The implementation of these procedures should provide critical information on reservoir heterogeneities and preferential flow direction. Geophysical methods generally indicated a continuous reservoir zone between wells.

A History Matching Framework to Characterize Fracture Network and Reservoir Properties in Tight Oil

2019 ◽  
Vol 142 (4) ◽  
Author(s):  
Xu Shiqian ◽  
Li Yuyao ◽  
Zhao Yu ◽  
Wang Sen ◽  
Feng Qihong
Keyword(s):  
History Matching ◽  
Fracture Network ◽  
Future Application ◽  
Tight Oil ◽  
Oil Reservoir ◽  
Fracture Permeability ◽  
Reservoir Properties ◽  
Matrix Permeability ◽  
Tight Oil Reservoir ◽  
Better Than

Abstract Accurately characterizing hydraulic fracture network and tight oil reservoir properties can lay the foundation for the production forecast and development design. In this work, we proposed a history matching framework for tight oil. We first use the Hough transform method to characterize complex fracture network from microseismic data. Then, we put the fracture network into an embedded discrete fracture model (EDFM) to build a tight oil reservoir simulation model. After that, we further couple whale optimization algorithm (WOA) and EDFM to match the field production data. In this way, we can accurately estimate reservoir properties, including matrix permeability and porosity, as well as fracture permeability. We apply the framework to two-field applications in China. One is fractured vertical well in the Songliao Basin of Daqing oilfield. The other one is multi-stage fractured horizontal well in the Jimsar Sag of the Xinjiang oilfield. Results show that if we do not consider tight oil characteristics, the estimated fracture permeability, matrix permeability, and matrix porosity will underestimate 73%, 20%, and 47%, respectively. Because we apply WOA to history matching for the first time, we compare the performance of WOA with ensemble–smoother with multiple data–assimilation (ES-MDA). When we fit six parameters, ES-MDA performs better than WOA. However, when we fit three parameters, WOA performs better than ES-MDA. In addition, for engineering problem, WOA performs well on both convergence speed and stability. Therefore, WOA is recommended in the future application of history matching.

Estimation of the Location of the Boundary of a Petroleum Reservoir

1975 ◽  
Vol 15 (01) ◽  
pp. 19-38 ◽  
Author(s):  
Wen H. Chen ◽  
John H. Seinfeld
Keyword(s):  
History Matching ◽  
Single Phase ◽  
Steepest Descent ◽  
Petroleum Reservoir ◽  
Pressure Data ◽  
Reservoir Properties ◽  
Matching Problems ◽  
Reservoir Property ◽  
Boundary Location

Abstract This paper considers the problem of estimating the shape of a petroleum reservoir on the basis of pressure data from wells within the boundaries of pressure data from wells within the boundaries of the reservoir. It is assumed that the reservoir properties, such as permeability and porosity, are properties, such as permeability and porosity, are known but that the location of the boundary is unknown. Thus, this paper addresses a new class of history-matching problems in which the boundary position is the reservoir property to be estimated. position is the reservoir property to be estimated. The problem is formulated as an optimal-control problem (the location of the boundary being the problem (the location of the boundary being the control variable). Two iterative methods are derived for the determination of the boundary location that minimizes a functional, depending on the deviation between observed and predicted pressures at the wells. The steepest-descent pressures at the wells. The steepest-descent algorithm is illustrated in two sample problems:the estimation of the radius of a bounded circular reservoir with a centrally located well, andthe estimation of the shape of a two-dimensional, single-phase reservoir with a constant-pressure outer boundary. Introduction A problem of substantial economic importance is the determination of the size and shape of a reservoir. Seismic data serve to define early the probable area occupied by the reservoir; however, probable area occupied by the reservoir; however, a means of using initial well-pressure data to determine further the volume and shape of the reservoir would be valuable. On the basis of representing the pressure behavior in a single-phase bounded reservoir in terms of an eigenfunction expansion, Gavalas and Seinfeld have shown how the total pore volume of an arbitrarily shaped reservoir can be estimated from late transient pressure data at the completed wells. We consider pressure data at the completed wells. We consider here the related problem of the estimation of the shape (or the location of the boundary) of a reservoir from pressure data at an arbitrary number of wells. For reasons of economy, the time allowable for closing wells is limited. It is important, therefore, that any method developed for estimating the shape of a reservoir be applicable, in principle, from the time at which the wells are completed until the current time. Thus, the problem we consider here may be viewed as one in the general realm of history matching, but also one in which the boundary location is the property to be estimated rather than the reserved physical properties. The formulation in the present study assumes that everything is known about the reservoir except its boundary. In actual practice, the reverse is generally true. (By the time sufficient information is available regarding the spatial distribution of permeability and porosity, the boundaries may be fairly well known.) Nevertheless, relatively early in the life of a reservoir, when initial drillstem tests have served to identify an approximate distribution of properties, it may be of some importance to attempt to estimate the reservoir shape. Since knowledge of reservoir properties such as permeability and porosity is at properties such as permeability and porosity is at best a result of initial estimates from well testing, core data, etc., the assumption that these properties are known will, of course, lead only to an approximate reservoir boundary. As the physical properties are identified more accurately, the reservoir boundary can be more accurately estimated. It is the object of this paper to formulate in a general manner and develop and initially test computational algorithms for the class of history-matching problems in which the boundary is the unknown property.There are virtually no prior available results on the estimation of the location of the boundary of a region over which the dependent variable(s) is governed by partial differential equations. The method developed here, based on the variation of a functional on a variable region, is applicable to a system governed by a set of nonlinear partial differential equations with general boundary conditions. The derivation of necessary conditions for optimality and the development of two computational gradient algorithms for determination of the optimal boundary are presented in the Appendix. To illustrate the steepest-descent algorithm we present two computational examples using simulated reservoir data. SPEJ P. 19

History matching of facies distribution with the EnKF and level set parameterization

2010 ◽  
Vol 229 (20) ◽  
pp. 8011-8030 ◽  
Author(s):  
Haibin Chang ◽  
Dongxiao Zhang ◽  
Zhiming Lu
Keyword(s):  
Level Set ◽  
History Matching ◽  
Facies Distribution

Identifiability of Estimates of Two-Phase Reservoir Properties in History Matching

1984 ◽  
Vol 24 (06) ◽  
pp. 697-706 ◽  
Author(s):  
A.T. Watson ◽  
G.R. Gavalas ◽  
J.H. Seinfeld
Keyword(s):  
Pressure Drop ◽  
History Matching ◽  
Single Phase ◽  
Pressure Data ◽  
Relative Permeabilities ◽  
Unknown Parameters ◽  
Reservoir Properties ◽  
Matching Problem ◽  
Two Phase ◽  
Reservoir History Matching

Abstract Since the number of parameters to be estimated in a reservoir history match is potentially quite large, it is important to determine which parameters can be estimated with reasonable accuracy from the available data. This aspect can be called determining the identifiability of the parameters. The identifiability of porosity and absolute parameters. The identifiability of porosity and absolute and relative permeabilities on the basis of flow and pressure data in a two-phase (oil/water) reservoir is pressure data in a two-phase (oil/water) reservoir is considered. The question posed is: How accurately can one expect to estimate spatially variable porosity and absolute permeability and relative permeabilities given typical permeability and relative permeabilities given typical production and pressure data" To gain insight into this production and pressure data" To gain insight into this question, analytical solutions for pressure and saturation in a one-dimensional (1D) waterflood are used. The following, conclusions are obtained.Only the average value of the porosity can be determined on the basis of water/oil flow measurements.The permeability distribution can be determined from pressure drop data with an accuracy depending on the pressure drop data with an accuracy depending on the mobility ratio.Exponents in a power function representation of the relative permeabilities can he determined from WOR data alone but not nearly so accurately as when pressure drop and flow data are used simultaneously. Introduction The utility of reservoir simulation in predicting reservoir behavior is limited by the accuracy with which reservoir properties can be estimated. Because of the high costs properties can be estimated. Because of the high costs associated with coring analysis, reservoir engineers must rely, on history matching as a means of estimating reservoir properties. In this process a history match is carried out by choosing the reservoir properties as those that result in simulated well pressure and flow data that match as closely as possible those measured during production. In general, reservoir properties at each gridblock in the simulator represent the unknown values to be determined. Although there are efficient methods for estimating such a large number of unknowns, it has long been recognized from the results of single phase history matching exercises that many different sets of parameter values may yield a nearly identical match of observed and predicted pressures. The conventional single phase predicted pressures. The conventional single phase history matching problem is in fact a mathematically illposed problem, which explains its nonunique behavior. Such a situation is, in short, the result of the large number of unknowns to be estimated on the basis of the available data and the lack of sensitivity of the simulator solutions to the parameters. Because of this lack of sensitivity, the need to reduce the number of unknown Parameters or to introduce some additional constraints, such as "smoothness" of the estimated parameters, has been recognized. A problem as important as that of choosing which minimization method to employ in history matching is that of choosing, on the basis of the available well data. which properties actually should be estimated. This selection properties actually should be estimated. This selection depends on the relationship of the unknown parameters to the simulated well data. Ideally one would want to knowwhich parameters can be determined uniquely if the measurements were exact, andgiven the expected level of error in the measurements, how accurately we can expect to be able to estimate the parameters. The first question, that of establishing uniqueness of the estimated parameters, is notoriously difficult to answer, and for a parameters, is notoriously difficult to answer, and for a problem as complicated as reservoir history matching, problem as complicated as reservoir history matching, there are virtually no general results available that allow one to establish uniqueness for permeability or porosity. Thus, it is not possible in general to base our choice of which parameters to estimate on rigorous mathematical uniqueness results. In lieu of an answer to Question 1, the selection of parameters to be estimated can be based on Question 2, parameters to be estimated can be based on Question 2, which is amenable to theoretical analysis. If the expected errors in estimation of any of the parameters, or any linear combination of the parameters, are extremely large, then that parameter or set of parameters can be judged as not identifiable. In such a case, steps may be taken to reduce the number of unknown parameters. In summary, the reservoir history matching problem is a difficult parameter estimation problem, and understanding the relationship between the unknown parameters and the measured data is essential to obtaining meaningful estimates of the reservoir properties. Quantitative studies regarding the accuracy of estimates for single-phase history matching problems have been reported by Shah et al. and Dogru et al. Shah et al,. investigated the optimal level of zonation for use with 1D single-phase (oil) situations. SPEJ P. 697

scholarly journals Building of Turbiditic Gas Field Dynamic Model with a Simplified 3D Simulation Software

2018 ◽  
Vol 3 (3) ◽  
pp. 141
Author(s):  
Octria Adi Prasojo ◽  
Vladimir Alejandro Choque Flores ◽  
Matthieu Plantevin ◽  
Reza Syahputra
Keyword(s):  
Simulation Model ◽  
History Matching ◽  
Structural Model ◽  
Simulation Software ◽  
3D Simulation ◽  
Well Testing ◽  
Data Quality Control ◽  
Reservoir Properties ◽  
Gas Field ◽  
3D Simulation Model

This study provides a novel approach of building 3D simulation model with extremely shorter time needed using Rubis simulation software from Kappa Engineering. The study focused on X Field that is located in a turbiditic setting, mainly consisted of separated channel bodies filled with gas, located in a slope apron or passive continental margin of Mahakam Delta. Methods of the study is quite contradictive with common reservoir simulation where it includes data integration, data quality control, model geometry building, reservoir properties distribution, and is followed by wells definition to build the 3D simulation model. Afterward, the reliability of the structural model was checked by the volume calculation for each segment from GeoX model where all dynamic and static data used in the simulation were checked using history matching data derived from well-testing. In conclusion, simulation was run and X Field will be producing for 23 years with 3 years and 10 months plateau rate. Where the static and dynamic data are already provided, the simulation conducted here was very beneficial during the exploration phase of a gas field where the whole process of modeling and simulation could be done only for 3 to 6 months.

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