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Constraining Method of Stochastic Modeling for Fluvial Petroleum Reservoir Controlled by Depositional Facies Using Wells and Seismic Data
EARTH SCIENCE FRONTIERS Volume 15, Issue 4, July 2008 Online English edition of the Chinese language journal Cite this article as: Earth Science Frontiers, 2008, 15(4): 33–41.
RESEARCH PAPER
Constraining Method of Stochastic Modeling for Fluvial Petroleum Reservoir Controlled by Depositional Facies Using Wells and Seismic Data YU Xinghe1, , LI Shengli1, ZHAO Shu2, CHEN Jianyang1, HOU Guowei3 1 School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, China 2 Development Research Institute of North China Branch, SINOPEC, Zhengzhou 450006, China 3 CNOOC Research Center, Beijing 100027, China
Abstract: Most of the conventional studies of depositional facies of fluvial petroleum reservoir were only based on well data. Firstly, the depositional microfacies in wells were distinguished, then the sections of depositional microfacies from well to well were correlated, and finally, the planar map of depositional facies was set up combined with the contours of sedimentary parameter. This method of predicting sandstone among wells may make big errors. However, the correctness of the distribution map of depositional facies directly affects the result of stochastic modeling controlled by facies and of petroleum exploration. Therefore, a new mapping method of depositional facies integrating well data with seismic data has been put forward. The stochastic modeling controlled by facies based on this mapping method has been constrained step by step in a hierarchy. At the same time, three fundamental constraining conditions controlled by facies have been proposed, those are as follows: firstly, to ensure “the order of facies” in stochastic modeling being consistent with geological rule; secondly, to ensure the statistic probability of each microfacies in the realization of stochastic modeling being consistent with the statistic probability obtained from the data of microfacies in each well dispersed into 3D grid; thirdly, to ensure the variogram of each microfacies in 3D being consistent with the quantitative geological knowledge database. In summary, the modeling constrained by facies is guided by depositional formation and genetic evolution; the methodology of the modeling is the use of multi-parameter coordination and a hierarchy of constraints; the results of the modeling are controlled by the planar distribution and vertical evolution of river channels to ensure the results approximate to the reality of underground geology. Key Words: fluvial facies; petroleum reservoir; integrating well data with seismic data; stochastic modeling
1
Introduction
The article considered the sedimentary reservoir of Permian fluvial system at the Daniudi gas field of Erdos basin in North China as the example. First, the microfacies distributions in plane are studied by the quantitive method integrated well with seismic data. Then, we present a stochastic modeling for the characteristics of fluvial petroleum reservoirs constrained by facies to yield results coinciding with real geological setting. The facies acting as the constraint are based on the known geological data and include depositional microfacies such as an extension direction in plane and a ratio of width to
thickness of channel[1,2]. The authors put forward a strategy of the reservoir modeling controlled by facies as follows: scale differences of reservoir controlled by hierarchy for multi-levels in vertical; for same level, plane zone differences of reservoir managed with nested structure. The following three aspects exert the constraints: combining with the order of facies, the probability correlation, and the quantitative geological knowledge database or variogram will characterize heterogeneities of fluvial reservoir. The modeling constrained by facies is characterized by depositional formation and genetic evolution as guidance of the modeling, to obtain the results using the coordination of multi-parameter and
Received date: 01-Feb-2008; Accepted date: 10-Mar-2008.
Corresponding author. E-mail: [email protected] Foundation item: Supported by the Specialized Research Fund for the Doctoral Program of China Higher Education(No. 20050491001). Copyright © 2008, China University of Geosciences (Beijing) and Peking University, Published by Elsevier B.V. All rights reserved.
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
constraint by hierarchy, as well as horizontal distribution and vertical evolvement of channel as constraint boundary. The geological and drilling data in the Daniudi gas field of North China are used to check and optimize the method. The results show that this optimized modeling can indicate heterogeneity and connectivity distribution of subsurface fluvial reservoirs. The predicting efficiency is more than 85% that has been confirmed by new wells in Daniudi gas field.
2
Regional geology
Daniudi gas field is located in the border areas of Yulin City, Shanxi Province and Yijinhuoluo, Wushenqi County, Inner Mongolia. In the division of geological structure, it belongs to the east section of the northern part in Yishan slope of Erdos basin, northwest China. The studied stratum are the main layers of producing gas in lower Permian, which are He 2 and He 3 members of lower Shihezi Formation. Lower Shihezi Formation is mainly sandstone interbedded few mudstone, which is composed of gray-green, gray, grayish-yellow massive coarse to middle sandstone with gravel bearing; fine sandstone interbedded purple-brown, grayish-green and brown shale; silt-shale and a few carbonaceous shale. Coal seam can be occasionally found. The thickness of lower Shihezi Formation generally ranges form 100 to 150 m; the thickest is 179.4 m and the thinnest is 34 m. The whole distributary features of this formation are: coarse and thick in north, fine and thin in south. From bottom to top, lower Shihezi Formation can be divided into three subsequences with fining upward. They are He 1, He 2, and He 3 members in order. The bottom sandstone in each subsequence usually contains gravel. The depositional facies of He 2 and He 3 members both belong to fluvial system. He 2 and He 3 members can be further classified into five and four layers, respectively, from bottom to top, namely, H 12 , H 22 , H 32 , H 42 , 5
1
2
3
4
H 2 , and H 3 , H 3 , H 3 , H 3 .
3
Analysis and use of seismic attribute
To ensure the use of extracted seismic attributes in each layer to be more accurate, through synthetical record, we have taken well top as controlling condition and seismic interface as trend. The division of layer on seismic section in well site has been controlled by the GR curve. Each layer is identified by the trend of reflection wave-set from top to bottom. Then, each layer in the two seismic projects has been tracked, which is used to control the extraction of seismic attribute. Using these technologies, various seismic attributes have been extracted such as RMS amplitude, the maximum amplitude, the minimum amplitude, frequency width, frequency steep, and the average instantaneous frequency. Through comparing the sand thickness and the seismic attributes in well site, and matching the two data, the RMS amplitude attribute has finally been
adopted to fit the math relationship of both to analyze the sand thickness distribution of each layer in the plane. The precondition of these fitted math relationships or models between well and seismic is that the correlation coefficient (R2) should be more than 0.65; otherwise, the models cannot be applied because their reliabilities are low. The study area has two blocks of 3-D seismic data, which are respectively 200 km2 in the north block and 100 km2 in the south. Hence, to ensure the consistency with the track and demarcation of each layer in both blocks, the correlation between both has been done in detail such that the seismic attributes in each layer are only extracted respectively and the relationship between the sandstone thickness in well and the seismic RMS amplitude has been fitted respectively in both blocks. For the study area, the data of seismic attributes in each layer being extracted is enormous. The original data without screen can be more than 0.7 million. To predict the distribution of sandbodies and to make the map of the depositional facies using integrated well and seismic data, the extracted data of seismic attributes must be screened to meet the need of prediction among wells. During this study, most of the distance among wells in the study area was usually from 1 to 2 km or more than 2 km. One third of spacing well being taken as the efficacious value was from 650 to 700 m around, i.e., on the basis of this view that to predict three points between every two wells is more reliable. The widths of channel being estimated range from 800 to 1000 m, on the basis of the ratio of width to thickness of fluvial system in the quantitive geological knowledge database. Thus, under the circumstances of known well site, the grid node of 600 × 600 m can completely arrive at the requirements of mapping depositional facies in plane[3–5]. Since the survey line of 3-D seismic data in both blocks is 20 m × 20 m, the spacing distance of grid data after screening according to In-line and X-line spacing 30 lines is 600 m. The whole controlling point is 994 along with the actual data of known well site (Fig. 1). In the actual mapping process, RMS in each node has been transformed into the data of sandstone thickness according to the fitted formulae between well and seismic in both blocks. The sieved data points can be used as “simulating virtual well point” or namely “controlling point” and addition to the actual data of known well site to form new grid data. It not only guarantees the precision of predication among wells, but also basically guarantees not to leak the distribution of fluvial sandbodies in spatiality. In this way, the node spacing of this grid is actually able to achieve such basic requirements of interpolation among wells for deterministic modeling[6].
4
Geological study on fluvial distribution
As well spacing is larger in the study area, to scientifically and rationally control the microfacies changes of fluvial system in plane, the distribution of sandstone thickness among wells has been predicted on the basis of a detailed analysis of
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
Fig. 1 Grid node plus well site in two 3-D seismic projects in the study area Total node is 994.
microfacies in well and combined with RMS amplitude attribute. The existence of sandbodies among wells has been carried out and the distribution of channel sand has been controlled. Having analyzed correlation between the sand thickness in actual well site and the RMS amplitude attribute extracted, the best fit formula of each layer is set up to predict the sand 1 thickness (Table). Taking H 2 layer as an example and using the data of sandstone thickness in new grid node (994 nodes or controlling point as above) after extracting along with known well site, the contours of initial sandstone thickness have been .
mapped on the basis of only the Kriging interpolation method of deterministic modeling (Fig. 2). By integrating regional background and depositional micro-facies in each well, and having modified the contours of initial sand thickness, the Table Formula of fit relationships between RMS and sandstone 1 thickness of two 3-D projects in H 2 layer of study area Layer 1
H2
3-D Project
Formula of fit relationship
(km2)
R2
200
y
0.0004 x 2 0.2196 x 12.584
0.6703
100
y 1E 0.8 x 2 0.0002 x 3.3681
0.9898
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
Fig. 2
1
Initial contours of sandstone thickness in H 2 layer depending on Kriging interpolation method Thickness in north is more than one in south.
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
Fig. 3
1
Modified contours of sandstone thickness in H 2 layer depending on the geological idea of fluvial system
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
the probability of these two type sandbodies to constrain the whole modeling process. Therefore, the constraint method of 2D trends probability has been selected for the stochastic modeling of microfacies in this study[8–11]. 5.1
Probability statistics of microfacies
The variability of microfacies in the modeling belongs to discrete variable state. The main microfacies on the basis of geological study include flooding mud, channel, overbank fine, point bar, channel bar, crevasse splay, and natural levee. There are several micro-facies that single microfacies can only account for a low proportion and it is not favorable to analyze the structure of variogram for each microfacies. Thus, channel and overbank fine have been combined to generalized channel. Analyses on the map of microfacies distribution (Fig. 4) and the probability statistics of microfacies in all wells (Figs. 5 and 6) display that the reservoir sandstone being developed in the study area mostly occur in channel bar and point bar in the channel. The area of point bar and channel bar accounts for 30.53% of He 2 and He 3 member; if not considering the flood plain microfacies, they can account for 64.59% of the total area. The probability of natural levee is secondary, but the probability of crevasse splay is minimum, which is less than 2.03%. These statistic data and geological viewpoints will be used to guide in optimizing the multiple realizations of stochastic modeling. When model realizations were optimized, the principle should be to stress considering the fitting degree between statistics probability of each realization and original data about point bar and channel bar microfacies. 1
Fig. 4 Distribution of depositional microfacies in H 2 layer depending on different data of sandstone thickness and genetic feature
ultimate contour map is obtained (Fig. 3) and the plane map of depositional facies is drawn (Fig. 4). On the basis of the results, the plane distribution and the vertical evolutive characteristics of depositional facies in each layer have been analyzed. The distribution of fluvial system was obvious that braided rivers were located in north of the study area and the meandering 1 river in south with H 2 layer was deposited.
5 Constraining method on reservoir stochastic modeling by controlled facies According to the field geological research of outcrops around the study area and combining with the analysis of depositional microfacies in wells, we found that the favorable reservoir sand bodies in the study area are channel bar or batture in braided river and point bar in meandering river[7]. In view of petroleum exploration, we have put forward a new thought of exploration to find channel bar in braided river and to determine point bar in meandering river. To represent the feature of stochastic modeling for fluvial reservoir, it is necessary to focus on using
Fig. 5 Single statistic probability of each microfacies for each layer of He 2 and He 3 members and their vertical changes in Daniudi gas field
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
microfacies, and have obtained their relational expressions of width and thickness. According to the sedimentary thickness of different microfacies measured by well, the distribution range of every microfacies can be calculated using their relational formula. The results have been calculated to be the channel bar width of braided river ranges from 890 to 1100 m, and the point bar width of meandering river from 800 to 980 m. These parameters have been selected to use as quantitative controlling data in the stochastic modeling process so as to further determine the ranges of sandstone extension and petrophysical change of each microfacies in lateral.
6
Fig. 6
Cumulative statistic probability of each microfacies for all layers of He 2 and He 3 members in Daniudi gas field
0: Flood Mud; 1: Channel; 2: Channel Bar; 3: Point Bar; 4: Crevasse Splay; 5: Natural Levee
5.2 Order of facies The sedimentary environment in the study area is a transitional region from braided river to meandering river, and the kind of microfacies is varied and its variation of channel in lateral is comparative frequently. Therefore, it is especially important to study the relationships of contact and transition among every microfacies. This interrelationship should be fully considered and used in the process of stochastic modeling[12]. The concrete method is as follows: Firstly, the strike directions and occurrence of major channel in the different position of the study area have been used to approximately control the microfacies distribution in plane. Then, the relationships of facies order or sequence (Fig. 7) have been applied to constrain modeling, and at the same time, the divisional data of microfacies in each well have been taken as the discrete variable to input software. Finally, these data have been dispersed into every node of three-dimensional grid to control microfacies change in vertical. 5.3
Model checking
Integrating study thought and technology as above, the algorithm of sequential indicator simulating has been applied to complete stochastic modeling controlled by facies for fluvial reservoir in the goal stratum of this study and to yield three realizations of the stochastic model. Through counting the probability of each microfacies in every realization and correlating with the original probability in each well, the optimal model can be selected according to the matching and fitting extent of every realization. Since the well numbers in the study area are less, for this reason, if the matching degree between original probability and realization can arrive in 75%–90%, it is seen that the predicting accuracy of stochastic modeling is very high. The result predicated that it could reflect truly the distributary characteristics of subsurface geological body. If the matching degree is more than 90%, it can be considered to be completely the result of deterministic modeling, and cannot act as the effect of stochastic prediction. If the matching degree is less than 75%, it can be considered that the accuracy is very less and its reliability is not enough. Through correlation between the probability of each microfacies in three realizations of the model and in each well, the matching error of realization one ranges from 15% to 20%, realization two about 35%, and realization three from 35% to38%. The fitting degree of realization one is the closest and matches best with the original geologic model; it shows that
Quantitative geological knowledge database
In the study area, as there are more changes in the thickness of each layer, the distribution of difficult microfacies were apparently controlled by the ratio of width to thickness in each microfacies. The extensional range of each microfacies sandbodies and the variation range of petrophysical property can be determined semi-quantitatively by the thickness of genetic sandstone, while their distributions have been predicated. In the study area, we have emphasized to perform statistics of the ratio of width to thickness for point bar and channel bar that occurred abundantly, fit the relationships respectively in
Fig. 7
Vertical order of microfacies in braided and meandering river of fluvial system
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
this model can reflect very well the role of stochastic prediction. The microfacies’ model of realization one (Fig. 8) and the distributary map of sandstone thickness (Fig. 9) have been selected to take as the final results that have been submit to exploring geologists. By having made the practice of drilling for two years and more than 100 wells, the successful ratio of these predicting models is over 85%.
7
Conclusions
These micro-facies of channel bar in braided river and point bar in meandering were the major sand bodies with gas bearing in the study stratum. To predict considerably better the developing position of favorable sandbodies and to avoid the traditional thinking method that the mapping channel depends on sandstones in well, the new thought is that finding sand bodies depends on the distribution of fluvial system. The plane distributions of fluvial reservoir have been studied in
Fig. 9
Optimized predicting model of sandstone thickness 1
in H 2 layer
detail using this thought integrating with well and seismic. These results have been considered as conditions to constrain the process of stochastic modeling and to obtain better effect. According to combining with the methods of collaborative information, deterministic and stochastic modeling, the scientific thought of three steps constraining the stochastic modeling controlled by facies for fluvial reservoir is as follows: the order of facies, the probability of microfacies, and the quantitative geological knowledge database have been used to constrain modeling step by step in hierarchy. To correlate and optimize multiple realizations of stochastic modeling, the final model has been selected that can reflect really the distribution of subsurface geological body.
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Fig. 8 Optimized model of microfacies in H 2 controlled by facies
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and shale modeling of a sandstone rich fluvial reservoir: upper
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Stratford Formation, Stratford field, Northern North Sea. AAPG, 1993, 77(6): 1016–1040. [4]
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component simulation. Acta Petrolei Sinica, 2003, 24(1): 53–57. [11] Wu S H, Zhang Y W, Li S J, et al. Geological constraint
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28(7): 857–880. Clastic Reservoir. Beijing: Geological Publishing House, 1996: [6]
Publisher, 1994: 1–21. [9]
2001, 25(1): 55–58. [12] Yu X H, Ma X X, Mu L X, et al. Geological Pattern and
Gotway C A, Rutherford B M. Stochastic simulation for
Hierarchical Boundary Analysis of Braided River Reservoir.
imaging spatial uncertainty: comparison and evaluation of
Beijing: Petroleum Industry Press, 2004: 1–207.
RESEARCH PAPER
Constraining Method of Stochastic Modeling for Fluvial Petroleum Reservoir Controlled by Depositional Facies Using Wells and Seismic Data YU Xinghe1, , LI Shengli1, ZHAO Shu2, CHEN Jianyang1, HOU Guowei3 1 School of Energy Resources, China University of Geosciences (Beijing), Beijing 100083, China 2 Development Research Institute of North China Branch, SINOPEC, Zhengzhou 450006, China 3 CNOOC Research Center, Beijing 100027, China
Abstract: Most of the conventional studies of depositional facies of fluvial petroleum reservoir were only based on well data. Firstly, the depositional microfacies in wells were distinguished, then the sections of depositional microfacies from well to well were correlated, and finally, the planar map of depositional facies was set up combined with the contours of sedimentary parameter. This method of predicting sandstone among wells may make big errors. However, the correctness of the distribution map of depositional facies directly affects the result of stochastic modeling controlled by facies and of petroleum exploration. Therefore, a new mapping method of depositional facies integrating well data with seismic data has been put forward. The stochastic modeling controlled by facies based on this mapping method has been constrained step by step in a hierarchy. At the same time, three fundamental constraining conditions controlled by facies have been proposed, those are as follows: firstly, to ensure “the order of facies” in stochastic modeling being consistent with geological rule; secondly, to ensure the statistic probability of each microfacies in the realization of stochastic modeling being consistent with the statistic probability obtained from the data of microfacies in each well dispersed into 3D grid; thirdly, to ensure the variogram of each microfacies in 3D being consistent with the quantitative geological knowledge database. In summary, the modeling constrained by facies is guided by depositional formation and genetic evolution; the methodology of the modeling is the use of multi-parameter coordination and a hierarchy of constraints; the results of the modeling are controlled by the planar distribution and vertical evolution of river channels to ensure the results approximate to the reality of underground geology. Key Words: fluvial facies; petroleum reservoir; integrating well data with seismic data; stochastic modeling
1
Introduction
The article considered the sedimentary reservoir of Permian fluvial system at the Daniudi gas field of Erdos basin in North China as the example. First, the microfacies distributions in plane are studied by the quantitive method integrated well with seismic data. Then, we present a stochastic modeling for the characteristics of fluvial petroleum reservoirs constrained by facies to yield results coinciding with real geological setting. The facies acting as the constraint are based on the known geological data and include depositional microfacies such as an extension direction in plane and a ratio of width to
thickness of channel[1,2]. The authors put forward a strategy of the reservoir modeling controlled by facies as follows: scale differences of reservoir controlled by hierarchy for multi-levels in vertical; for same level, plane zone differences of reservoir managed with nested structure. The following three aspects exert the constraints: combining with the order of facies, the probability correlation, and the quantitative geological knowledge database or variogram will characterize heterogeneities of fluvial reservoir. The modeling constrained by facies is characterized by depositional formation and genetic evolution as guidance of the modeling, to obtain the results using the coordination of multi-parameter and
Received date: 01-Feb-2008; Accepted date: 10-Mar-2008.
Corresponding author. E-mail: [email protected] Foundation item: Supported by the Specialized Research Fund for the Doctoral Program of China Higher Education(No. 20050491001). Copyright © 2008, China University of Geosciences (Beijing) and Peking University, Published by Elsevier B.V. All rights reserved.
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
constraint by hierarchy, as well as horizontal distribution and vertical evolvement of channel as constraint boundary. The geological and drilling data in the Daniudi gas field of North China are used to check and optimize the method. The results show that this optimized modeling can indicate heterogeneity and connectivity distribution of subsurface fluvial reservoirs. The predicting efficiency is more than 85% that has been confirmed by new wells in Daniudi gas field.
2
Regional geology
Daniudi gas field is located in the border areas of Yulin City, Shanxi Province and Yijinhuoluo, Wushenqi County, Inner Mongolia. In the division of geological structure, it belongs to the east section of the northern part in Yishan slope of Erdos basin, northwest China. The studied stratum are the main layers of producing gas in lower Permian, which are He 2 and He 3 members of lower Shihezi Formation. Lower Shihezi Formation is mainly sandstone interbedded few mudstone, which is composed of gray-green, gray, grayish-yellow massive coarse to middle sandstone with gravel bearing; fine sandstone interbedded purple-brown, grayish-green and brown shale; silt-shale and a few carbonaceous shale. Coal seam can be occasionally found. The thickness of lower Shihezi Formation generally ranges form 100 to 150 m; the thickest is 179.4 m and the thinnest is 34 m. The whole distributary features of this formation are: coarse and thick in north, fine and thin in south. From bottom to top, lower Shihezi Formation can be divided into three subsequences with fining upward. They are He 1, He 2, and He 3 members in order. The bottom sandstone in each subsequence usually contains gravel. The depositional facies of He 2 and He 3 members both belong to fluvial system. He 2 and He 3 members can be further classified into five and four layers, respectively, from bottom to top, namely, H 12 , H 22 , H 32 , H 42 , 5
1
2
3
4
H 2 , and H 3 , H 3 , H 3 , H 3 .
3
Analysis and use of seismic attribute
To ensure the use of extracted seismic attributes in each layer to be more accurate, through synthetical record, we have taken well top as controlling condition and seismic interface as trend. The division of layer on seismic section in well site has been controlled by the GR curve. Each layer is identified by the trend of reflection wave-set from top to bottom. Then, each layer in the two seismic projects has been tracked, which is used to control the extraction of seismic attribute. Using these technologies, various seismic attributes have been extracted such as RMS amplitude, the maximum amplitude, the minimum amplitude, frequency width, frequency steep, and the average instantaneous frequency. Through comparing the sand thickness and the seismic attributes in well site, and matching the two data, the RMS amplitude attribute has finally been
adopted to fit the math relationship of both to analyze the sand thickness distribution of each layer in the plane. The precondition of these fitted math relationships or models between well and seismic is that the correlation coefficient (R2) should be more than 0.65; otherwise, the models cannot be applied because their reliabilities are low. The study area has two blocks of 3-D seismic data, which are respectively 200 km2 in the north block and 100 km2 in the south. Hence, to ensure the consistency with the track and demarcation of each layer in both blocks, the correlation between both has been done in detail such that the seismic attributes in each layer are only extracted respectively and the relationship between the sandstone thickness in well and the seismic RMS amplitude has been fitted respectively in both blocks. For the study area, the data of seismic attributes in each layer being extracted is enormous. The original data without screen can be more than 0.7 million. To predict the distribution of sandbodies and to make the map of the depositional facies using integrated well and seismic data, the extracted data of seismic attributes must be screened to meet the need of prediction among wells. During this study, most of the distance among wells in the study area was usually from 1 to 2 km or more than 2 km. One third of spacing well being taken as the efficacious value was from 650 to 700 m around, i.e., on the basis of this view that to predict three points between every two wells is more reliable. The widths of channel being estimated range from 800 to 1000 m, on the basis of the ratio of width to thickness of fluvial system in the quantitive geological knowledge database. Thus, under the circumstances of known well site, the grid node of 600 × 600 m can completely arrive at the requirements of mapping depositional facies in plane[3–5]. Since the survey line of 3-D seismic data in both blocks is 20 m × 20 m, the spacing distance of grid data after screening according to In-line and X-line spacing 30 lines is 600 m. The whole controlling point is 994 along with the actual data of known well site (Fig. 1). In the actual mapping process, RMS in each node has been transformed into the data of sandstone thickness according to the fitted formulae between well and seismic in both blocks. The sieved data points can be used as “simulating virtual well point” or namely “controlling point” and addition to the actual data of known well site to form new grid data. It not only guarantees the precision of predication among wells, but also basically guarantees not to leak the distribution of fluvial sandbodies in spatiality. In this way, the node spacing of this grid is actually able to achieve such basic requirements of interpolation among wells for deterministic modeling[6].
4
Geological study on fluvial distribution
As well spacing is larger in the study area, to scientifically and rationally control the microfacies changes of fluvial system in plane, the distribution of sandstone thickness among wells has been predicted on the basis of a detailed analysis of
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
Fig. 1 Grid node plus well site in two 3-D seismic projects in the study area Total node is 994.
microfacies in well and combined with RMS amplitude attribute. The existence of sandbodies among wells has been carried out and the distribution of channel sand has been controlled. Having analyzed correlation between the sand thickness in actual well site and the RMS amplitude attribute extracted, the best fit formula of each layer is set up to predict the sand 1 thickness (Table). Taking H 2 layer as an example and using the data of sandstone thickness in new grid node (994 nodes or controlling point as above) after extracting along with known well site, the contours of initial sandstone thickness have been .
mapped on the basis of only the Kriging interpolation method of deterministic modeling (Fig. 2). By integrating regional background and depositional micro-facies in each well, and having modified the contours of initial sand thickness, the Table Formula of fit relationships between RMS and sandstone 1 thickness of two 3-D projects in H 2 layer of study area Layer 1
H2
3-D Project
Formula of fit relationship
(km2)
R2
200
y
0.0004 x 2 0.2196 x 12.584
0.6703
100
y 1E 0.8 x 2 0.0002 x 3.3681
0.9898
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
Fig. 2
1
Initial contours of sandstone thickness in H 2 layer depending on Kriging interpolation method Thickness in north is more than one in south.
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
Fig. 3
1
Modified contours of sandstone thickness in H 2 layer depending on the geological idea of fluvial system
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
the probability of these two type sandbodies to constrain the whole modeling process. Therefore, the constraint method of 2D trends probability has been selected for the stochastic modeling of microfacies in this study[8–11]. 5.1
Probability statistics of microfacies
The variability of microfacies in the modeling belongs to discrete variable state. The main microfacies on the basis of geological study include flooding mud, channel, overbank fine, point bar, channel bar, crevasse splay, and natural levee. There are several micro-facies that single microfacies can only account for a low proportion and it is not favorable to analyze the structure of variogram for each microfacies. Thus, channel and overbank fine have been combined to generalized channel. Analyses on the map of microfacies distribution (Fig. 4) and the probability statistics of microfacies in all wells (Figs. 5 and 6) display that the reservoir sandstone being developed in the study area mostly occur in channel bar and point bar in the channel. The area of point bar and channel bar accounts for 30.53% of He 2 and He 3 member; if not considering the flood plain microfacies, they can account for 64.59% of the total area. The probability of natural levee is secondary, but the probability of crevasse splay is minimum, which is less than 2.03%. These statistic data and geological viewpoints will be used to guide in optimizing the multiple realizations of stochastic modeling. When model realizations were optimized, the principle should be to stress considering the fitting degree between statistics probability of each realization and original data about point bar and channel bar microfacies. 1
Fig. 4 Distribution of depositional microfacies in H 2 layer depending on different data of sandstone thickness and genetic feature
ultimate contour map is obtained (Fig. 3) and the plane map of depositional facies is drawn (Fig. 4). On the basis of the results, the plane distribution and the vertical evolutive characteristics of depositional facies in each layer have been analyzed. The distribution of fluvial system was obvious that braided rivers were located in north of the study area and the meandering 1 river in south with H 2 layer was deposited.
5 Constraining method on reservoir stochastic modeling by controlled facies According to the field geological research of outcrops around the study area and combining with the analysis of depositional microfacies in wells, we found that the favorable reservoir sand bodies in the study area are channel bar or batture in braided river and point bar in meandering river[7]. In view of petroleum exploration, we have put forward a new thought of exploration to find channel bar in braided river and to determine point bar in meandering river. To represent the feature of stochastic modeling for fluvial reservoir, it is necessary to focus on using
Fig. 5 Single statistic probability of each microfacies for each layer of He 2 and He 3 members and their vertical changes in Daniudi gas field
YU Xinghe et al. / Earth Science Frontiers, 2008, 15(4): 33–41
microfacies, and have obtained their relational expressions of width and thickness. According to the sedimentary thickness of different microfacies measured by well, the distribution range of every microfacies can be calculated using their relational formula. The results have been calculated to be the channel bar width of braided river ranges from 890 to 1100 m, and the point bar width of meandering river from 800 to 980 m. These parameters have been selected to use as quantitative controlling data in the stochastic modeling process so as to further determine the ranges of sandstone extension and petrophysical change of each microfacies in lateral.
6
Fig. 6
Cumulative statistic probability of each microfacies for all layers of He 2 and He 3 members in Daniudi gas field
0: Flood Mud; 1: Channel; 2: Channel Bar; 3: Point Bar; 4: Crevasse Splay; 5: Natural Levee
5.2 Order of facies The sedimentary environment in the study area is a transitional region from braided river to meandering river, and the kind of microfacies is varied and its variation of channel in lateral is comparative frequently. Therefore, it is especially important to study the relationships of contact and transition among every microfacies. This interrelationship should be fully considered and used in the process of stochastic modeling[12]. The concrete method is as follows: Firstly, the strike directions and occurrence of major channel in the different position of the study area have been used to approximately control the microfacies distribution in plane. Then, the relationships of facies order or sequence (Fig. 7) have been applied to constrain modeling, and at the same time, the divisional data of microfacies in each well have been taken as the discrete variable to input software. Finally, these data have been dispersed into every node of three-dimensional grid to control microfacies change in vertical. 5.3
Model checking
Integrating study thought and technology as above, the algorithm of sequential indicator simulating has been applied to complete stochastic modeling controlled by facies for fluvial reservoir in the goal stratum of this study and to yield three realizations of the stochastic model. Through counting the probability of each microfacies in every realization and correlating with the original probability in each well, the optimal model can be selected according to the matching and fitting extent of every realization. Since the well numbers in the study area are less, for this reason, if the matching degree between original probability and realization can arrive in 75%–90%, it is seen that the predicting accuracy of stochastic modeling is very high. The result predicated that it could reflect truly the distributary characteristics of subsurface geological body. If the matching degree is more than 90%, it can be considered to be completely the result of deterministic modeling, and cannot act as the effect of stochastic prediction. If the matching degree is less than 75%, it can be considered that the accuracy is very less and its reliability is not enough. Through correlation between the probability of each microfacies in three realizations of the model and in each well, the matching error of realization one ranges from 15% to 20%, realization two about 35%, and realization three from 35% to38%. The fitting degree of realization one is the closest and matches best with the original geologic model; it shows that
Quantitative geological knowledge database
In the study area, as there are more changes in the thickness of each layer, the distribution of difficult microfacies were apparently controlled by the ratio of width to thickness in each microfacies. The extensional range of each microfacies sandbodies and the variation range of petrophysical property can be determined semi-quantitatively by the thickness of genetic sandstone, while their distributions have been predicated. In the study area, we have emphasized to perform statistics of the ratio of width to thickness for point bar and channel bar that occurred abundantly, fit the relationships respectively in
Fig. 7
Vertical order of microfacies in braided and meandering river of fluvial system
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this model can reflect very well the role of stochastic prediction. The microfacies’ model of realization one (Fig. 8) and the distributary map of sandstone thickness (Fig. 9) have been selected to take as the final results that have been submit to exploring geologists. By having made the practice of drilling for two years and more than 100 wells, the successful ratio of these predicting models is over 85%.
7
Conclusions
These micro-facies of channel bar in braided river and point bar in meandering were the major sand bodies with gas bearing in the study stratum. To predict considerably better the developing position of favorable sandbodies and to avoid the traditional thinking method that the mapping channel depends on sandstones in well, the new thought is that finding sand bodies depends on the distribution of fluvial system. The plane distributions of fluvial reservoir have been studied in
Fig. 9
Optimized predicting model of sandstone thickness 1
in H 2 layer
detail using this thought integrating with well and seismic. These results have been considered as conditions to constrain the process of stochastic modeling and to obtain better effect. According to combining with the methods of collaborative information, deterministic and stochastic modeling, the scientific thought of three steps constraining the stochastic modeling controlled by facies for fluvial reservoir is as follows: the order of facies, the probability of microfacies, and the quantitative geological knowledge database have been used to constrain modeling step by step in hierarchy. To correlate and optimize multiple realizations of stochastic modeling, the final model has been selected that can reflect really the distribution of subsurface geological body.
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