CN117930380A - Carbonate reservoir prediction method and system based on seismology - Google Patents

Carbonate reservoir prediction method and system based on seismology Download PDF

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CN117930380A
CN117930380A CN202410103027.7A CN202410103027A CN117930380A CN 117930380 A CN117930380 A CN 117930380A CN 202410103027 A CN202410103027 A CN 202410103027A CN 117930380 A CN117930380 A CN 117930380A
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reservoir
data
seismic
reservoir prediction
lithology
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谭磊
刘宏
李泯星
张旋
马梓珂
马乾
刘博文
杨孟祥
李哲宇
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Southwest Petroleum University
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    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
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    • GPHYSICS
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    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/21Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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    • G06F18/2131Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on a transform domain processing, e.g. wavelet transform
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08Feature extraction
    • G06F2218/10Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks

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Abstract

The invention discloses a carbonate reservoir prediction method and a carbonate reservoir prediction system based on seismology, comprising the following steps: data are collected from the study area. By analyzing the seismic data and the logging curves, the sequence interface of the target layer is identified and divided. Seismic amplitude data of the target layer is evaluated and a valid signal range is determined. And frequency division processing is carried out on the seismic data in the effective signal range. And performing high-precision sequence interface seismic tracking interpretation. And analyzing the distribution rule of the reservoir in the layer sequence lattice and the distribution condition of special lithology. And establishing a seismic forward model. Seismic forward modeling is performed to confirm the seismic response characteristics of the reservoir and the particular lithology. And selecting proper earthquake reservoir prediction methods or technologies for reservoirs at different sequence positions to perform reservoir prediction work. And evaluating the reservoir prediction result, and eliminating the systematic error which leads to the abnormal region. The invention has the advantages that: the accuracy, the efficiency and the economic benefits of data processing, exploration and development of reservoir prediction are improved.

Description

Carbonate reservoir prediction method and system based on seismology
Technical Field
The invention relates to the technical field of petroleum exploration and development, in particular to a carbonate reservoir prediction method and system based on seismology under a complex lithology background.
Background
Reservoir prediction is one of the most critical links in the oil and gas industry nowadays, and along with the continuous advancement of global oil and gas exploration and development processes, reservoir prediction targets are gradually transferred from a single-lithology thick-sleeve reservoir to a complex-lithology thin reservoir, so that the prediction precision requirements are increased. The reservoir prediction work performed by means of geological data such as outcrop and drilling is difficult to meet the high-precision reservoir prediction requirement, and the geophysical data has higher transverse resolution, so that the reservoir prediction method is an effective means for predicting the spatial distribution of the reservoir, and the main reservoir prediction method in the industry is mainly based on earthquake prediction.
Seismic reservoir prediction can be divided into qualitative prediction and quantitative prediction, wherein reservoir qualitative analysis mainly comprises seismic attribute analysis, and the seismic attribute refers to geometrical morphology, kinematics, dynamics and statistical characteristics of related seismic waves which are derived from pre-stack or post-stack seismic data through mathematical transformation. And information about reservoirs, structures and physical properties hidden in the data is picked up from the seismic data through attribute analysis, so that the purpose of describing the characteristic parameters of the reservoirs is achieved. Quantitative prediction mainly refers to seismic inversion, and is to estimate the internal structure, morphology and material composition of an object in the earth through known various geophysical measurement data and geological data and quantitatively calculate various related petrophysical parameters so as to achieve the aim of reservoir prediction.
Due to the complexity of geological conditions and the limitations of technology, various seismic attribute analyses have certain applicable conditions in application, and the prediction results also have multiple solutions. The seismic inversion technology is firstly unsuitable for large-scale use in early exploration and development due to huge calculation amount and cost, and the problem of multi-solution still exists when aiming at reservoir prediction under the complex lithology background. Therefore, there is no practical and effective technical means for reservoir prediction in a complex lithology setting.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a carbonate reservoir prediction method and a carbonate reservoir prediction system based on seismology.
In order to achieve the above object, the present invention adopts the following technical scheme:
A carbonate reservoir prediction method based on seismology, comprising the steps of:
S1, collecting zone adjustment data, seismic data, well logging curves, well drilling information and layering data of a research zone;
s2, identifying and dividing a single well sequence interface of a target layer by analyzing the seismic data and the logging curve, and comparing the sequence;
s3, evaluating seismic amplitude data of the target layer, and performing spectrum analysis to determine an effective signal range;
S4, frequency division processing is carried out on the seismic data in the effective signal range, the seismic data of a plurality of frequency bands are obtained, frequency division single well synthesis records are manufactured, frequency division data bodies with highest correlation coefficients are screened, and a layer sequence interface tracking interpretation scheme is defined;
s5, performing high-precision layer sequence interface seismic tracking interpretation to determine a layer sequence plane distribution range;
s6, analyzing the distribution rule of the reservoir in the well-seismic layer sequence grid and the distribution condition of special lithology;
s7, establishing an interval stratum, an internal reservoir and a special lithology earthquake forward model;
S8, selecting a proper forward modeling method, and performing earthquake forward modeling to confirm earthquake response characteristics of the reservoir and the special lithology;
S9, selecting a proper earthquake reservoir prediction method or technology aiming at reservoirs at different sequence positions on the basis of S5 to S8, and carrying out reservoir prediction work;
s91, selecting stratum units of an interval related to reservoir and special lithology distribution, and taking the characteristics of the stratum units as priori conditions of subsequent reservoir prediction;
S92, selecting a reservoir qualitative or quantitative prediction means based on the completion result of the S92, and predicting a reservoir development area.
S10, evaluating and analyzing the effect of the reservoir prediction result, evaluating the regional geological knowledge related to reservoir development and the data superposition of drilling test results, eliminating abnormal regions caused by systematic errors, and realizing reservoir prediction under the complex lithology background.
Further, the identification marks of the single well sequence interface identification division and the sequence comparison in the step S2 include, but are not limited to, marks of lithology, electrical property, archaea and geochemistry indexes.
Further, the effective signal range parameters in S3 include: dominant frequency and bandwidth.
Further, in the step S4, a frequency division technique based on fast matching pursuit, wavelet transformation and S transformation is used for testing, and a processing method with optimal profile effect and layer sequence depicting ability is selected to perform frequency division processing on the original amplitude data body.
Further, in the step S5, for the reflection feature, the interface is clear, the layer sequence interface with extremely strong traceability is subjected to auxiliary interpretation tracking by means of the seed point technology, so as to reduce the interpretation workload; for the relatively fuzzy reflective interface and the sequence interface with poor traceability, the manual interpretation is taken as the leading, and the auxiliary tracing solution technology is taken as the auxiliary.
Further, in said S6, within the well-to-seismic layer sequence grid, the impedance characteristics of the particular lithology should be of particular interest, in particular lithology bodies that are confusing with the reservoir interval; for these special lithologies, comprehensive analysis should be performed in combination with downhole geological information and seismic data to ensure accurate identification of reservoir intervals and to avoid confusion.
Further, in the step S7, based on analysis of the sequence, the reservoir, and the special lithology body spread, the thickness, the acoustic time difference, and the density of the theoretical model unit are obtained; and verifying and correcting by utilizing the seismic data and the underground geological data, so as to ensure that the acquired theoretical model unit parameters are accurate and reliable.
Further, in the step S8, the theoretical wavelet in the forward seismic modeling should select a wavelet consistent with the main frequency and bandwidth of the actual seismic data; picking up and realizing the well side wavelets to ensure that the selected theoretical wavelets accord with the characteristics of actual seismic data; the ray tracing method is selected in the forward excitation mode of the earthquake so as to realize reasonable excitation and propagation of the earthquake wave.
Further, in the step S9, according to the principle of depositional science, the development of the reservoir and the deposition distribution of the special lithology are controlled by the related layer sequence, so that the related analysis of the layer sequence stratum, the reservoir and the special lithology is required; the development probability of the reservoir and the characteristic lithology in a certain area is indicated through the related analysis of the stratum sequence stratum, the reservoir and the special lithology, and the development probability is used as a priori condition of reservoir prediction, so that the accuracy of reservoir prediction is improved; in conducting the analysis, it is noted that the analysis range of the isochronous formation thickness should not only include the sequence, but also should consider the system domains that make up the sequence to ensure the integrity and accuracy of the analysis.
Further, in the step S10, according to the actual drilling situation, the reservoir prediction result is corrected to ensure that the prediction result is consistent with the actual situation; verifying and confirming the prediction result by combining the data evaluation result, the regional geological knowledge and the drilling result data in the step S3, and eliminating an abnormal region possibly caused by abnormal data; by eliminating the abnormal region, the accuracy and reliability of the prediction result are improved, and the accuracy and effectiveness of subsequent work are ensured.
The invention also discloses a carbonate reservoir prediction system based on the seismology, which can be used for implementing the carbonate reservoir prediction method based on the seismology, and specifically comprises the following steps:
The basic data collection module: for collecting and inputting zone-tuning data, seismic data, logs, drilling information, and stratification data for a study area.
And the layer sequence interface identification and division module: by analyzing the seismic data and the logging curves, the sequence interface of the target layer is identified and divided.
The objective layer amplitude data evaluation and spectrum analysis module: and evaluating the seismic amplitude data of the target layer, and performing spectrum analysis to determine the effective signal range.
The data frequency division processing module: and carrying out frequency division processing on the seismic data in the effective signal range, obtaining the seismic data of a plurality of frequency bands and manufacturing frequency division single well synthesis records.
And the layer sequence interface tracking and interpretation module: and carrying out high-precision sequence interface seismic tracking interpretation, and determining the sequence plane distribution range.
And a reservoir and special lithology distribution rule analysis module: and analyzing the distribution rule of the reservoir in the layer sequence lattice and the distribution condition of special lithology.
Establishing a seismic forward geological model module: and establishing a seismic forward model of the stratum sequence stratum, the internal reservoir and the special lithology.
And a seismic forward module: and selecting a proper forward modeling method to perform earthquake forward modeling so as to confirm the earthquake response characteristics of the reservoir and the special lithology.
Reservoir prediction module: and selecting proper earthquake reservoir prediction methods or technologies for reservoirs at different sequence positions to perform reservoir prediction work.
And a reservoir prediction result evaluation and analysis module: and evaluating the reservoir prediction result, and eliminating the systematic error which leads to the abnormal region.
The invention also discloses a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the above-described method of carbonate reservoir prediction based on seismology.
Compared with the prior art, the invention has the advantages that:
1. More accurate prediction: by analyzing the seismic data and log, and combining with principles of seismology, reservoir distribution can be more accurately identified and predicted.
2. Multi-module comprehensive analysis: the method comprises layer sequence interface identification and division, spectrum analysis, reservoir distribution rule analysis and the like, and can comprehensively analyze geological information in different aspects, and improve prediction accuracy and comprehensiveness.
3. And (3) establishing a seismic forward geological model: by establishing the forward seismic model, the earthquake response characteristics of the reservoir and the special lithology are better simulated, and accurate prediction of the position and the property of the reservoir is facilitated.
4. Improving reliability of reservoir prediction: and the method combines information of various aspects such as seismic data, logging information, geological principles and the like, so that reservoir prediction is more basic and reliable.
5. Optimizing drilling decisions: accurate reservoir prediction is beneficial to optimizing a drilling scheme, reducing drilling risks and improving exploration and development efficiency and economic benefits.
6. And reservoir prediction is performed based on post-stack offset data, so that compared with a pre-stack reservoir prediction means, the method has the advantages of small calculated amount and high calculation speed, and improves the data processing efficiency.
7. By adopting the correlation theory and method of the seismology, the reservoir is stripped from the complex lithology through the sequence analysis, and the problem that the reservoir is difficult to distinguish from the special lithology is effectively solved, so that the accuracy and the precision of reservoir prediction are improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram of a spectrum analysis of a destination horizon of original post-stack data according to one embodiment of the present invention;
FIG. 3 is a layer sequence interface identification division flag for purposes of an embodiment of the present invention;
FIG. 4 is a diagram of a synthetic record of frequency-divided seismic data and an interval interface calibration according to an embodiment of the invention;
FIG. 5 is a histogram of complex lithology (silicalite) and reservoir (Yun Yan) impedance distribution according to an embodiment of the invention;
FIG. 6 is a comparative cross-sectional view of an interval, reservoir, complex lithology (silicalite) well connection in accordance with an embodiment of the present invention;
FIG. 7 is a plot of forward geologic models and forward simulated traces of earthquakes in accordance with an embodiment of the present invention;
FIG. 8 is a graph of an analysis of correlation of selected interval system domain thickness (isochronous stratigraphic units) with complex lithology and reservoirs in accordance with an embodiment of the present invention.
FIG. 9 is a plot of selected layer sequence domain thickness (isochronous stratigraphic unit) planes in accordance with an embodiment of the present invention.
FIG. 10 is a plan view of a reservoir for predicting a complex lithology (silicalite) development zone based on sequence formation constraints in accordance with an embodiment of the present invention.
FIG. 11 is a graph of reservoir plane distribution of a predicted complex lithology (silicalite) underdeveloped zone based on sequence formation constraints in accordance with an embodiment of the present invention.
FIG. 12 is a plan view of a reservoir prediction as ultimately completed by an embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the accompanying drawings and by way of examples in order to make the objects, technical solutions and advantages of the invention more apparent.
As shown in fig. 1, a carbonate reservoir prediction method based on seismology includes the following steps:
S1, basic data collection: collecting data from a study area, the data comprising: data such as zone-tuning data, post-stack migration seismic volumes, conventional logging curves, drilling logging data, well deviation data, coring data, layering data, seismic interpretation horizons, single well test productivity and the like;
s2, identifying and dividing single well sequence interfaces in the embodiment by utilizing data including conventional logging, imaging logging, core data, slice data, seismic data, geochemical indexes and the like, and carrying out sequence comparison;
S3, evaluating and spectrum analyzing original post-stack amplitude data of the target layer, and determining an effective signal range of the data of the target layer, wherein the main frequency of the data of the target layer is 36Hz, and the effective bandwidth is 9-65 Hz;
S4, frequency division processing is carried out on the data body within the effective signal range of the original amplitude data body, the seismic data body with multiple frequency bands is obtained, in the embodiment, 7 narrow-band data with main frequencies of 15Hz, 20Hz, 25Hz, 30Hz, 35Hz, 40Hz and 45Hz are obtained by adopting a frequency division technology based on wavelet transformation according to the mode that 15Hz is used as the initial frequency, 45Hz is used as the final frequency and 5Hz is used as the step length. The frequency division single well synthesis record is manufactured, wherein the correlation coefficient between the narrow frequency band data with 20Hz as the main frequency and the synthesis channel is 0.79, and the highest frequency division data is selected from 7 frequency division data, and the data is preferably used as the main research data of the sequence identification and characterization. Through calibration, three layer sequence interfaces SB1, SB2 and SB3 in the discovery zone from bottom to top are all expressed as peak phases, and the transverse traceability and the contrast are strong;
s5, carrying out seismic trace interpretation of a high-precision sequence interface, wherein in the embodiment, the phases of SB1 and SB3 are stable, the transverse traceability and the contrast are extremely high, the interface is interpreted by means of a seed point tracking technology, the quality is controlled by manual intervention, and the transverse traceability of SB2 is weakened and mainly interpreted manually. The plane distribution of SQ1 and SQ2 layer sequences is obtained through fine transformation graphics and other works;
S6, analyzing reservoir distribution and special lithology distribution rules in the well-seismic layer sequence frame, wherein in the embodiment, the reservoir development condition can be found to be good in the region with larger SQ1 high-level region (HST) stratum thickness, the reservoir development is poor in the region with thinner stratum thickness, and the silicalite deposition thickness is larger. According to the principle of depositional science, carbonate hill beach phase deposition has obvious inheritance of topography, so that in the early hill beach deposition, namely, the region with thicker stratum, the next period of hill beach construction belongs to the region with high topography, the carbonate hill beach construction has obvious constructive effect, and in the region with thinner deposition, the carbonate hill beach phase deposition belongs to the region with relatively lower energy, and siliceous fluid in the region is easier to deposit, so that siliceous rock with mixed impedance characteristics and reservoirs in the embodiment is formed. Therefore, the silicalite development area and the reservoir development area can be restricted qualitatively through the stratum thickness of the high-order domain in SQ1 in the embodiment;
S7, according to the real drilling situation, key parameters such as thickness values, speeds and densities of theoretical layer sequence stratum units, reservoirs and special lithology are picked up, and a seismic forward geological model is established;
S8, according to an actual seismic data optimization forward modeling method, forward modeling of the earthquake is performed, seismic response characteristics of reservoirs and special lithology are implemented, and in the embodiment, it can be found that when the SQ2 layer sequence top reservoir in the research area develops, the top amplitude is weakened, the reservoir associated with siliceous rock in the middle of the SQ2 layer sequence is characterized by weak peak amplitude, but peak reflection of the siliceous rock development area is stronger than that of the reservoir development area;
s9, carrying out reservoir prediction work on reservoirs with different sequence positions on the basis of S5-S8, preferably selecting a seismic reservoir prediction method or technology;
S91, selecting stratum units of an interval related to the reservoir and special lithology distribution, taking the characteristics of the stratum units as prior conditions of subsequent reservoir prediction, and particularly aiming at complex lithology (silicalite) and the reservoir in the middle of the SQ2 interval, in the embodiment, reflecting the peak of the stratum units as silicalite response when the stratum of the high-order region of the SQ1 interval is thinner, and reflecting the peak of the stratum units as reservoir response when the stratum of the high-order region of the SQ1 interval is thicker.
S92, selecting a reservoir qualitative or quantitative prediction means based on the completion result of S91, and predicting a reservoir development area. In the embodiment, under the condition that the thickness of the SQ1-HST is taken as a constraint, describing a reservoir in the middle of the SQ2 layer sequence by adopting the peak amplitude attribute at the bottom of the SQ2-HST, wherein in a region with smaller thickness of the SQ1-HST, the region with weaker peak amplitude is a region with stronger development of the reservoir; in the region with thicker SQ1-HST, the region with stronger peak amplitude is a region with stronger reservoir development, and two sets of reservoir prediction thickness maps are fused into a map on the basis.
S10, evaluating and analyzing the effect of the reservoir prediction result, overlapping and evaluating the related data such as regional geological awareness, drilling test result and the like related to reservoir development, removing abnormal values caused by data reasons, and finally forming a graph. In the embodiment, the reservoir prediction result and the regional drilling condition and the early-stage result are high in recognition coincidence rate;
as shown in fig. 2, the effective signal range of the original post-stack seismic data is defined as the basis condition for determining the crossover range.
As shown in FIG. 3, identification division marks of the layer sequence interfaces are defined, and layer sequence transverse comparison analysis is carried out.
As shown in fig. 4, the sequence interface is accurately calibrated by combining the frequency division synthesis records, and the plane distribution range and the distribution rule of each sequence and each system domain are implemented.
As shown in fig. 5, the complex lithology (silicalite) and reservoir (Yun Yan) impedance distribution histogram, and the subsequent sequence stratigraphic analysis and lithology distribution should be emphasized on the silicalite and reservoir distribution relationship.
As shown in fig. 6, key parameters such as the sequence, the reservoir, the distribution rule of each geological unit of the complex lithology (silicalite), the thickness and the speed of the forward geological model and the like are defined according to the well-connected section.
As shown in fig. 7, forward modeling provides a theoretical basis for sequence identification tracking and rejection of complex lithologic (silicalite) effects.
As shown in fig. 8, the sequence stratigraphic units of the reservoir prediction constraint are selected based on the sequence and complexity lithology and the reservoir correlation analysis.
As shown in fig. 9, the reservoir prediction constraint layer sequence stratum unit plane layout map is used for dividing the complex lithology (silicalite) distribution dominant region on the basis of the reservoir prediction constraint layer sequence stratum unit plane layout map.
As shown in fig. 10, the reservoir development of the complex lithology (silicalite) development zone is predicted based on the layer sequence formation constraints.
As shown in fig. 11, the reservoir development of complex lithology (silicalite) underdeveloped zones is predicted based on the formation constraints of the sequence.
As shown in fig. 12, the reservoir prediction plan in different partitions is subjected to anomaly value removal, fusion into a map, correction, comprehensive evaluation and other tasks, and then the reservoir prediction plan is finally completed.
In yet another embodiment of the present invention, a carbonate reservoir prediction system based on seismology is provided, which can be used to implement the above carbonate reservoir prediction method based on seismology, specifically including:
The basic data collection module: for collecting and inputting zone-tuning data, seismic data, logs, drilling information, and stratification data for a study area.
And the layer sequence interface identification and division module: by analyzing the seismic data and the logging curves, the sequence interface of the target layer is identified and divided.
The objective layer amplitude data evaluation and spectrum analysis module: and evaluating the seismic amplitude data of the target layer, and performing spectrum analysis to determine the effective signal range.
The data frequency division processing module: and carrying out frequency division processing on the seismic data in the effective signal range, obtaining the seismic data of a plurality of frequency bands and manufacturing frequency division single well synthesis records.
And the layer sequence interface tracking and interpretation module: and carrying out high-precision sequence interface seismic tracking interpretation, and determining the sequence plane distribution range.
And a reservoir and special lithology distribution rule analysis module: and analyzing the distribution rule of the reservoir in the layer sequence lattice and the distribution condition of special lithology.
Establishing a seismic forward geological model module: and establishing a seismic forward model of the stratum sequence stratum, the internal reservoir and the special lithology.
And a seismic forward module: and selecting a proper forward modeling method to perform earthquake forward modeling so as to confirm the earthquake response characteristics of the reservoir and the special lithology.
Reservoir prediction module: and selecting proper earthquake reservoir prediction methods or technologies for reservoirs at different sequence positions to perform reservoir prediction work.
And a reservoir prediction result evaluation and analysis module: and evaluating the reservoir prediction result, and eliminating the systematic error which leads to the abnormal region.
In a further embodiment of the present invention, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a terminal device, for storing programs and data. It will be appreciated that the computer readable storage medium herein may include both a built-in storage medium in the terminal device and an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the respective steps of the above embodiments with respect to a method for carbonate reservoir prediction based on seismology; one or more instructions in a computer-readable storage medium are loaded by a processor and perform the steps of:
s1, collecting zone adjustment data, seismic data, logging curves, drilling information and layering data of a research zone
S2, identifying and dividing a single well sequence interface of a target layer by analyzing the seismic data and the logging curve, and comparing the sequence;
s3, evaluating seismic amplitude data of the target layer, and performing spectrum analysis to determine an effective signal range;
S4, frequency division processing is carried out on the seismic data in the effective signal range, the seismic data of a plurality of frequency bands are obtained, frequency division single well synthesis records are manufactured, frequency division data bodies with highest correlation coefficients are screened, and a layer sequence interface tracking interpretation scheme is defined;
s5, performing high-precision layer sequence interface seismic tracking interpretation to determine a layer sequence plane distribution range;
s6, analyzing the distribution rule of the reservoir in the well-seismic layer sequence grid and the distribution condition of special lithology;
s7, establishing an interval stratum, an internal reservoir and a special lithology earthquake forward model;
S8, selecting a proper forward modeling method, and performing earthquake forward modeling to confirm earthquake response characteristics of the reservoir and the special lithology;
S9, selecting a proper earthquake reservoir prediction method or technology aiming at reservoirs at different sequence positions on the basis of S5 to S8, and carrying out reservoir prediction work;
s91, selecting stratum units of an interval related to reservoir and special lithology distribution, and taking the characteristics of the stratum units as priori conditions of subsequent reservoir prediction;
S92, selecting a reservoir qualitative or quantitative prediction means based on the completion result of the S92, and predicting a reservoir development area.
S10, evaluating and analyzing the effect of the reservoir prediction result, evaluating the regional geological knowledge related to reservoir development and the data superposition of drilling test results, eliminating abnormal regions caused by systematic errors, and realizing reservoir prediction under the complex lithology background.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those of ordinary skill in the art will appreciate that the embodiments described herein are intended to aid the reader in understanding the practice of the invention and that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.

Claims (10)

1. A carbonate reservoir prediction method based on seismology, comprising the steps of:
S1, collecting zone adjustment data, seismic data, well logging curves, well drilling information and layering data of a research zone;
s2, identifying and dividing a single well sequence interface of a target layer by analyzing the seismic data and the logging curve, and comparing the sequence;
s3, evaluating seismic amplitude data of the target layer, and performing spectrum analysis to determine an effective signal range;
S4, frequency division processing is carried out on the seismic data in the effective signal range, the seismic data of a plurality of frequency bands are obtained, frequency division single well synthesis records are manufactured, frequency division data bodies with highest correlation coefficients are screened, and a layer sequence interface tracking interpretation scheme is defined;
s5, performing high-precision layer sequence interface seismic tracking interpretation to determine a layer sequence plane distribution range;
s6, analyzing the distribution rule of the reservoir in the well-seismic layer sequence grid and the distribution condition of special lithology;
s7, establishing an interval stratum, an internal reservoir and a special lithology earthquake forward model;
S8, selecting a proper forward modeling method, and performing earthquake forward modeling to confirm earthquake response characteristics of the reservoir and the special lithology;
S9, selecting a proper earthquake reservoir prediction method or technology aiming at reservoirs at different sequence positions on the basis of S5 to S8, and carrying out reservoir prediction work;
s91, selecting stratum units of an interval related to reservoir and special lithology distribution, and taking the characteristics of the stratum units as priori conditions of subsequent reservoir prediction;
S92, selecting a reservoir qualitative or quantitative prediction means based on the completion result of the S91, and predicting a reservoir development area;
S10, evaluating and analyzing the effect of the reservoir prediction result, evaluating the regional geological knowledge related to reservoir development and the data superposition of drilling test results, eliminating abnormal regions caused by systematic errors, and realizing reservoir prediction under the complex lithology background.
2. A method of carbonate reservoir prediction based on seismology according to claim 1, characterized by: the identification marks of the single well sequence interface identification division and the sequence comparison in the S2 include but are not limited to marks of lithology, electrical property, archaea and geochemistry indexes.
3. A method of carbonate reservoir prediction based on seismology according to claim 1, characterized by: the effective signal range parameters in S3 include: dominant frequency and bandwidth.
4. A method of carbonate reservoir prediction based on seismology according to claim 1, characterized by: in the step S4, the frequency division technology based on the fast matching pursuit, wavelet transformation and S transformation is used for testing, and the processing method with optimal profile effect and layer sequence depicting capability is selected for carrying out frequency division processing on the original amplitude data body.
5. A method of carbonate reservoir prediction based on seismology according to claim 1, characterized by: in the S5, for clear reflection characteristics, clear interface and extremely strong traceability of an interval interface, auxiliary interpretation tracking is carried out by means of a seed point technology; for the relatively fuzzy reflective interface and the sequence interface with poor traceability, the manual interpretation is taken as the leading, and the auxiliary tracing solution technology is taken as the auxiliary.
6. A method of carbonate reservoir prediction based on seismology according to claim 1, characterized by: in said S6, within the well-to-seismic layer sequence grid, the impedance characteristics of the particular lithology should be of particular interest, particularly lithology bodies that are confusing with the reservoir interval; for these special lithologies, comprehensive analysis should be performed in combination with downhole geological information and seismic data to ensure accurate identification of reservoir intervals and to avoid confusion.
7. A method of carbonate reservoir prediction based on seismology according to claim 1, characterized by: in the S7, based on analysis of layer sequence, reservoir and special lithology body spread, the thickness, acoustic time difference and density of a theoretical model unit are obtained; and verifying and correcting by using the seismic data and the underground geological data.
8. A method of carbonate reservoir prediction based on seismology according to claim 1, characterized by: in the S8, the theoretical wavelet in the forward earthquake modeling is selected to be the wavelet consistent with the main frequency and the bandwidth of the actual earthquake data; by picking up and implementing the side wavelet; a ray tracing method is selected in a seismic forward excitation mode.
9. A method of carbonate reservoir prediction based on seismology according to claim 1, characterized by: in the step S10, according to the actual drilling situation, reservoir prediction results are corrected to ensure that the prediction results are consistent with the actual situation; and (3) verifying and confirming the prediction result by combining the data evaluation result, the regional geological knowledge and the drilling result data in the step (S3), and eliminating the abnormal region possibly caused by the abnormal data.
10. A carbonate reservoir prediction system based on seismology, characterized in that: the system can be used to implement the carbonate reservoir prediction method based on seismography according to one of claims 1 to 9, comprising in particular:
the basic data collection module: zone control data, seismic data, well logs, well drilling information, and stratification data for collecting and inputting a study area;
and the layer sequence interface identification and division module: identifying and dividing an interval interface of a target layer by analyzing the seismic data and the logging curve;
the objective layer amplitude data evaluation and spectrum analysis module: evaluating seismic amplitude data of the target layer, and performing spectrum analysis to determine an effective signal range;
the data frequency division processing module: performing frequency division processing on the seismic data in the effective signal range to obtain the seismic data of a plurality of frequency bands and manufacturing frequency division single well synthesis records;
and the layer sequence interface tracking and interpretation module: performing high-precision sequence interface seismic tracking interpretation, and determining a sequence plane distribution range;
and a reservoir and special lithology distribution rule analysis module: analyzing the distribution rule of the reservoir in the layer sequence lattice frame and the distribution condition of special lithology;
establishing a seismic forward geological model module: establishing an earthquake forward model of an interval stratum, an internal reservoir and special lithology;
And a seismic forward module: selecting a proper forward modeling method, and performing earthquake forward modeling to confirm earthquake response characteristics of a reservoir and special lithology;
reservoir prediction module: selecting proper earthquake reservoir prediction methods or technologies for reservoirs at different sequence positions to perform reservoir prediction work;
and a reservoir prediction result evaluation and analysis module: and evaluating the reservoir prediction result, and eliminating the systematic error which leads to the abnormal region.
CN202410103027.7A 2024-01-25 2024-01-25 Carbonate reservoir prediction method and system based on seismology Pending CN117930380A (en)

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