Well leakage prediction method with seismic data

A technology of seismic data and prediction method, which is applied in the directions of surveying, earthwork drilling and production, wellbore/well components, etc. It can solve problems such as leakage channels, formation pressure deficit, pressure excitation, etc., achieve accuracy and efficiency improvement, and identification accuracy Effect of improvement, efficiency improvement of recognition

Active Publication Date: 2019-11-12
CHINA NAT PETROLEUM CORP CHUANQING DRILLING ENG CO LTD +1
10 Cites 3 Cited by

AI-Extracted Technical Summary

Problems solved by technology

[0002] During operations such as shale gas horizontal well drilling in the Sichuan Basin, lost circulation often occurs, which seriously affects the progress and development effect of shale gas exploration and development projects
[0006] 2. The performance of the drilling fluid is not good or the operation is improper, resulting in artificial leakage channels
[0007] 3. The pressure of the drilled formation is insufficient, or the density of the drilling fluid is too high, resulting in a large pressure difference
[0008] 4. The viscosity of the drilling fluid is too high and the shear force is too high, resulting in too high pressure to start the pump, resulting in pressure excitement and formation leakage
[0009] 5. The sand-carrying performance of the drilling fluid ...
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Abstract

The invention discloses a well leakage prediction method with seismic data, and relates to the technical field of seismic data application of oil and gas field exploration and development. The well leakage prediction method includes the steps that a, high resolution processing is conducted; b, fine horizon correlation is conducted; c, a fold is automatically identified, specifically, formation curvature is obtained along the seismic horizon; d, the fold property is judged, specifically, anticline or syncline are judged according to positive and negative change of the curvature; e, amplitude difference is extracted, specifically, the amplitude difference of each direction in the range of a target layer is calculated with the center of a micro-amplitude fold; f, the micro-amplitude fold amount is calculated, specifically, according to three parameters of positive and negative sign symbols of the curvature, the curvature and an amplitude difference value, the micro-amplitude fold amount is calculated comprehensively; g, the micro-amplitude fold is classified, specifically, according to the calculated micro-amplitude fold amount, the micro-amplitude fold is classified; and h, a well leakage point is predicted, specifically, according to well trajectory coordinates, possible well leakage point positions are picked up from distribution data of the micro-amplitude fold. According to the well leakage prediction method, the accuracy and the efficiency for predicting the fractured leakage point of a horizontal well according to the micro-amplitude fold are significantly improved.

Application Domain

SurveyData processing applications

Technology Topic

Image resolutionNegative sign +6

Image

  • Well leakage prediction method with seismic data
  • Well leakage prediction method with seismic data
  • Well leakage prediction method with seismic data

Examples

  • Experimental program(3)

Example Embodiment

[0042] Example 1
[0043] As a preferred embodiment of the present invention, it discloses a method for prediction of lost circulation from seismic data, the steps of which are:
[0044] a. High-resolution processing: processing conventional lower-resolution seismic data into high-resolution data;
[0045] b. Fine horizon comparison: Accurately pick up the seismic horizon according to the usual seismic horizon comparison technology;
[0046] c. Automatic recognition of folds: obtain the curvature of the stratum along the seismic horizon, find folds, and judge the positive or negative curvature at the same time;
[0047] d. Judgment of fold properties: judge whether it is an anticline or syncline according to the positive or negative change of curvature;
[0048] e. Extract the amplitude difference: calculate the amplitude difference in all directions within the range of the target layer based on the center of the slight fold;
[0049] f. Calculate the amount of slight wrinkles: comprehensively calculate the amount of slight wrinkles according to the three parameters of the sign of curvature, the magnitude of the curvature and the difference in amplitude;
[0050] g. Grading of micro folds: According to the calculated amount of micro folds, the micro folds are classified;
[0051] h. Lost circulation point prediction: According to the well trajectory coordinates, pick up the possible lost circulation point location from the microfold fold distribution data, and extract the slight fold volume along the horizontal well trajectory to obtain the lost circulation point prediction result.
[0052] The high resolution mentioned in step a above is relative. According to the actual prediction effect, if the resolution of seismic data is already high, high resolution processing is not required first, otherwise, high resolution processing must be performed first to increase the resolution The method of is arbitrary, and any high-resolution processing method is included; the calculation formula of the amount of small wrinkles in step f is not fixed, but all include the magnitude of curvature, the sign of curvature and the amplitude difference value. The horizontal well in step h is not limited to shale gas, but includes horizontal wells of various reservoir types.
[0053] Term explanation
[0054] Folding: refers to the structure that the rock layers that make up the earth's crust bend in a wave shape without losing continuity under the action of tectonic stress, generally including anticline, syncline, flexure and other structures.
[0055] Micro-fold folds: that is, micro-folds. It is a relative concept. It refers to folds with a small amplitude and their scale in the vertical or horizontal direction is relatively small. Microfold folds are not easy to identify on seismic profiles with lower resolution. The micro-amplitude folds referred to in the present invention refer to folds that are difficult to identify on lower-resolution seismic data, but can be identified on relatively high-resolution seismic data, including synclines, anticlines, and flexures.

Example Embodiment

[0056] Example 2
[0057] Reference figure 1 As the best embodiment of the present invention, the steps are:
[0058] a. High-resolution processing: processing conventional lower-resolution seismic data into high-resolution data; if the resolution of the seismic data is already high, this step can be ignored. The high-resolution processing method is arbitrary, but the resolution There should be a significant improvement.
[0059] b. Fine horizon comparison: Accurately pick up seismic horizons according to the usual seismic horizon comparison technology; the picked seismic horizons should be on the same reflection feature point or geological interface, and the reflection feature points are wave crests or troughs.
[0060] c. Automatic recognition of folds: obtain the curvature of the stratum along the seismic horizon, find folds, and judge the positive or negative curvature at the same time;
[0061] d. Judgment of fold properties: judge whether it is an anticline or a syncline according to the positive or negative change of curvature; determine the fold direction according to the positive or negative curvature. If the fold direction is convex, it means an anticline, and if the fold direction is concave, it means syncline or syncline. For flexure, the fold properties corresponding to synclines are compressive stress, and the fold properties corresponding to anticlines are tensile stress.
[0062] e. Extract the amplitude difference: calculate the amplitude difference in all directions within the range of the target layer based on the center of the slight fold;
[0063] f. Calculate the amount of slight wrinkles: the amount of slight wrinkles = Σ curvature sign × curvature × amplitude difference,
[0064] Σ represents the sum of the amount of slight folds in all directions to obtain the amount of slight folds at the fold position, and then calculate the amount of slight folds at each point in the formation.
[0065] g. Grading of micro folds: According to the calculated amount of micro folds, the micro folds are classified;
[0066] h. Lost circulation point prediction: According to the well trajectory coordinates, pick up the possible lost circulation point location from the microfold fold distribution data, and extract the slight fold volume along the horizontal well trajectory to obtain the lost circulation point prediction result.
[0067] In the step g, when the number of lost circulation points is known to be small or not statistically significant, the classification is directly divided according to the amount of slight folds, on the contrary, according to the severity of the existing lost circulation data such as the amount of lost circulation and slight folds The amount is calibrated to determine.
[0068] In the step h, the geological design designs the section and the plane well trajectory according to the seismic section and the reservoir prediction plan, and then projects the well trajectory on the micro-fold plan prediction map and the section prediction map to observe the micro trajectory. According to the well trajectory coordinates, the amount of slight folds is accurately extracted, and the risk points of lost circulation are determined according to the classification of the amount of slight folds, and the result of the leakage point prediction is obtained.

Example Embodiment

[0069] Example 3
[0070] Specific application examples: Refer to 2 and 3 to clarify the principles of specific calculation methods.
[0071] figure 2 It contains an anticline and a syncline, just take an anticline as an example. Determine the center of the formation, and the thickness of the formation (the length of the time window when the seismic profile is in the time domain) is the range between the top and bottom of the formation. According to the horizon data picked up from the stratum center, the curvature can be calculated, the folds can be found, and the positive and negative of the curvature (anisotropic and syncline properties) can be obtained, and the seismic trace—the central trace—where the core of the fold and the center of the fold is located can be determined. Calculate the amplitude difference on both sides of the center track point by point. In actual calculations, it is also based on image 3 Calculate the amplitude difference between the multiple channels in each direction of the central channel, and then according to the formula: Σ curvature sign × curvature × amplitude difference, the amount of slight folds in the central channel is obtained, and then the points in the formation are calculated to obtain the various points in the formation. The amount of folds at the point.
[0072] Repeat the above steps for each fold to get the final result of identifying all the micro-fold folds on the well track.
[0073] Figure 4 Implement this method for a section of seismic profile in a certain area to get an example of slight folds. The upper part of the figure is the original seismic section, and the lower part is the micro-fold detection section of the shale gas layer. The black line in the middle is the horizon line of the shale gas layer, and the colors above and below the horizon line are the comprehensive values ​​of the slight folds—including the amplitude difference, curvature magnitude and direction. The figure can clearly identify the slight folds and their differences, reflecting the changes in the strength of the folds.
[0074] Figure 5 It is the prediction map of the fracture and slight fold plane distribution of a shale gas layer in the Sichuan Basin in this area. The left picture is the fracture prediction result, the northern half is the fracture development area, and the right picture is the prediction distribution map of the slight fold, south The half area is the development area of ​​slight folds. The fracture prediction map shows that the lost circulation points in the northern half are all on the fractures, while the lost circulation points in the southern half have basically nothing to do with fractures. The microfold prediction map shows that the lost circulation points in the southern half are basically on the microfold axis, and they are all tension folds.
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

no PUM

Description & Claims & Application Information

We can also present the details of the Description, Claims and Application information to help users get a comprehensive understanding of the technical details of the patent, such as background art, summary of invention, brief description of drawings, description of embodiments, and other original content. On the other hand, users can also determine the specific scope of protection of the technology through the list of claims; as well as understand the changes in the life cycle of the technology with the presentation of the patent timeline. Login to view more.
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Similar technology patents

Video face identification algorithm on basis of multi-instance learning

ActiveCN104778457AImprove recognition accuracyAvoid difficult choicesCharacter and pattern recognitionInstance-based learningCharacteristic space
Owner:JILIN UNIV

Subway driver emotion identification method and device based on deep learning

ActiveCN108171176AImprove recognition accuracyEarly detection of human errorNeural architecturesAcquiring/recognising facial featuresDriving safetyNetwork model
Owner:CRRC IND INST CO LTD

System for monitoring boiler efficiency of thermal power station on line based on identification of calorific capacity of fire coal

InactiveCN102252784ASimple dataImprove recognition accuracyCalorimeterBoiler efficiencyAnalysis data
Owner:SHANGHAI JIAO TONG UNIV

Packet sampling and application signature based internet application flux identifying method

InactiveCN101184000AImprove recognition accuracyImprove processing efficiencyUser identity/authority verificationData switching networksTraffic volumePacket sampling
Owner:BEIJING JIAOTONG UNIV

Video frequency objects recognition method and system based on supporting vectors machine

ActiveCN101140623ACalculation speedImprove recognition accuracyCharacter and pattern recognitionImage resolutionDecision-making
Owner:HUAWEI TECH CO LTD

Classification and recommendation of technical efficacy words

  • Improve recognition accuracy
  • Improve recognition efficiency
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2023 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap