CN109387867B - Compact sandstone reservoir modeling method - Google Patents
Compact sandstone reservoir modeling method Download PDFInfo
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- CN109387867B CN109387867B CN201710680680.XA CN201710680680A CN109387867B CN 109387867 B CN109387867 B CN 109387867B CN 201710680680 A CN201710680680 A CN 201710680680A CN 109387867 B CN109387867 B CN 109387867B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/282—Application of seismic models, synthetic seismograms
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
- G01V2210/6169—Data from specific type of measurement using well-logging
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/624—Reservoir parameters
Abstract
The invention discloses a compact sandstone reservoir modeling method, which comprises the following steps: performing lithologic inversion by using well drilling and logging data as constraints based on the post-stack three-dimensional seismic interpretation data to obtain a plurality of inversion data volumes; comparing the plurality of inversion data volumes with the well logging interpretation conclusion, and preferably selecting the inversion data volumes which can reflect different lithologies most; determining lithology division standard according to the correlation between lithology data released by well logging interpretation and the value in the optimized inversion data body; performing lithology recognition on the numerical value of the inversion data volume in each three-dimensional grid in the three-dimensional space based on the lithology division standard to obtain a three-dimensional lithology data volume; arranging virtual wells at the main body center of the sedimentary facies and the positions inside each boundary, and determining numerical values in a three-dimensional lithology data body corresponding to the virtual wells as lithology data of the virtual wells; the lithological data of the real well and the virtual well are used as modeling basic data to carry out random simulation modeling to obtain a three-dimensional reservoir lithofacies model, so that the aim of precisely describing the reservoir is fulfilled.
Description
Technical Field
The invention relates to the technical field of petroleum and natural gas development, in particular to a compact sandstone reservoir modeling method.
Background
Reservoir fine geological modeling is the basis and key point of oil and gas field development, has the significance of visually describing the structural characteristics, sedimentary facies types and distributions, the geometric morphology and size of a reservoir body, reservoir parameter distribution, heterogeneity characteristics and the like of an oil and gas reservoir, and provides a reliable geological information carrier for gas reservoir engineering research. The basic idea of the current more common reservoir fine description is as follows: firstly, rock core observation and analysis are carried out to determine the lithofacies type, logging interpretation and logging facies analysis are carried out by combining the logging curve characteristics, and vertical and plane sedimentary combination is carried out to determine sedimentary facies distribution under the regional geological background; and then, establishing a reservoir three-dimensional geological model by taking the logging interpretation conclusion as a basis, the sedimentary model as a guide and the seismic attribute and the sedimentary facies spread as constraints.
The existing reservoir geological modeling method is to comprehensively utilize means such as rock cores, outcrops, well logging and the like to analyze sedimentary facies and reservoir distribution, combine with the well earthquake to explain the predicted favorable reservoir position and boundary, take well point hard data as the basis, comprehensively utilize means such as geology, earthquake and the like to carry out constraint, and adopt a random modeling method to establish a three-dimensional geological model. The method is mainly characterized in that reservoir geological modeling is carried out by combining core, outcrop, earthquake and well logging under the condition of a close well pattern and utilizing a random simulation method. But for a large-well-distance thin well pattern area, under the condition that the inter-well constraint mainly depends on seismic data, the three-dimensional fine reservoir characterization scheme in the method has large uncertainty.
The defects of the prior art are as follows:
(1) under the condition of a dense well pattern, well point information is established mainly by using rock cores and logging information, geological modeling is carried out under the constraints of sedimentary facies analysis, seismic interpretation, configuration analysis and the like, the dependence degree on seismic information is low, and the effect of finely describing a reservoir can be achieved to a certain extent, but for the condition that the well distance of a new block is large, particularly the condition that a river facies compact reservoir changes complicatedly, the distribution of the reservoir on a three-dimensional space cannot be finely carved by a random modeling method based on the well points and the conventional phase control constraint;
(2) in a thin well network area, a stochastic modeling technology based on seismic data constraint mainly combines post-stack seismic inversion data and a logging interpretation conclusion to establish a probability volume of reservoir distribution, and establishes a geological model by taking the probability volume as an interwell constraint.
Disclosure of Invention
Aiming at the technical problem, the invention provides a compact sandstone reservoir modeling method, which comprises the following steps:
s10, based on the post-stack three-dimensional seismic interpretation data, using well drilling data and well logging data as constraints to perform lithological inversion to obtain a plurality of inversion data volumes;
s20, comparing the plurality of inversion data volumes with the well logging interpretation conclusion, and preferably selecting the inversion data volume which can best embody lithology change characteristics from the plurality of inversion data volumes;
s30, determining the distribution range of the numerical values corresponding to different lithologies in the optimized inversion data body according to the correlation between the various lithology data analyzed by logging and the numerical values on the well track in the optimized inversion data body, and taking the distribution range as the lithology division standard;
s40, under the constraint of planar sedimentary facies, according to the lithology division standard, performing lithology recognition on the numerical value of the inversion data volume in each three-dimensional grid in the whole three-dimensional space to obtain a corresponding three-dimensional lithology data volume;
s50, arranging virtual wells at the main body centers and the inner positions of all boundaries of different sedimentary facies in the geological model according to sedimentary facies spreading and reservoir development scale, and taking the numerical values in the three-dimensional lithology data bodies corresponding to the virtual wells as the lithology data of the virtual wells;
and S60, taking lithologic data of the real well and lithologic data of the virtual well as basic data for modeling, taking reservoir configuration parameters obtained by geological knowledge as a variation function for modeling, and taking the optimized inversion data body as a constraint to carry out random simulation to obtain a three-dimensional reservoir lithofacies model.
In one embodiment, in step S10, lithology inversion is performed using a wave impedance method or using a gamma method.
In one embodiment, in step S30, the plurality of lithology data interpreted by the logging includes sandstone data, medium sandstone data and mudstone data.
In one embodiment, in step S40, the distribution range of the values corresponding to the sandstone in the inverted data volume is 60 to 140, the distribution range of the values corresponding to the sandstone in the inverted data volume is 140 to 230, and the distribution range of the values corresponding to the mudstone in the inverted data volume is 230 to 450.
In one embodiment, in step S40, the three-dimensional lithology data volume is a three-dimensional coarse sandstone data volume, a three-dimensional medium fine sandstone data volume and a three-dimensional mudstone data volume.
In one embodiment, in step S50, the virtual well is set to meet the well spacing requirements of the area under study.
In one embodiment, in step S50, if the sedimentary facies is a river facies, a virtual well is set at a position inside a river center axis and each river boundary of the river facies.
In one embodiment, in step S60, the reservoir configuration parameter is the shape and size of the reservoir.
In one embodiment, the size of the reservoir includes the length, width, and thickness of the reservoir.
In one embodiment, in step S60, sequential indicative stochastic simulation is performed with the inverted data volume as a constraint, resulting in a three-dimensional reservoir lithofacies model.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
1. the method comprises the steps of performing lithology recognition on numerical values of inverted data bodies in each three-dimensional grid according to distribution ranges of numerical values corresponding to different lithologies in the inverted data bodies to obtain three-dimensional lithology data bodies, inserting virtual wells around actual drilling wells in a geological model due to overlarge well spacing of a thin well pattern area, determining the numerical values in the three-dimensional lithology data bodies as lithology data of the virtual wells, and using the lithology data of the actual drilling wells and the lithology data of the virtual wells as modeling basic data together, so that the influence range of known wells can be increased in the modeling process, the constraint effect of seismic data of the thin well pattern area in the reservoir modeling process can be increased, the reservoir can be precisely described, and particularly the purpose of accurately describing the morphology and the spatial distribution characteristics of a river-phase compact sandstone reservoir can be achieved.
2. The modeling method based on lithology recognition and virtual well constraint is easy to implement and strong in operability, and the established three-dimensional reservoir lithofacies model can provide important basis for well position deployment and horizontal well design.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
figure 1 shows a flow chart of a tight sandstone reservoir modeling method of a first embodiment of the present invention;
FIG. 2 is a diagram illustrating a correlation analysis of a plurality of lithology data interpreted by logging and a preferred gamma inversion data volume according to a second embodiment of the present invention;
FIG. 3 is a perspective view showing lithology identification of data volume inverted from a set of stone boxes below a block in a Dongsheng gas field according to a second embodiment of the present invention;
FIG. 4 is a diagram showing the location of a small layer of virtual wells in a set of stone boxes at a region of the Dongsheng gas field in accordance with a second embodiment of the present invention;
fig. 5 shows a three-dimensional reservoir lithofacies model of a set of stone boxes below a certain region of the Dongsheng gas field according to a second embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
First embodiment
Fig. 1 is a flow chart of a tight sandstone reservoir modeling method according to a first embodiment of the present invention. As shown in fig. 1, the following steps S10 to S60 are mainly included.
In step S10, lithological inversion is performed based on the post-stack three-dimensional seismic interpretation data with the drilling data and the logging data as constraints to obtain a plurality of inversion data volumes.
Specifically, a geological model database of a research area is established, the post-stack three-dimensional seismic data is utilized, drilling data and logging data are used as constraints, lithological inversion is carried out by a wave impedance method or by a gamma method, and a plurality of inversion data bodies are obtained. In this embodiment, it is preferable that the inverse data volume in the form of a three-dimensional grid obtained by the gamma method is itself a data volume consisting of a large number of three-dimensional grids, each of which has a numerical value.
In step S20, the multiple inversion data volumes are compared with the well logging interpretation conclusion, and the inversion data volume that best represents the lithology change characteristics is selected from the multiple inversion data volumes. That is, one inversion data volume capable of clearly distinguishing different lithologies is preferably selected from the plurality of inversion data volumes.
In step S30, according to the correlation between the lithology data interpreted by logging and the values on the well trajectory in the inverted data volume preferred in step S20, the distribution range of the values corresponding to different lithologies in the inverted data volume preferred in step S20, that is, the lithology division standard, is determined. Specifically, the plurality of lithology data extracted by the logging interpretation may be obtained by interpreting lithology types and response characteristics partitioned by the logging interpretation before performing lithology inversion in step S10.
And performing correlation analysis on various lithological data interpreted by logging and various numerical values on a well track in the optimized inversion data body, namely performing intersection analysis on the lithological data interpreted by logging on the well track in the two-dimensional space and the numerical values on the well track in the inversion data body in the three-dimensional space so as to determine the lithological division standard.
In step S40, under the constraint of planar depositional facies, according to the lithology division standard established in step S30, performing lithology recognition on the numerical value of the inverted data volume in each three-dimensional grid in the whole three-dimensional space, so as to obtain a corresponding three-dimensional lithology data volume. That is, under the planar depositional facies constraint, the lithology to which the numerical value in the inverted data volume in the entire three-dimensional space belongs is identified according to the lithology division standard established in step S30.
In step S50, according to the depofacies spread and the reservoir development scale, virtual wells are set at the main body centers and the positions inside the boundaries of different depofacies in the geological model, and the numerical values in the three-dimensional lithology data volume corresponding to the virtual wells are used as the lithology data of the virtual wells.
In specific application, the density of the virtual wells is determined by combining reasonable development well spacing of a research area, and the virtual wells are inserted into proper positions.
In step S60, the lithology data of the real wells and the lithology data of the virtual wells are used as basic data for modeling, the reservoir configuration parameters obtained from geological knowledge are used as a variation function for modeling, and random simulation is performed with the inversion data volume selected in step S20 as a constraint to obtain a three-dimensional reservoir lithofacies model. In particular, the reservoir configuration parameters are the shape and size of the reservoir, including the length, width and thickness of the reservoir.
In summary, the tight sandstone reservoir modeling method provided by the invention identifies the lithology of the numerical value of the inverted data volume in each three-dimensional grid according to the distribution range of the numerical values corresponding to different lithologies in the inverted data volume, so as to obtain the three-dimensional lithology data volume, and is particularly suitable for reservoir modeling in a thin well pattern area. Because the well spacing of the thin well pattern area is too large, virtual wells are inserted around actual wells in the geological model, numerical values in the three-dimensional lithology data volume are determined as lithology data of the virtual wells, and then the lithology data of the actual wells and the lithology data of the virtual wells are used as modeling basic data together, so that the influence range of known wells can be enlarged in the modeling process, the constraint effect of seismic data of the thin well pattern area in the reservoir modeling process is improved, and the purpose of precisely describing the reservoir is achieved.
Second embodiment
The method has strong operability, and the application of a certain block of the northeast China-Sheng gas field proves that the method can more accurately establish the three-dimensional reservoir facies model of the target area, thereby realizing the fine depiction of the reservoir in the three-dimensional space. In the following, a certain block of the northeast China-Sheng gas field is taken as a research object, and how to apply the compact sandstone reservoir modeling method to the research area is explained in detail.
And establishing a geological model database of the research area, and performing lithologic inversion by using a gamma method by taking well drilling data and well logging data as constraints on the basis of post-stack three-dimensional seismic interpretation data to obtain a plurality of inversion data volumes. And before lithology inversion is carried out, various lithology data are interpreted according to lithology types and response characteristics divided by well logging interpretation. The well logging interpretation includes coarse sandstone data, medium and fine sandstone data and mudstone data.
And comparing the plurality of inversion data volumes with the well logging interpretation conclusion, and determining an inversion data volume capable of distinguishing different lithologies from the plurality of inversion data volumes.
And determining the distribution range of numerical values corresponding to different lithologies in the inversion data body according to the correlation between the various lithology data (coarse sandstone data, medium and fine sandstone data and mudstone data) interpreted by logging and various numerical values on the well track in the gamma inversion data body. As shown in fig. 2, it is a correlation analysis graph (i.e. a cross plot) of a plurality of lithology data interpreted by logging in the embodiment and a preferred gamma inversion data volume, where the abscissa represents the value in the gamma inversion data volume and the ordinate represents the probability. 10 represents a numerical distribution area in the gamma inversion data body corresponding to coarse sandstone of the gamma inversion data body, 20 represents a numerical distribution area in the gamma inversion data body corresponding to medium-fine sandstone, and 30 represents a numerical distribution area in the gamma inversion data body corresponding to mudstone. It can be seen that there is a significant difference in the values in the inverted data volumes for different lithologies. The distribution range of the numerical values corresponding to the coarse sandstone in the gamma inversion data body is 60-140, the distribution range of the numerical values corresponding to the fine sandstone in the gamma inversion data body is 140-230, and the distribution range of the numerical values corresponding to the mudstone in the gamma inversion data body is 230-450.
And under the constraint of the planar sedimentary facies, according to the distribution range of numerical values corresponding to different lithologies in the inverted data volume, performing lithology identification on the numerical value of the inverted data volume in each three-dimensional grid to obtain a three-dimensional lithology data volume, and performing three-dimensional perspective display. Fig. 3 is a perspective view showing lithology recognition of data volume by inversion of a rock box set under a certain block of the Dongsheng gas field according to an embodiment of the present invention. Wherein, the numbers marked in fig. 3 are numbers of actual drilling. The three-dimensional lithology data volume is a three-dimensional coarse sandstone data volume, a three-dimensional medium and fine sandstone data volume and a three-dimensional mudstone data volume.
And arranging virtual wells at the main body centers and the inner positions of all boundaries of different sedimentary facies in the geological model according to sedimentary facies spreading and reservoir development scale, and simultaneously meeting the requirement of reasonable well spacing in the region. In this embodiment, the sedimentary facies is a river facies, virtual wells are arranged at the central axis position of the river channel and the positions inside the boundaries of each river channel, and then the virtual wells are inserted at appropriate positions according to the reasonable development well spacing of the region of 800 to 1000 meters, so as to control the reservoir distribution of the whole research area. Fig. 4 is a diagram showing the position of a small layer of virtual wells of a stone box set in a certain area of the Dongsheng gas field according to an embodiment of the present invention, wherein the solid dots in fig. 4 represent the virtual wells, and the hollow dots represent the actual wells. And after the virtual well is set, determining the numerical value in the three-dimensional lithology data body corresponding to the virtual well as the lithology data of the virtual well.
Using lithology data of real wells and lithology data of virtual wells as basic data of modeling, using reservoir configuration parameters obtained by geological recognition as a variation function of modeling, wherein the main variation range is 6000 meters, the secondary variation range is 3500 meters, the vertical variation range is 5 meters, and performing sequential indication random simulation by using a preferred gamma inversion data body as constraint to obtain a fine three-dimensional reservoir lithofacies model shown in figure 5.
The tight sandstone reservoir modeling method of the embodiment is applied to a certain block of the northeast China-Hengsheng gas field, and according to experimental results, the tight sandstone reservoir modeling method of the embodiment solves the problems that the lithology of a river-facies tight sandstone reservoir is fast in change, the sand body distribution is complex, the reservoir is difficult to accurately describe, particularly, a reservoir model cannot be effectively constrained by the interwell seismic attributes in a thin well pattern area, and can accurately establish a three-dimensional reservoir lithofacies model of a target area, so that the reservoir can be precisely carved on a three-dimensional space.
Therefore, different from the prior art, the lithological data of the actual well and the lithological data of the virtual well are used as the basic data of modeling, the influence range of the known well is enlarged in the modeling process, the uncertainty of the simulation result is reduced, and the purpose of describing the reservoir stratum finely is achieved.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A tight sandstone reservoir modeling method is characterized by comprising the following steps:
s10, based on the post-stack three-dimensional seismic interpretation data, using well drilling data and well logging data as constraints to perform lithological inversion to obtain a plurality of inversion data volumes;
s20, comparing the plurality of inversion data volumes with the well logging interpretation conclusion, and preferably selecting the inversion data volume which can best embody lithology change characteristics from the plurality of inversion data volumes;
s30, determining the distribution range of the numerical values corresponding to different lithologies in the optimized inversion data body according to the correlation between the various lithology data analyzed by logging and the numerical values on the well track in the optimized inversion data body, and taking the distribution range as the lithology division standard;
s40, under the constraint of planar sedimentary facies, according to the lithology division standard, performing lithology recognition on the numerical value of the inversion data volume in each three-dimensional grid in the whole three-dimensional space to obtain a corresponding three-dimensional lithology data volume;
s50, arranging virtual wells at the main body centers and the inner positions of all boundaries of different sedimentary facies in the geological model according to sedimentary facies spreading and reservoir development scale, and taking the numerical values in the three-dimensional lithology data bodies corresponding to the virtual wells as the lithology data of the virtual wells;
and S60, taking lithologic data of the real well and lithologic data of the virtual well as basic data for modeling, taking reservoir configuration parameters obtained by geological knowledge as a variation function for modeling, and taking the optimized inversion data body as a constraint to carry out random simulation to obtain a three-dimensional reservoir lithofacies model.
2. The method of claim 1, wherein in step S10, lithology inversion is performed by using a wave impedance method or by using a gamma method.
3. The method of claim 1, wherein in step S30, the plurality of lithology data interpreted by the logging comprises sandstone data, siltstone data, and mudstone data.
4. The method of claim 3, wherein in step S40, the distribution range of the values corresponding to sandstone in the inverted data volume is 60 to 140, the distribution range of the values corresponding to sandstone in the inverted data volume is 140 to 230, and the distribution range of the values corresponding to mudstone in the inverted data volume is 230 to 450.
5. The method of claim 3 or 4, wherein in step S40, the three-dimensional lithology data volumes are a three-dimensional sandstone data volume, a three-dimensional siltstone data volume and a three-dimensional mudstone data volume.
6. The method of claim 1, wherein in step S50, the virtual well is set to meet well spacing requirements for the area under study.
7. The method of claim 1, wherein in step S50, if the sedimentary facies is a river facies, a virtual well is provided at a position inside a river center axis and each river boundary of the river facies.
8. The method of claim 1, wherein in step S60, the reservoir configuration parameter is a shape and a size of the reservoir.
9. The method of claim 8, wherein the size of the reservoir comprises a length, a width, and a thickness of the reservoir.
10. The method of claim 1, wherein in step S60, sequential indicative stochastic simulation is performed with the inverted data volume as a constraint, resulting in a three-dimensional reservoir lithofacies model.
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