WO2017114443A1 - 一种确定储层岩溶发育程度的方法及装置 - Google Patents

一种确定储层岩溶发育程度的方法及装置 Download PDF

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WO2017114443A1
WO2017114443A1 PCT/CN2016/112856 CN2016112856W WO2017114443A1 WO 2017114443 A1 WO2017114443 A1 WO 2017114443A1 CN 2016112856 W CN2016112856 W CN 2016112856W WO 2017114443 A1 WO2017114443 A1 WO 2017114443A1
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karst
dissolution
feature
reservoir
horizontal
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French (fr)
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李宁
武宏亮
冯周
冯庆付
王克文
张晓涛
张宫
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Petrochina Co Ltd
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Petrochina Co Ltd
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Priority to US15/990,054 priority patent/US10641088B2/en
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E21B49/08—Obtaining fluid samples or testing fluids, in boreholes or wells
    • E21B49/087—Well testing, e.g. testing for reservoir productivity or formation parameters
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/002—Survey of boreholes or wells by visual inspection
    • E21B47/0025—Survey of boreholes or wells by visual inspection generating an image of the borehole wall using down-hole measurements, e.g. acoustic or electric
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/18—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V8/00—Prospecting or detecting by optical means
    • G01V8/02—Prospecting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/695—Preprocessing, e.g. image segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30184—Infrastructure

Definitions

  • the invention relates to a method and a device for determining the degree of reservoir karst development, and belongs to the technical field of logging interpretation.
  • Weathered crust karst reservoirs are one of the important types of carbonate oil and gas resources.
  • a large number of studies have shown that the key factor controlling the development and distribution of weathering crust karst reservoirs lies in the degree of karst development, and the formation of the original stratum by karstification.
  • the dissolution of the fracture cavity has an important influence on the reservoir performance and the improvement of the seepage capacity. Therefore, how to use the existing logging methods to accurately identify the weathered crust karst reservoir and divide the development degree of karstification can provide an important technical basis for the identification of reservoir effectiveness, the submission of oil and gas reserves and the development of oil and gas wells.
  • karst development degree is mainly based on core observation, and the karst holes and fracture development on the rock surface are qualitatively divided by reservoir coring or outcrop rock sampling.
  • This type of method has a high degree of dependence on the core, high cost and poor operability; in addition, the core obtained from the oil field is often discontinuous, and it is very difficult to complete the core in the fracture development layer, and the karst development characteristics observed on the core It is difficult to represent the overall situation of the reservoir, thus affecting the comprehensive judgment of the reservoir.
  • imaging logging combined with conventional logging technology to classify weathered crust karst reservoirs into longitudinally karst facies belts such as epigenetic karst zones, vertical seepage zones, and horizontal subsurface zones, and based on imaging measurements of different karst zones.
  • Well feature map and logging response characteristic model are used to qualitatively identify the karst zone, but the development degree of the specific karst zone of each karst zone is not divided and evaluated. Therefore, the application and interpretation of imaging logging data is still in qualitative analysis. At the stage, it cannot meet the needs of logging evaluation of weathering crust karst reservoirs.
  • the invention solves the problem that the existing karst development degree division method has strong multi-solution, poor operability, and can not meet the needs of logging evaluation of weathering crust karst reservoir, and proposes a method and device for determining the degree of reservoir karst development, Including the following technical solutions:
  • a method of determining the extent of reservoir karst development including:
  • a device for determining the extent of reservoir karst development comprising:
  • the feature and parameter extraction unit is configured to extract a vertical dissolution hole joint feature and a horizontal dissolution hole joint feature from the electrical image log image data of the to-be-determined reservoir and statistically log the feature parameter;
  • a development degree dividing unit configured to determine a karst facies zone to which the undetermined reservoir belongs according to the vertical dissolution hole joint feature, a horizontal dissolution hole joint feature, and a logging characteristic parameter, and divide the karst development degree;
  • a development degree determining unit configured to determine an effective reservoir development degree of the undetermined reservoir according to the karst facies and the karst development degree division result.
  • the invention has the beneficial effects that the vertical dissolution pore feature extracted from the electrical imaging log image data, the horizontal dissolution pore feature and the logging characteristic parameter are used to divide the karst development degree of the reservoir, which can be quickly and intuitively
  • the reservoir karst facies belt is identified and the karst development degree is divided.
  • the discriminant results are consistent with the core display, which provides important technical support for oil and gas field exploration and development.
  • Figure 1 is a flow chart showing, by way of example, a method of determining the extent of reservoir karst development.
  • FIG. 2 is a flow chart of a method for determining the degree of development of a reservoir karst as set forth in the first embodiment.
  • FIG. 3 is a flow chart of generating electrical imaging log image data for full wellbore coverage as set forth in the first embodiment.
  • FIG. 4 is a schematic diagram of static and dynamic full borehole image information obtained by processing the electrical imaging log data proposed in the first embodiment.
  • FIG. 5 is a schematic diagram showing the vertical dissolution hole seam, the horizontal dissolution hole boundary and the characteristic parameters of the electric imaging log image extracted in the first embodiment.
  • Fig. 6 is a schematic view showing the results of karst zone and karst development degree division according to the first embodiment.
  • Figure 7 is a structural view of the apparatus for determining the degree of reservoir karst development proposed in the second embodiment.
  • FIG. 8 is a structural diagram of an electronic device according to a third embodiment.
  • the existing methods cannot classify the development degree of the specific karstification of each karst zone of the weathered crust karst reservoir, so that the overall situation of the reservoir cannot be determined, and the log evaluation results of the reservoir are affected.
  • the applicants of this application found in the study of weathered crust karst reservoirs that the vertical seepage zone karstification is dominated by vertical dissolution and the vertical dissolution of the pores is developed; the horizontal landscaping karst is dominated by horizontal dissolution and developed layered. Dissolution hole; the degree of dissolution hole development reflects the degree of formation karst. Therefore, the development degree of reservoir karstification can be divided by identifying and extracting the characteristics of vertical seepage zone and horizontal subsurface flow zone on the electrical imaging log.
  • the method for determining the degree of reservoir karst development proposed in this embodiment includes:
  • step 11 the vertical dissolution hole joint feature and the horizontal dissolution hole joint feature are extracted from the electrical image log image data of the to-be-determined reservoir and the log characteristic parameters are statistically calculated.
  • the electrical imaging log data of the undetermined reservoir can be obtained by the existing electric imaging logging instrument, and the static imaging image and the dynamic image are obtained by processing the electrical imaging logging data by a predetermined processing method, and the interpolation is performed by a predetermined probability.
  • the processing method can obtain static imaging images and dynamic image processing to obtain electrical imaging log image data covered by the entire wellbore.
  • the vertical dissolution pore feature and the horizontal dissolution hole joint feature were quantitatively extracted from the image data of the electrical imaging log of the reservoir to be determined by image analysis method, and the characteristic parameters of the vertical dissolution hole and the horizontal dissolution hole in the unit depth range were counted.
  • the vertical dissolution hole joint feature and the horizontal dissolution hole joint feature can be extracted from the total wellbore covered electrical imaging log image data and the logging characteristic parameters can be statistically calculated.
  • the process of obtaining the vertical dissolution hole feature and the horizontal dissolution hole feature includes three parts: feature color, feature shape, and feature texture.
  • feature color based on the electrical imaging log image, the feature category, the feature shape and the feature texture are comprehensively analyzed to determine the category to which the feature belongs.
  • the vertical dissolution aperture and the horizontal dissolution aperture feature parameters include the aperture face ratio, the average particle size, and the number.
  • the face rate, average particle size and number of vertical dissolution pores and horizontal dissolution pores in the unit depth range are statistically calculated.
  • Step 12 Determine the karst facies zone to which the reservoir to be determined belongs and divide the karst development degree according to the vertical dissolution hole joint feature, the horizontal dissolution hole joint feature and the logging characteristic parameter.
  • the karst facies belt to which the reservoir to be determined belongs can be discriminated according to the characteristics of the vertical dissolution hole and the horizontal dissolution hole. If the face rate Ap V of the vertical dissolution hole is greater than the face rate Ap H of the horizontal dissolution hole, it can be determined. The corresponding undetermined reservoir is located in the vertical seepage karst zone, and vice versa in the horizontal subsurface karst zone.
  • the karst development index Kf V of the vertical seepage karst zone and the karst development index of the horizontal subsurface karst zone can be determined by using the face rate, particle size, quantity, formation porosity and karst thickness of the dissolved pores in the logging feature. Kf H.
  • the karst development degree can be directly divided.
  • step 13 the effective reservoir development degree of the undetermined reservoir is determined according to the karst facies and the karst development degree.
  • the effective reservoir development is determined.
  • the corresponding relationship between different karst facies and effective reservoirs is: the reservoir is developed in the vertical seepage zone, the general reservoir or the horizontal reservoir is developed in the horizontal subsurface zone.
  • the karst development level reaches the best or better level, the corresponding reservoir is a high-quality reservoir, and its reservoir productivity can reach industrial capacity; if the karst development degree is poor The level of the reservoir is poor and cannot meet the industrial capacity requirements.
  • the vertical dissolution pore joint feature, the horizontal dissolution pore joint feature and the logging characteristic parameter extracted from the electrical imaging log image data are used to divide the karst development degree of the fixed reservoir, and can be quickly, Visually identify the reservoir karst facies belt and classify the karst development degree.
  • the discriminant results are consistent with the core display, which provides important technical support for oil and gas field exploration and development.
  • the method for determining the degree of reservoir karst development proposed in this embodiment includes:
  • Step 21 acquiring logging data and geological logging data of the reservoir to be determined.
  • the logging data of the reservoir to be determined mainly includes the electrical imaging logging data of the target interval of the study area, and may also include other conventional logging data, geological data, logging data, and core description and analysis to facilitate the development of karst And the stratum horizon is comprehensively discriminated.
  • Step 22 generating electrical imaging log image data covered by the entire wellbore.
  • the electric imaging logging image data covered by the whole wellbore can be obtained by preprocessing, static enhancement, dynamic enhancement, and full borehole image generation processing of the electrical imaging logging data, and as shown in FIG. 3, the process may include :
  • Step 221 performing acceleration correction and equalization processing on the electrical imaging logging data of the fixed reservoir to obtain original electrical imaging logging image data;
  • Step 222 Perform static enhancement processing on the original electrical imaging log image data to obtain static image data reflecting the color change of the rock in the whole well segment;
  • Step 223 Perform dynamic enhancement processing on the original electrical imaging log image data to obtain dynamic image data reflecting the color change of the rock in the whole well segment;
  • Step 224 Perform full probability processing on the still image data and the uncovered portion of the moving image data by using the full borehole image processing method to obtain the electrical imaging log image data covered by the whole wellbore.
  • the static image data and the moving image data after the enhanced processing can be referred to the left part of the image in the left part of FIG. 4, and the still image data and the moving image data after the whole wellbore image processing. See the two images on the right side of the figure. It can be seen that through the whole wellbore image processing, the blank part in the original image can be effectively removed, and the image features are more complete and intuitive, which lays a foundation for the automatic recognition and extraction of image features in the later stage.
  • Step 23 extract vertical dissolution hole joint features and horizontal dissolution hole joint features from the electrical imaging log image data of the to-be-determined reservoir and statistically log the well characteristic parameters.
  • step 23 may also extract vertical dissolution aperture features and horizontal dissolution aperture features from the full borehole covered static image data and dynamic image data by existing image processing techniques.
  • a specific method of quantitatively extracting image features may include three parts: feature color, feature shape, and feature texture. Based on the image segmentation of the electrical imaging log, this step determines the category of each feature by comprehensively analyzing the feature color, feature shape and feature texture.
  • the feature color mainly refers to the size of the gray value in the pixel included in the feature object and its distribution, and is described by using the gray maximum value Gmax, the gray minimum value Gmin, and the gray average value Gavg.
  • the analysis of the feature form is mainly described by four parameters: a feature area A, a width-to-length ratio F, a roundness R, and a direction D.
  • the four parameters can be determined by the following methods:
  • the single pixel area of the imaging log image is a unit area, and the feature area A is the actual pixel number included in the feature object;
  • Direction D describes the extended orientation of the feature object, that is, the angle between the long axis direction of the feature object and the horizontal direction.
  • this step proposes to quantitatively calculate texture feature parameters such as texture second moment W M , contrast W C and uniformity W H using gray level co-occurrence matrix method, which can be determined by the following methods:
  • each element in the gray level co-occurrence matrix p of S is defined as:
  • the molecular portion on the right side of the equal sign indicates the number of element pairs having a spatial relationship f(x 1 , y 1 ) and the values are divided into g 1 and g 2 , and the denominator portion indicates the element pair in the S Number of (#S);
  • W M ⁇ g 1 ⁇ g 2 p 2 (g 1 ,g 2 ) (2)
  • the face rate Ap, the average particle size Gs and the number N of each dissolution hole in the depth range are counted.
  • the quantity N describes the degree of feature development, which is determined by the unit depth feature count accumulation
  • the face rate Ap describes the strength of the feature development and Where A i represents the area of the extracted feature object; S 0 represents the total area of the unit depth image of the statistics;
  • the average particle size Gs describes the average size of the features and can be described by the Feret diameter.
  • the single feature particle size Gs i can be averaged by measuring the diameter of the feature object in multiple directions, and the average particle size within the unit depth range can be determined as
  • Figure 5 shows the feature extraction effect and feature parameter statistics of this step.
  • the first to third partial images from the left represent the depth, the full borehole static image, and the dynamic image information.
  • the fourth partial image is extracted.
  • the vertical dissolution pores and the characteristic boundary of the horizontal dissolution pores; the fifth to seventh partial images are the statistically obtained face dissolution rate, average particle size and quantity parameters of the vertical dissolution pores and the horizontal dissolution pores, respectively, wherein the curve A indicates Vertically dissolve the aperture face rate, curve B represents the horizontal dissolution aperture face ratio, curve C represents the vertical average particle size, curve D represents the horizontal average particle size, curve E represents the vertical quantity parameter, and curve F represents the horizontal quantity parameter.
  • step 24 the karst facies zone to which the reservoir is to be determined is determined according to the vertical dissolution hole joint feature, the horizontal dissolution hole joint feature and the logging characteristic parameter, and the karst development degree is divided.
  • the type of the karst facies belt can be determined according to the vertical dissolution pore feature and the face rate of the horizontal dissolution hole joint feature. If the face rate Ap V of the vertical dissolution hole is greater than the face rate Ap H of the horizontal dissolution hole, the corresponding The stratum is located in the vertical seepage karst zone, and vice versa in the horizontal subsurface karst zone.
  • the karst development index Kf V of the vertical seepage karst zone and the karst development index Kf H of the horizontal subsurface karst zone determined by parameters such as face rate, particle size, quantity, formation porosity, and karst thickness of the dissolution hole are selected.
  • the degree of karst development is divided.
  • the vertical seepage karst zone karst development index Kf V can be determined by the following formula:
  • Kf V in the formula represents the development index of vertical seepage karst zone karst;
  • Ap V represents the vertical dissolution pore face ratio;
  • Gs V represents the average diameter of the vertical dissolution pore;
  • N V represents the number of vertical dissolution pores;
  • H V represents the determined total thickness of the vertical seepage karst zone;
  • a V , b V , c V , d V , and e V respectively represent the predetermined regional parameters, according to the vertical seepage zone of the reservoir to be determined
  • the overall karst development intensity and scale are determined.
  • the horizontal karst zone karst development index Kf H can be determined by the following formula:
  • Kf H represents the development index of karst in horizontal seepage karst zone
  • Ap H represents the horizontal dissolution of the face of the hole
  • Gs H represents the average particle size of the horizontal dissolution hole
  • N H represents the number of horizontal dissolution holes
  • H H represents the determined total thickness of the horizontal subsurface karst zone
  • a H , b H , c H , d H , and e H respectively represent the predetermined regional parameters, according to the horizontal subsurface flow zone of the reservoir to be determined
  • the overall karst development intensity and scale are determined.
  • the karst development degree can be divided.
  • the degree of formation karst development can be quantitatively divided into three grades:
  • Grade I karst vertical seepage karst zone Kf V ⁇ CPv I ; horizontal subsurface karst zone Kf H ⁇ CPh I ;
  • Grade II karst vertical seepage karst zone CPv II ⁇ Kf V ⁇ CPv I ; horizontal subsurface karst zone CPh II ⁇ Kf H ⁇ CPh I ;
  • Grade III karst vertical seepage karst zone Kf V ⁇ CPv II ; horizontal subsurface karst zone Kf H ⁇ CPh II .
  • CPv I , CPv II , CPh I and CPh II are the vertical seepage karst zone, the horizontal subsurface karst zone I and II karst development division criteria, according to the actual karst development degree of the region, usually 6.5. 3.5, 6.0, 3.0.
  • Figure 6 shows the karst facies and karst development degrees divided by karst characteristic parameters.
  • the curve a in the first partial image from the left represents the conventional natural gamma (GR)
  • the curve b represents the photoelectric absorption section index (PE)
  • the curve c represents the caliper (CAL)
  • the curve d in the second partial image represents the density (DEN)
  • curve e represents neutron (CNL)
  • curve f represents sound wave (AC)
  • curve g in the third partial image represents deep lateral resistivity (RD)
  • curve h represents shallow lateral resistivity (RS)
  • the fourth part is the depth
  • the fifth to the seventh part are the full borehole static image, the moving image information and the extracted vertical dissolution hole seam and the horizontal dissolution hole joint feature boundary
  • the curve i in the eighth part image The face rate of the vertical dissolution hole feature, the curve j represents the face rate of the horizontal dissolution hole feature;
  • the curve k in the ninth part image represents the particle size of
  • the curve n in the image of the tenth part represents the quantity parameter of the vertical dissolution hole joint feature
  • the curve o represents the quantity parameter of the horizontal dissolution hole joint feature
  • the curve p in the eleventh part image represents the porosity
  • the twelfth part image Karst facies belt
  • the result consists of three parts: the upper part is the karst top, the middle part is the vertical seepage zone, the lower part is the horizontal subsurface flow zone; the thirteenth part is the calculated karst development index, where the curve q represents vertical The karst development index of the seepage karst zone, the curve r indicates the karst development index of the horizontal submerged karst zone; the image of the fourteenth part is the grade of karst development according to the karst development index, the uppermost part is the grade I karst, the middle part is the grade II karst, the most The lower part is grade III karst; the fifteenth part of the image is the result of
  • the upper part of the well section is mainly characterized by vertical dissolution pores and is divided into vertical seepage karst zone; the lower part is horizontal subsurface karst zone.
  • the karst development index calculated by the comprehensive porosity and the identified karst zone development thickness indicates that the karst development degree of the vertical seepage karst zone is higher overall, and the karst development index is basically above 6.5, which can be classified as Grade I karst development;
  • the face rate and particle size of the upper to lower hole are reduced, the karst degree is gradually reduced, and the upper karst development index is distributed between 6-8, which can be classified as Grade I karst, and the lower karst development index is mainly distributed between 4-6.
  • step 25 the effective reservoir development degree of the undetermined reservoir is determined according to the karst facies belt and the degree of karst development degree.
  • the effective reservoir development is determined.
  • the 3954.0-3965.0m well section shown in Figure 6 is located in the upper part of the vertical seepage karst zone and the horizontal subsurface karst zone. The whole part belongs to the favorable reservoir development site.
  • the calculation of the karst development index value is large, indicating that the karst development degree is high.
  • Karst development degree is I The level is comprehensively determined as the industrial gas interval.
  • the present example was applied in the field of a well in a certain oil and gas field, which can quickly and intuitively identify the reservoir karst facies belt and classify the karst development degree.
  • the discriminant result is consistent with the core display, which provides important technical support for oil and gas field exploration and development.
  • a method for dividing the development degree of karstification of weathered crust karst reservoirs based on image extraction of electrical imaging logs is proposed and implemented.
  • the karst features are extracted directly from the image of electric imaging logs and the characteristic parameters are quantitatively calculated for karst development. Dividing, avoiding the multi-solution problem caused by the electrical imaging log description;
  • karst development index is constructed by characterizing the porosity, particle size, quantity, formation porosity and karst thickness of the dissolved pores. The corresponding relationship between the index and the karst development degree is established, and the storage is solved. Uncertainty in the evaluation of the degree of karst development;
  • karst development degree of karst reservoirs is divided into three grades, which is of great significance for the evaluation of logging reservoir quality, and has a significant effect in the field application of scheduled oil and gas fields.
  • This embodiment provides a device for determining the degree of development of a reservoir karst, as shown in FIG. 7, comprising:
  • the feature and parameter extraction unit 71 is configured to extract a vertical dissolution hole joint feature, a horizontal dissolution hole joint feature, and a logging characteristic parameter from the electrical imaging log image data of the to-be-determined reservoir;
  • the development degree dividing unit 72 is configured to determine a karst facies zone to be determined by the undetermined reservoir according to the vertical dissolution hole joint feature and the horizontal dissolution hole joint feature and statistical logging characteristic parameters, and divide the karst development degree;
  • the development degree determining unit 73 is configured to determine the effective reservoir development degree of the undetermined reservoir according to the karst facies and the karst development degree division result.
  • the feature and parameter extraction unit 71 can obtain the electrical imaging log image data of the to-be-determined reservoir by the existing electrical imaging logging method, and process the electrical imaging log image data by a predetermined image processing method to obtain a static image and dynamics.
  • the image is quantitatively extracted on the static image and the dynamic image to obtain a vertical dissolution hole feature and a horizontal dissolution hole feature of the electrical imaging log image data.
  • the development degree dividing unit 72 can determine the karst facies zone to which the reservoir to be determined belongs according to the characteristics of the vertical dissolution hole and the horizontal dissolution hole. If the face rate Ap V of the vertical dissolution hole is greater than the face rate Ap H of the horizontal dissolution hole, It can be determined that the corresponding undetermined reservoir is located in the vertical seepage karst zone, and vice versa in the horizontal subsurface karst zone. According to the determined vertical seepage karst zone karst development index Kf V and horizontal subsurface karst zone karst development index Kf H , the karst development degree can be directly divided. The larger the Kf V or Kf H value, the higher the formation karst development degree, Kf V or The smaller the Kf H value, the lower the karst development.
  • the development degree determining unit 73 can determine the effective reservoir development based on the karst facies and the karst development degree.
  • the corresponding relationship between different karst facies and effective reservoirs is: the reservoir is developed in the vertical seepage zone, the general reservoir or the horizontal reservoir is developed in the horizontal subsurface zone.
  • the karst development level reaches the best or better level, the corresponding reservoir is a high-quality reservoir, and its reservoir productivity can reach industrial capacity; if the karst development degree is poor The level of the reservoir is poor and cannot meet the industrial capacity requirements.
  • the vertical dissolution pore joint feature, the horizontal dissolution pore joint feature and the logging characteristic parameter extracted from the electrical imaging log image data are used to divide the karst development degree of the fixed reservoir, and can be quickly, Visually identify the reservoir karst facies belt and classify the karst development degree.
  • the discriminant results are consistent with the core display, which provides important technical support for oil and gas field exploration and development.
  • An embodiment of the present invention provides an electronic device including the device for determining the degree of karst development of a reservoir as described in Embodiment 2.
  • FIG. 8 is a schematic block diagram showing the system configuration of an electronic device 800 according to an embodiment of the present invention.
  • the electronic device 8 can include a central processing unit 100 and a memory 140; the memory 140 is coupled to the central processing unit 100.
  • the figure is exemplary; other types of structures may be used in addition to or in place of the structure to implement telecommunications functions or other functions.
  • the functionality of the device that determines the extent of reservoir karst development may be integrated into central processor 100.
  • the central processing unit 100 may be configured to: extract vertical dissolution hole joint features and horizontal dissolution hole joint features from the electrical imaging log image data of the undetermined reservoir and statistically log the feature parameters; according to the vertical dissolution hole joint feature
  • the horizontal dissolution pore feature and the logging characteristic parameter determine the karst facies zone to which the undetermined reservoir belongs and divide the karst development degree; determine the effectiveness of the undetermined reservoir according to the karst facies belt and the karst development degree division result The degree of reservoir development.
  • the means for determining the degree of reservoir karst development may be configured separately from the central processing unit 100, for example, a device that determines the degree of reservoir karst development may be configured as a chip coupled to the central processing unit 100, through a central processing unit. Control to achieve the function of the device that determines the extent of reservoir karst development.
  • the electronic device 800 may further include: a communication module 110, an input unit 120, an audio processing unit 130, a display 160, and a power source 170. It is worth noting that the electronic device 800 does not have to include a map. All of the components shown in FIG. 8; in addition, the electronic device 800 may also include components not shown in FIG. 8, and reference may be made to the prior art.
  • central processor 100 also sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device that receives input and controls various portions of electronic device 800. The operation of the part.
  • the memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable device.
  • the above-mentioned information related to the failure can be stored, and a program for executing the related information can be stored.
  • the central processing unit 100 can execute the program stored by the memory 140 to implement information storage or processing and the like.
  • the functions of other components are similar to those of the existing ones and will not be described here.
  • the various components of electronic device 800 may be implemented by special purpose hardware, firmware, software or a combination thereof without departing from the scope of the invention.
  • the embodiment of the present invention further provides a computer readable program, wherein when the program is executed in an electronic device, the program causes the computer to execute the method for determining the degree of reservoir karst development described in Embodiment 1 in the electronic device.
  • the embodiment of the present invention further provides a storage medium storing a computer readable program, wherein the computer readable program causes the computer to execute the method for determining the degree of reservoir karst development in the first embodiment described in the electronic device.

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Abstract

一种确定储层岩溶发育程度的方法，属于测井解释技术领域。所述方法包括：从待定储层的电成像测井图像数据中提取垂直溶蚀孔缝特征和水平溶蚀孔缝特征并统计测井特征参数；根据所述垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数确定所述待定储层所属的岩溶相带并划分岩溶发育程度；根据所述岩溶相带与所述岩溶发育程度划分结果确定所述待定储层的有效储层发育程度。还公开一种确定储层岩溶发育程度的装置。本方法和装置通过从电成像测井图像数据中提取的垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数对待定储层的岩溶发育程度进行划分，能够快速、直观地识别储层岩溶相带并划分出岩溶发育程度，判别结果与岩心显示一致，为油气田勘探开发提供了重要的技术支持。

Description

一种确定储层岩溶发育程度的方法及装置 技术领域
本发明涉及一种确定储层岩溶发育程度的方法及装置，属于测井解释技术领域。
背景技术
风化壳岩溶型油气藏是碳酸盐岩油气资源的重要类型之一，大量的研究表明控制风化壳岩溶型储层发育和分布的关键因素在于岩溶作用发育程度，通过岩溶作用对原地层改造形成溶蚀缝洞体，对储层储集性能及渗流能力提升具有重要影响。因此，如何利用现有测井方法准确识别风化壳岩溶型储层并划分岩溶作用发育程度，能够对储层有效性判别、油气储量上交及油气井开发改造措施制定提供重要的技术依据。
现有技术对岩溶发育程度评价主要以岩心观察描述为主，通过储层取心或露头岩石钻孔取样观察岩石表面岩溶孔、裂缝发育情况进行定性划分。这类方法对岩心依赖程度较高，成本较大，可操作性差；此外，由于油田现场获取的岩心往往不连续，并且在裂缝发育层段完整取心非常困难，岩心上观察到的岩溶发育特征难以代表储层整体情况，从而影响对储层综合判别。
虽然现有技术曾利用成像测井结合常规测井技术将风化壳岩溶储层从纵向上划分为表生岩溶带、垂直渗流带、水平潜流带等岩溶相带，并根据不同岩溶带的成像测井特征图版和测井响应特征模式对岩溶带进行定性识别，但没有对各岩溶带的具体岩溶作用的发育程度进行划分和评价，因而对成像测井资料的处理解释应用基本还停留在定性分析阶段，不能满足风化壳岩溶型储层测井评价的需要。
发明内容
本发明为解决现有岩溶发育程度划分方法存在的多解性强、操作性差、无法满足风化壳岩溶型储层测井评价需要的问题，提出了一种确定储层岩溶发育程度的方法及装置，具体包括如下的技术方案：
一种确定储层岩溶发育程度的方法，包括：
从待定储层的电成像测井图像数据中提取垂直溶蚀孔缝特征和水平溶蚀孔缝特征并统计测井特征参数；
根据所述垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数确定所述待定储层所属的岩溶相带并划分岩溶发育程度；
根据所述岩溶相带与所述岩溶发育程度划分结果确定所述待定储层的有效储层发育程度。
一种确定储层岩溶发育程度的装置，包括：
特征及参数提取单元，用于从待定储层的电成像测井图像数据中提取垂直溶蚀孔缝特征和水平溶蚀孔缝特征并统计测井特征参数；
发育程度划分单元，用于根据所述垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数确定所述待定储层所属的岩溶相带并划分岩溶发育程度；
发育程度确定单元，用于根据所述岩溶相带与所述岩溶发育程度划分结果确定所述待定储层的有效储层发育程度。
本发明的有益效果是：通过从电成像测井图像数据中提取的垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数对待定储层的岩溶发育程度进行划分，能够快速、直观地识别储层岩溶相带并划分出岩溶发育程度，判别结果与岩心显示一致，为油气田勘探开发提供了重要的技术支持。
附图说明
图1是以示例的方式示出了确定储层岩溶发育程度的方法的流程图。
图2是实施例一提出的确定储层岩溶发育程度的方法的流程图。
图3是实施例一提出的生成全井眼覆盖的电成像测井图像数据的流程图。
图4是实施例一提出的通过对电成像测井资料处理获得的静态及动态的全井眼图像信息示意图。
图5是实施例一提出的电成像测井图像提取的垂直溶蚀孔缝、水平溶蚀孔缝边界与特征参数示意图。
图6是实施例一提出的岩溶带及岩溶发育程度划分结果示意图。
图7是实施例二提出的确定储层岩溶发育程度的装置的结构图。
图8是实施例三提出的电子设备的结构图。
具体实施方式
在本领域中，由于现有方法无法对风化壳岩溶型储层各岩溶带的具体岩溶作用的发育程度进行划分，从而无法确定储层的整体情况，影响该类储层测井评价结果。而本申请的申请人在对风化壳岩溶型储层研究中发现：垂直渗流带岩溶作用以垂向溶蚀为主，发育垂直溶蚀孔缝；水平潜流带岩溶作用以水平溶蚀为主，发育层状溶蚀孔缝；溶蚀孔缝发育程度反映了地层岩溶作用程度。因此，可以通过在电成像测井图像上对垂直渗流带、水平潜流带溶蚀孔缝特征的识别和提取来划分储层岩溶作用发育程度。
结合图1所示，本实施例提出的确定储层岩溶发育程度的方法包括：
步骤11，从待定储层的电成像测井图像数据中提取垂直溶蚀孔缝特征和水平溶蚀孔缝特征并统计测井特征参数。
其中，通过已有的电成像测井仪器测量可以获得待定储层的电成像测井图像资料，并通过预定处理方法将电成像测井资料处理后获得静态图像和动态图像，通过预定的概率插值处理方法可以将静态图像和动态图像处理获得全井眼覆盖的电成像测井图像数据。
通过图像分析方法从待定储层的电成像测井图像数据中定量提取垂直溶蚀孔缝特征和水平溶蚀孔缝特征，并统计单位深度范围内垂直溶蚀孔缝和水平溶蚀孔缝的特征参数。
实施时，为了获得更多的特征及测井特征参数，还可从全井眼覆盖的电成像测井图像数据中提取垂直溶蚀孔缝特征和水平溶蚀孔缝特征并统计测井特征参数。
可选的，获得垂直溶蚀孔缝特征和水平溶蚀孔缝特征的过程包括三个部分：特征颜色、特征形态和特征纹理。本实施例在电成像测井图像的基础上，通过对特征颜色、特征形态和特征纹理综合分析后判定特征所属类别。
可选的，垂直溶蚀孔缝和水平溶蚀孔缝特征参数包括孔缝面孔率、平均粒径及数量。本实施例在图像特征提取的基础上，统计单位深度范围内垂直溶蚀孔缝和水平溶蚀孔缝特征的面孔率、平均粒径及数量。
步骤12，根据垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数确定待定储层所属的岩溶相带并划分岩溶发育程度。
其中，待定储层所属的岩溶相带可根据垂直溶蚀孔缝特征及水平溶蚀孔缝特征进行判别，若垂直溶蚀孔缝的面孔率ApV大于水平溶蚀孔缝的面孔率ApH，则可确定对应的待定储层位于垂直渗流岩溶带，反之则位于水平潜流岩溶带。
可选的，利用测井特征参中的溶蚀孔缝的面孔率、粒径、数量以及地层孔隙度和岩溶厚度等参数可确定垂直渗流岩溶带岩溶发育指数KfV以及水平潜流岩溶带岩溶发育指数KfH。
根据确定的垂直渗流岩溶带岩溶发育指数KfV与水平潜流岩溶带岩溶发育指数KfH即可直接划分岩溶发育程度，KfV或KfH值越大，表明地层岩溶发育程度越高，KfV或KfH值越小，则表明岩溶发育程度较低。
步骤13，根据岩溶相带与岩溶发育程度划分结果确定待定储层的有效储层发育程度。
根据岩溶相带和岩溶发育程度划分结果，确定有效储层发育情况。不同岩溶相带与有效储层之间的对应关系为：垂直渗流带发育好储层、水平潜流带发育一般储层或者差储层。在储层所处有利岩溶相带基础上，若岩溶发育程度达到最好或较好的级别，则对应储层为优质储层，其储层产能能够达到工业产能；若岩溶发育程度为较差的级别，则储层较差，不能达到工业产能要求。
采用本实施例提出的技术方案，通过从电成像测井图像数据中提取的垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数对待定储层的岩溶发育程度进行划分，能够快速、直观地识别储层岩溶相带并划分出岩溶发育程度，判别结果与岩心显示一致，为油气田勘探开发提供了重要的技术支持。
下面通过具体的实施例对本发明提出的技术方案进行详细说明：
实施例一
结合图2所示，本实施例提出的确定储层岩溶发育程度的方法包括：
步骤21，获取待定储层的测井数据及地质录井资料。
待定储层的测井数据主要包括研究区目的层段电成像测井资料，同时还可以包括其它常规测井资料、地质资料、录井资料以及岩心描述、分析等相关资料，以便对岩溶发育部位及地层层位进行综合判别。
步骤22，生成全井眼覆盖的电成像测井图像数据。
其中，通过对电成像测井资料进行预处理、静态增强、动态增强、全井眼图像生成处理后可获得全井眼覆盖的电成像测井图像数据，结合图3所示，其过程可以包括：
步骤221，对待定储层的电成像测井资料进行加速度校正及均衡化处理，获得原始电成像测井图像数据；
步骤222，对原始电成像测井图像数据进行静态增强处理，获得反映全井段岩石颜色变化的静态图像数据；
步骤223，对原始电成像测井图像数据进行动态增强处理，获得反映全井段岩石颜色变化的动态图像数据；
步骤224，采用全井眼图像处理方法对静态图像数据以及动态图像数据中的未覆盖部分进行概率插值处理，获得全井眼覆盖的电成像测井图像数据。
本步骤以某油气田A井为例，通过增强处理后的静态图像数据和动态图像数据可参考图4中的左侧两部分图像所示，全井眼图像处理后的静态图像数据和动态图像数据可参考图中的右侧两部分图像所示。由此可知，通过全井眼图像处理，能够有效去除原始图像中的空白部分，使图像特征更完整、直观，为后期图像特征自动识别和提取奠定了基础。
步骤23，从待定储层的电成像测井图像数据中提取垂直溶蚀孔缝特征和水平溶蚀孔缝特征并统计测井特征参数。
实施时，步骤23还可通过已有的图像处理技术从全井眼覆盖的静态图像数据和动态图像数据上提取垂直溶蚀孔缝特征以及水平溶蚀孔缝特征。定量提取图像特征的具体方法可以包括三个部分：特征颜色、特征形态和特征纹理。本步骤在电成像测井图像分割的基础上，通过对特征颜色、特征形态和特征纹理综合分析后确定各特征所属类别。
可选的，特征颜色主要指特征对象包含的像素点内灰度值大小及其分布，采用灰度最大值Gmax、灰度最小值Gmin和灰度均值Gavg进行描述。
可选的，特征形态的分析主要采用特征面积A、宽长比F、圆度R以及方向D四个参数进行描述，该四个参数可通过以下方法确定：
设成像测井图像单像素点面积为单位面积，特征面积A则为特征对象所包含的实际像素点数；
宽长比F描述了特征对象的细长程度，F＝W/L，其中L为对象最小外接矩形的长度，W为对象最小外接矩形的宽度；
圆度R描述了特征对象的似圆程度，R＝P2/A，其中P为对象周长，基于对象的边界点计算，A为特征面积；
方向D描述了特征对象延伸方位，即特征对象长轴方向与水平方向之间的夹角。
可选的，对特征纹理分析，本步骤提出利用灰度共生矩阵方法定量计算纹理二阶矩WM、对比度WC和均匀性WH等纹理特征参数进行描述，可通过以下方法确定：
对图像S，如果函数f(x1,y1)定义了两种参数之间的空间关系，则S的灰度共生矩阵p中各元素定义为：
Figure PCTCN2016112856-appb-000001
式(1)中，等号右侧的分子部分表示是具有空间关系f(x1,y1)且值分为g1和g2的元素对的个数，分母部分表示S中元素对总的个数(#S)；
则纹理二阶矩WM、对比度WC和均匀性WH参数计算公式可为：
WM＝∑g1∑g2p2(g1,g2)    (2)
WC＝∑g1∑g2|g1-g2|p(g1,g2)    (3)
Figure PCTCN2016112856-appb-000002
根据提取获得的垂直溶蚀孔缝特征以及水平溶蚀孔缝特征，统计单位深度范围内各溶蚀孔缝的面孔率Ap、平均粒径Gs及数量N。
其中，数量N描述了特征发育的程度，通过单位深度特征计数累加确定；
面孔率Ap描述了特征发育的强弱且
Figure PCTCN2016112856-appb-000003
其中，Ai表示提取出的特征对象的面积；S0表示统计的单位深度图像总面积；
平均粒径Gs描述了特征平均尺寸，可采用Feret直径描述。单个特征粒径Gsi可通过测量特征对象在多个方向上的直径取平均值，单位深度范围内的平均粒径可以确定为
Figure PCTCN2016112856-appb-000004
图5所示的是本步骤的特征提取效果及特征参数统计结果，其中左起第一至第三部分图像分别表示深度、全井眼静态图像、动态图像信息；第四部分图像为提取出的垂直溶蚀孔缝以及水平溶蚀孔缝的特征边界；第五至第七部分图像分别为统计得到的垂直溶蚀孔缝和水平溶蚀孔缝的面孔率、平均粒径及数量参数，其中的曲线A表示垂直溶蚀孔缝面孔率，曲线B表示水平溶蚀孔缝面孔率，曲线C表示垂直平均粒径，曲线D表示水平平均粒径，曲线E表示垂直数量参数，曲线F表示水平数量参数。
步骤24，根据垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数确定待定储层所属的岩溶相带并划分岩溶发育程度。
其中，岩溶相带所属的类型可根据垂直溶蚀孔缝特征及水平溶蚀孔缝特征的面孔率进行判别，若垂直溶蚀孔缝的面孔率ApV大于水平溶蚀孔缝的面孔率ApH，则对应地层位于垂直渗流岩溶带，反之则位于水平潜流岩溶带。
可选的，本实施例通过溶蚀孔缝的面孔率、粒径、数量以及地层孔隙度、岩溶厚度等参数确定的垂直渗流岩溶带岩溶发育指数KfV和水平潜流岩溶带岩溶发育指数KfH对岩溶发育程度进行划分。
其中，垂直渗流岩溶带岩溶发育指数KfV可采用下式确定：
Figure PCTCN2016112856-appb-000005
式中的KfV表示垂直渗流岩溶带岩溶的发育指数；ApV表示垂直溶蚀孔缝面孔率；GsV表示垂直溶蚀孔缝平均粒径；NV表示垂直溶蚀孔缝数量；
Figure PCTCN2016112856-appb-000006
表示对应地层常规测井计算孔隙度；HV表示确定的垂直渗流岩溶带总厚度；aV、bV、cV、dV、eV分别表示预定地区参数，根据待定储层的垂直渗流带整体岩溶发育强度和规模确定。
水平潜流岩溶带岩溶发育指数KfH可采用下式确定：
Figure PCTCN2016112856-appb-000007
其中，KfH表示水平渗流岩溶带岩溶的发育指数；ApH表示水平溶蚀孔缝面孔率；GsH表示水平溶蚀孔缝平均粒径；NH表示水平溶蚀孔缝数量；
Figure PCTCN2016112856-appb-000008
表示对应地层常规测井计算孔隙度；HH表示确定的水平潜流岩溶带总厚度；aH、bH、cH、dH、eH分别表示预定地区参数，根据待定储层的水平潜流带整体岩溶发育强度和规模确定。
根据确定的垂直渗流岩溶带岩溶发育指数KfV与水平潜流岩溶带岩溶发育指数KfH即可划分岩溶发育程度，KfV或KfH值越大，则表明地层岩溶发育程度越高，反之，则表明岩溶发育程度较低。可选的，根据岩溶发育指数可将地层岩溶发育程度定量划分为三个等级：
Ⅰ级岩溶：垂直渗流岩溶带KfV≥CPvI；水平潜流岩溶带KfH≥CPhI；
Ⅱ级岩溶：垂直渗流岩溶带CPvII≤KfV<CPvI；水平潜流岩溶带CPhII≤KfH<CPhI；
Ⅲ级岩溶：垂直渗流岩溶带KfV<CPvII；水平潜流岩溶带KfH<CPhII。
其中，CPvI、CPvII、CPhI、CPhII分别为垂直渗流岩溶带、水平潜流岩溶带Ⅰ级、Ⅱ级岩溶发育划分标准，根据地区实际岩溶发育程度取值，通常可取值为6.5、3.5、6.0、3.0。
例如，图6所示的是通过岩溶特征参数划分的岩溶相带及岩溶发育程度。其中，左起第一部分图像中的曲线a表示常规自然伽马(GR)，曲线b表示光电吸收截面指数(PE)，曲线c表示井径(CAL)；第二部分图像中的曲线d表示密度(DEN)，曲线e表示中子(CNL)，曲线f表示声波(AC)；第三部分图像中的曲线g表示深侧向电阻率(RD)，曲线h表示浅侧向电阻率(RS)；第四部分为深度；第五至第七部分图像分别为全井眼静态图像、动态图像信息及提取出的垂直溶蚀孔缝以及水平溶蚀孔缝特征边界；第八部分图像中的曲线i表示垂直溶蚀孔缝特征的面孔率，曲线j表示水平溶蚀孔缝特征的面孔率；第九部分图像中的曲线k表示垂直溶蚀孔缝特征的粒径，曲线m表示水平溶蚀孔缝特征的粒径；第十部分图像中的曲线n表示垂直溶蚀孔缝特征的数量参数，曲线o表示水平溶蚀孔缝特征的数量参数；第十一部分图像中的曲线p表示孔隙度；第十二部分图像为岩溶相带识别结果，该结果包括三个部分：最上部分为岩溶顶部，中间部分为垂直渗流带，最下部分为水平潜流带；第十三部分图像为计算得到的岩溶发育指数，其中的曲线q表示垂直渗流岩溶带岩溶发育指数，曲线r表示水平潜流岩溶带岩溶发育指数；第十四部分图像为根据岩溶发育指数划分的岩溶发育程度级别，最上部分为Ⅰ级岩溶，中间部分为Ⅱ级岩溶，最下部分为Ⅲ级岩溶；第十五部分图像为有效储层解释结果，其中的Ⅰ级岩溶和Ⅱ级岩溶能够达到工业产能的要求。
由图6可知，该井段上部以垂直溶蚀孔缝特征为主，划分为垂直渗流岩溶带；下部为水平潜流岩溶带。综合常规孔隙度、识别的岩溶带发育厚度计算岩溶发育指数表明，垂直渗流岩溶带岩溶发育程度整体较高，岩溶发育指数基本都在6.5以上，可划为Ⅰ级岩溶发育；水平潜流岩溶带从上部到下部孔缝面孔率、粒径变小，岩溶程度逐渐降低，上部岩溶发育指数分布在6-8之间，可划为Ⅰ级岩溶，下部岩溶发育指数主要分布在4-6之间，可划分为Ⅱ级。
步骤25，根据岩溶相带与岩溶发育程度划分结果确定待定储层的有效储层发育程度。
根据岩溶相带和岩溶发育程度划分结果，确定有效储层发育情况。如图6中所示的3954.0-3965.0m井段位于垂直渗流岩溶带和水平潜流岩溶带上部，整体属于有利储层发育部位，同时该段计算岩溶发育指数值较大，表明岩溶发育程度高，岩溶发育程度为Ⅰ 级，综合判定为工业气层段。
将本实施例在某油气田A井进行了现场应用，能够快速、直观识别储层岩溶相带并划分出岩溶发育程度，判别结果与岩心显示一致，为油气田勘探开发提供了重要的技术支持。
本实施例提出的确定储层岩溶发育程度的方法具有以下的优点：
1)提出并实现了基于电成像测井图像特征提取的风化壳岩溶型储层岩溶作用发育程度的划分方法，直接从电成像测井图像上提取岩溶特征并定量计算其特征参数进行岩溶发育程度划分，避免了电成像测井描述产生的多解性问题；
2)通过表征溶蚀孔缝特征的孔率、粒径、数量以及地层孔隙度、岩溶厚度等参数构建了岩溶发育指数定量计算方法，建立了该指数与岩溶发育程度明确的对应关系，解决了储层岩溶发育程度评价的不确定问题；
3)提出将岩溶储层岩溶发育程度划分为三个等级，对于测井储层品质评价具有重要意义，在预定油气田现场应用中效果显著。
实施例二
本实施例提供了一种确定储层岩溶发育程度的装置，结合图7所示，包括：
特征及参数提取单元71，用于从待定储层的电成像测井图像数据中提取垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数；
发育程度划分单元72，用于根据垂直溶蚀孔缝特征和水平溶蚀孔缝特征并统计测井特征参数确定待定储层所属的岩溶相带并划分岩溶发育程度；
发育程度确定单元73，用于根据岩溶相带与岩溶发育程度划分结果确定待定储层的有效储层发育程度。
特征及参数提取单元71可通过已有的电成像测井方法获得待定储层的电成像测井图像数据，并通过预定图像处理方法将该电成像测井图像数据进行处理后获得静态图像和动态图像，在该静态图像和动态图像上定量提取获得电成像测井图像数据的垂直溶蚀孔缝特征和水平溶蚀孔缝特征。
发育程度划分单元72可根据垂直溶蚀孔缝特征及水平溶蚀孔缝特征判别待定储层所属的岩溶相带，若垂直溶蚀孔缝的面孔率ApV大于水平溶蚀孔缝的面孔率ApH，则可确定对应的待定储层位于垂直渗流岩溶带，反之则位于水平潜流岩溶带。根据确定的垂直渗流岩溶带岩溶发育指数KfV与水平潜流岩溶带岩溶发育指数KfH即可直接划分岩溶发 育程度，KfV或KfH值越大，表明地层岩溶发育程度越高，KfV或KfH值越小，则表明岩溶发育程度较低。
发育程度确定单元73根据岩溶相带和岩溶发育程度划分结果，能够确定有效储层发育情况。不同岩溶相带与有效储层之间的对应关系为：垂直渗流带发育好储层、水平潜流带发育一般储层或者差储层。在储层所处有利岩溶相带基础上，若岩溶发育程度达到最好或较好的级别，则对应储层为优质储层，其储层产能能够达到工业产能；若岩溶发育程度为较差的级别，则储层较差，不能达到工业产能要求。
采用本实施例提出的技术方案，通过从电成像测井图像数据中提取的垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数对待定储层的岩溶发育程度进行划分，能够快速、直观地识别储层岩溶相带并划分出岩溶发育程度，判别结果与岩心显示一致，为油气田勘探开发提供了重要的技术支持。
实施例三
本发明实施例提供一种电子设备，该电子设备包括如实施例二所述的确定储层岩溶发育程度的装置。
图8是本发明实施例的电子设备800的系统构成的一示意框图。如图8所示，该电子设备8可以包括中央处理器100和存储器140；存储器140耦合到中央处理器100。值得注意的是，该图是示例性的；还可以使用其他类型的结构，来补充或代替该结构，以实现电信功能或其他功能。
在一个实施方式中，确定储层岩溶发育程度的装置的功能可以被集成到中央处理器100中。其中，中央处理器100可以被配置为：从待定储层的电成像测井图像数据中提取垂直溶蚀孔缝特征和水平溶蚀孔缝特征并统计测井特征参数；根据所述垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数确定所述待定储层所属的岩溶相带并划分岩溶发育程度；根据所述岩溶相带与所述岩溶发育程度划分结果确定所述待定储层的有效储层发育程度。
在另一个实施方式中，确定储层岩溶发育程度的装置可以与中央处理器100分开配置，例如可以将确定储层岩溶发育程度的装置配置为与中央处理器100连接的芯片，通过中央处理器的控制来实现确定储层岩溶发育程度的装置的功能。
如图8所示，该电子设备800还可以包括：通信模块110、输入单元120、音频处理单元130、显示器160、电源170。值得注意的是，电子设备800也并不是必须要包括图 8中所示的所有部件；此外，电子设备800还可以包括图8中没有示出的部件，可以参考现有技术。
如图8所示，中央处理器100有时也称为控制器或操作控件，可以包括微处理器或其他处理器装置和/或逻辑装置，该中央处理器100接收输入并控制电子设备800的各个部件的操作。
其中，存储器140，例如可以是缓存器、闪存、硬驱、可移动介质、易失性存储器、非易失性存储器或其它合适装置中的一种或更多种。可储存上述与失败有关的信息，此外还可存储执行有关信息的程序。并且中央处理器100可执行该存储器140存储的该程序，以实现信息存储或处理等。其他部件的功能与现有类似，此处不再赘述。电子设备800的各部件可以通过专用硬件、固件、软件或其结合来实现，而不偏离本发明的范围。
本发明实施例还提供一种计算机可读程序，其中当电子设备中执行所述程序时，所述程序使得计算机在电子设备中执行实施例一所述的确定储层岩溶发育程度的方法。
本发明实施例还提供一种存储有计算机可读程序的存储介质，其中所述计算机可读程序使得计算机在电子设备中执行实施例一所述的确定储层岩溶发育程度的方法。
本具体实施方式是对本发明的技术方案进行清楚、完整地描述，其中的实施例仅仅是本发明的一部分实施例，而并不是全部的实施例。基于本发明中的实施例，本领域技术人员在没有经过创造性劳动的前提下所获得的所有其它实施方式都属于本发明的保护范围。

Claims (11)

  1. 一种确定储层岩溶发育程度的方法，其中，包括：
    从待定储层的电成像测井图像数据中提取垂直溶蚀孔缝特征和水平溶蚀孔缝特征并统计测井特征参数；
    根据所述垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数确定所述待定储层所属的岩溶相带并划分岩溶发育程度；
    根据所述岩溶相带与所述岩溶发育程度划分结果确定所述待定储层的有效储层发育程度。
  2. 如权利要求1所述的方法，其中,从待定储层的电成像测井图像数据中提取垂直溶蚀孔缝特征和水平溶蚀孔缝特征并统计测井特征参数进一步包括：
    根据待定储层的电成像测井图像数据获得全井眼覆盖的电成像测井图像数据；
    从全井眼覆盖的电成像测井图像数据中提取垂直溶蚀孔缝特征和水平溶蚀孔缝特征并统计测井特征参数。
  3. 如权利要求2所述的方法，其中，根据待定储层的电成像测井图像数据得到全井眼覆盖的电成像测井图像数据包括：
    对待定储层的电成像测井图像数据进行加速度校正及均衡化处理，获得原始电成像测井图像数据；
    对所述原始电成像测井图像数据进行静态增强处理，获得反映全井段岩石颜色变化的静态图像数据；
    对所述原始电成像测井图像数据进行动态增强处理，获得反映全井段岩石颜色变化的动态图像数据；
    采用全井眼图像处理方法对所述静态图像数据以及所述动态图像数据中的未覆盖部分进行概率插值处理，获得全井眼覆盖的电成像测井图像数据。
  4. 如权利要求1所述的方法，其中，对所述垂直溶蚀孔缝特征和水平溶蚀孔缝特征的提取包括对特征颜色、特征形态和特征纹理的提取。
  5. 如权利要求4所述的方法，其中，所述电成像测井图像信息的特征形态包括：特征面积、宽长比、圆度以及方向；所述特征面积为特征对象所包含的实际像素点数；所述宽长比为特征对象的最小外接矩形的宽度与最小外接矩形的长度的比值；所述圆度为特征对象的2倍周长与所述特征面积的比值；所述方向为特征对象在长轴方向与水平方向之间的夹角。
  6. 如权利要求4所述的方法，其中，所述电成像测井图像信息的特征纹理包括：通过灰度共生矩阵方法获得特征对象的纹理二阶距、对比度和均匀性。
  7. 如权利要求1所述的方法，其中，所述测井特征参数包括：溶蚀孔缝的面孔率、粒径、数量以及地层孔隙度和岩溶厚度。
  8. 如权利要求7所述的方法，其中，根据所述垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数确定所述待定储层所属的岩溶相带并划分岩溶发育程度包括：
    根据所述测井特征参数确定所述垂直渗流岩溶带岩溶的发育指数以及所述水平潜流岩溶带岩溶的发育指数；
    根据所述垂直渗流岩溶带岩溶的发育指数以及所述水平潜流岩溶带岩溶的发育指数对所述待定储层的岩溶发育程度进行划分。
  9. 如权利要求8所述的方法，其中，所述垂直渗流岩溶带岩溶的发育指数通过以下公式确定：
    Figure PCTCN2016112856-appb-100001
    其中，KfV表示垂直渗流岩溶带岩溶的发育指数；ApV表示垂直溶蚀孔缝面孔率；GsV表示垂直溶蚀孔缝平均粒径；NV表示垂直溶蚀孔缝数量；
    Figure PCTCN2016112856-appb-100002
    表示对应地层常规测井计算孔隙度；HV表示确定的垂直渗流岩溶带总厚度；aV、bV、cV、dV、eV分别表示预定地区参数。
  10. 如权利要求8所述的方法，其中，所述水平渗流岩溶带岩溶的发育指数通过以下公式确定：
    Figure PCTCN2016112856-appb-100003
    其中，KfH表示水平渗流岩溶带岩溶的发育指数；ApH表示水平溶蚀孔缝面孔率；GsH表示水平溶蚀孔缝平均粒径；NH表示水平溶蚀孔缝数量；
    Figure PCTCN2016112856-appb-100004
    表示对应地层常规测井计算孔隙度；HH表示确定的水平潜流岩溶带总厚度；aH、bH、cH、dH、eH分别表示预定地区参数。
  11. 一种确定储层岩溶发育程度的装置，其中，包括：
    特征及参数提取单元，用于从待定储层的电成像测井图像数据中提取垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数；
    发育程度划分单元，用于根据所述垂直溶蚀孔缝特征、水平溶蚀孔缝特征及测井特征参数确定所述待定储层所属的岩溶相带并划分岩溶发育程度；
    发育程度确定单元，用于根据所述岩溶相带与所述岩溶发育程度划分结果确定所述待定储层的有效储层发育程度。
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