CN109061763B - Comprehensive evaluation method for carbonate rock solution reservoir cave logging - Google Patents
Comprehensive evaluation method for carbonate rock solution reservoir cave logging Download PDFInfo
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Abstract
The invention discloses a comprehensive evaluation method for carbonate rock dissolved oil reservoir cave logging, wherein a logging comprehensive evaluation system main control module judges the type of a reservoir through calling reservoir type identification modules such as caves and the like, calls a cave filling material cause type identification module to judge the filling material cause type, calls a cave filling material type judgment module to judge the filling material lithologic type, and calls a cave filling degree evaluation module to calculate the cave filling degree. The invention has the beneficial effects that: from the logging response characteristics of each reservoir type, sensitive logging types are researched, judgment standards are summarized, sensitive indication parameters are provided, a software module is formed, and a reservoir type indication curve is output; the comprehensive characterization content of the cave is comprehensive, the qualitative judgment of the cause type and the lithologic type of the cave filling material is included, the filling degree of the semi-quantitative evaluation is included, and the comprehensive evaluation of the carbonate rock solution reservoir cave can be realized.
Description
Technical Field
The invention relates to the field of petroleum exploration, in particular to a comprehensive evaluation method for carbonate rock solution reservoir cave logging.
Background
1. Prior art solutions
Carbonate rock hydrosolve is a new reservoir type discovered in recent years, and is mainly formed by controlled-fracture corrosion. According to characteristic analysis, the carbonate rock solution reservoir has various spreading forms, 3 types including a strip shape, a sandwich cake shape and a flat plate shape, a reservoir space is complex, and reservoir types are various. The fracture-cavity carbonate reservoir space mainly comprises matrix pores (erosion holes), cracks, caves and the like, and the carbonate stratum can be divided into large-scale caves and small-scale fracture-cavity reservoir bodies according to different scales. According to different allocation modes of reservoir spaces, the fracture-cavity carbonate reservoir is divided into 5 types on the reservoir type, namely, fracture, hole, fracture-hole, unfilled hole and filled hole. The sources of the filling materials are obviously different for filling the cavities, and the filling degree is also obviously different.
The current research progress generally aims at the judgment of the type of the lithology of the cave filling, and the adopted method is mainly a cross plot method.
2. Disadvantages of the prior art
The existing comprehensive evaluation technology is mainly limited to cave identification and filling lithology type interpretation, and the adopted method only adopts a cross plot method and a judgment standard thereof for analysis, is not explained from the cause of the filling, and has low recognition and quantification degree of the filling degree.
Disclosure of Invention
The invention relates to a comprehensive evaluation method for carbonate rock dissolved-water reservoir cave logging, which is designed for improving the accuracy of logging interpretation and providing basic data for geophysical and geological analysis.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a carbonate rock dissolution reservoir cave logging comprehensive evaluation method is characterized in that a logging comprehensive evaluation system main control module judges reservoir types by calling reservoir type identification modules such as caves and the like, judges filling material cause types by calling a cave filling material cause type identification module, judges filling material lithology types by calling a cave filling material type judgment module, and calculates filling degree by calling a cave filling degree evaluation module; the method comprises the following specific steps:
firstly, judging the type of a reservoir; inputting logging data, and judging the type of a reservoir according to a deep lateral resistivity-natural gamma intersection diagram and fracture porosity constraint; inputting natural gamma GR, deep lateral resistivity RD and fracture porosity PORF curves into the module, outputting a reservoir type discrimination indication curve CAVE _2, and identifying a CAVE according to the characteristics of the output curve;
secondly, judging the cause type of the cave filling material; inputting logging data, calculating the shale content, and judging the cause type of the filling according to a shale content-shallow lateral intersection graph and a judgment standard thereof; inputting shallow lateral resistivity RS, density DEN, argillaceous content SH and reservoir type discrimination indication CAVE _2 curves in a module, outputting a Filler curve, and judging the filling cause type according to the characteristics of the output curve;
thirdly, judging the lithology type of the cave filling material; inputting logging data, and judging the lithology type of the filling material according to the 6 intersection maps and the judgment standard thereof; inputting shallow lateral resistivity RS, deep lateral resistivity RD, natural gamma GR, density DEN, acoustic wave time difference AC and neutron CNL into the module, outputting a packing curve, and judging the lithology type of the filler according to the characteristics of the output curve;
fourthly, calculating the cave filling degree; inputting logging data, calculating the relative gamma content, calculating a filling degree indicating curve according to a corresponding formula, and obtaining the comprehensive judgment of the filling degree through a filling degree dividing standard; KTH, GR and CAVE _2 curves are input into the filling degree calculation module, VUG1 and VUG2 curves are output, and VUG1 and VUG2 curves are input into the filling degree type judgment module, and VUGt curves are output; and comprehensively judging the total filling degree of a certain cave according to the output filling degree indication curve.
According to the comprehensive evaluation method for the carbonate rock solution reservoir cave well logging, the judgment indication of the output cave in the first step is realized on the basis of establishing the judgment standards of different reservoir spaces; the method comprises the steps that logging response characteristics of a reservoir space of a fracture-cavity reservoir body are different along with different reservoir space types, and GR-RS and AC-CNL cross maps are established through a dual-lateral resistivity, natural gamma, compensated neutron and acoustic wave time difference logging method; the shallow lateral resistivity of the cave in the GR-RS intersection is obviously different from the natural gamma, the sound wave and neutron logging of the cave in the AC-CNL intersection are also different from other reservoir types, and the two intersection maps are used for establishing the discrimination standards of different reservoir spaces.
According to the comprehensive evaluation method for the carbonate rock dissolved oil reservoir cave well logging, cave and other reservoir type identification modules perform cave discrimination and comprehensive analysis by using an RBF neural network algorithm; the RBF is a three-layer forward network with a single hidden layer, wherein the first layer is an input layer and consists of logging data sensitive to the type of a reservoir; the second layer is a hidden layer, the transformation function of the neurons in the hidden layer, namely the radial basis function, is a nonnegative linear function which is radially symmetrical and attenuated to the central point, and the function is a local response function and is superior to the original global response function; the third layer is the output layer, which responds to the input data, and here, the judgment of the output reservoir type, especially the judgment of the cave.
The method for comprehensively evaluating the carbonate rock dissolved oil reservoir cave logging comprises the following steps of: the filling materials with different causes have different logging response characteristics, wherein the depth double lateral resistivity and the natural gamma logging response are sensitive, the corresponding shale content is greatly different, and the shale content is obtained by the natural gamma, so that intersection graphs of different cause types of the cave filling materials are constructed; and constructing a cave filling cause type identification boundary by utilizing Vsh and shallow lateral resistivity, wherein the expressions are respectively as follows:
the method for comprehensively evaluating the carbonate rock dissolved oil reservoir cave logging comprises the following steps of: different lithologic fillings have different logging response characteristics, and 6 different cave filling cause type identification charts are respectively constructed by combining the natural gamma GR, the deep lateral resistivity RD, the shallow lateral resistivity RS, the acoustic wave time difference AC, the neutron CNL and the density DEN according to the logging response characteristics of 5 lithologic fillings including mud, sand, glutenite and calcite, so that the interpretation standard of cave filling lithologic identification is established.
According to the comprehensive evaluation method for the carbonate rock dissolved oil reservoir well logging, the cavern in the research area is divided into three situations of unfilled, half-filled and full-filled according to the filling degree in the cavern.
The method for comprehensively evaluating the logging of the carbonate rock dissolved oil reservoir cave comprises the following steps of:
firstly, performing pairwise intersection by using natural gamma GR, deep lateral resistivity RD, shallow lateral resistivity RS, neutron CNL, density DEN and acoustic wave time difference AC, and respectively establishing explanation charts of different filling degrees of the cave;
secondly, preferably selecting the natural gamma GR as a sensitive parameter reflecting different filling degrees of the cave, and calculating the filling degree of the cave; defining a parameter IVUGThe method is called as a natural gamma relative value, and quantitatively characterizes the cavern filling degree:
wherein X is a natural gamma curve value, XminIs a natural gamma limestone base line value, XmaxFilling the maximum gamma value of the cave for the pure mudstone;
finally, establishing a cave filling degree explanation plate and a standard; the higher the filling degree of the cavern, IVUGThe value is increased, and by using the parameter and the shallow lateral resistivity, a template for explaining the filling degree of the cave can be obtained, and an indication standard of different filling degrees of the cave is established.
Through the introduction of comprehensive well logging evaluation to the carbonate rock solution-breaking oil reservoir cave, the method has the advantages that: (1) the evaluation content is comprehensive and comprises the evaluation of the cavern filling degree and the evaluation of the filling type, and the filling type comprises the lithology type evaluation and the identification of the cause type. (2) Sensitive logging data are optimized according to requirements of cave filling lithology type, cause type and filling degree evaluation on a research method logging response mechanism, and theoretical method basis is embodied. (3) And an RBF algorithm and a fracture evaluation result are introduced into the reservoir type and cave discrimination for comprehensive identification, so that the advancement of the method technology is reflected. (4) Aiming at the method, a data processing module is compiled, and the requirements of large-scale processing and application can be met.
The invention has the beneficial effects that: and starting from the logging response characteristics of each reservoir type, researching the sensitive logging type, summarizing the judgment standard, providing a sensitive indication parameter, forming a software module and outputting a reservoir type indication curve. The comprehensive characterization content of the cave is comprehensive, the qualitative cave filling cause type and lithology type judgment is included, the semi-quantitative cave filling degree evaluation is included, and the comprehensive evaluation of the carbonate rock solution reservoir cave can be realized.
Drawings
FIG. 1 is a diagram of GR-RS intersections of caverns with other reservoir type identifications.
FIG. 2 is a diagram of AC-CNL intersections of caverns with other reservoir type identifications.
FIG. 3 is a plot of cavern fill cause type Vsh-RS intersection.
FIG. 4 is a DEN-RS intersection diagram for determining the lithology of the cave filling.
FIG. 5 is a GR-RS intersection of cavern fill lithology determinations.
Fig. 6 is a cavern filling level identification chart.
FIG. 7 is a diagram of an example of Z-well cave filling and filling degree logging application.
Fig. 8 is a functional block diagram of the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
The invention relates to a comprehensive evaluation method for carbonate rock dissolved oil reservoir cave logging, which is characterized in that a logging comprehensive evaluation system main control module judges the type of a reservoir by calling reservoir type identification modules such as caves and the like, calls a cave filling material cause type identification module to judge the filling material cause type, calls a cave filling material type judgment module to judge the filling material lithology type, and calls a cave filling degree evaluation module to calculate the filling degree; the method comprises the following specific steps:
firstly, judging the type of a reservoir; inputting logging data, and judging the type of a reservoir according to a deep lateral resistivity-natural gamma intersection diagram and fracture porosity constraint; inputting natural gamma GR, deep lateral resistivity RD and fracture porosity PORF curves into the module, outputting a reservoir type discrimination indication curve CAVE _2, and identifying a CAVE according to the characteristics of the output curve;
secondly, judging the cause type of the cave filling material; inputting logging data, calculating the shale content, and judging the cause type of the filling according to a shale content-shallow lateral intersection graph and a judgment standard thereof; inputting shallow lateral resistivity RS, density DEN, argillaceous content SH and reservoir type discrimination indication CAVE _2 curves in a module, outputting a Filler curve, and judging the filling cause type according to the characteristics of the output curve;
thirdly, judging the lithology type of the cave filling material; inputting logging data, and judging the lithology type of the filling material according to the 6 intersection maps and the judgment standard thereof; inputting shallow lateral resistivity RS, deep lateral resistivity RD, natural gamma GR, density DEN, acoustic wave time difference AC and neutron CNL in the module, outputting a packing curve, and judging the lithology type of the filler according to the characteristics of the output curve;
fourthly, calculating the cave filling degree; inputting logging data, calculating the relative gamma content, calculating a filling degree indicating curve according to a corresponding formula, and obtaining the comprehensive judgment of the filling degree through a filling degree dividing standard; KTH, GR and CAVE _2 curves are input into the filling degree calculation module, VUG1 and VUG2 curves are output, and VUG1 and VUG2 curves are input into the filling degree type judgment module, and VUGt curves are output; and comprehensively judging the total filling degree of a certain cave according to the output filling degree indication curve.
According to the comprehensive evaluation method for the carbonate rock solution reservoir cave well logging, the judgment indication of the output cave in the first step is realized on the basis of establishing the judgment standards of different reservoir spaces; the method comprises the steps that logging response characteristics of a reservoir space of a fracture-cavity reservoir body are different along with different reservoir space types, and GR-RS and AC-CNL cross maps are established through a dual-lateral resistivity, natural gamma, compensated neutron and acoustic wave time difference logging method; the shallow lateral resistivity of the cave in the GR-RS intersection is obviously different from the natural gamma, the sound wave and neutron logging of the cave in the AC-CNL intersection are also different from other reservoir types, and the two intersection maps are used for establishing the discrimination standards of different reservoir spaces.
According to the comprehensive evaluation method for the carbonate rock dissolved oil reservoir cave well logging, cave and other reservoir type identification modules perform cave discrimination and comprehensive analysis by using an RBF neural network algorithm; the RBF is a three-layer forward network with a single hidden layer, wherein the first layer is an input layer and consists of logging data sensitive to the type of a reservoir; the second layer is a hidden layer, the transformation function of the neurons in the hidden layer, namely the radial basis function, is a nonnegative linear function which is radially symmetrical and attenuated to the central point, and the function is a local response function and is superior to the original global response function; the third layer is the output layer, which responds to the input data, and here, the judgment of the output reservoir type, especially the judgment of the cave.
The method for comprehensively evaluating the carbonate rock dissolved oil reservoir cave logging comprises the following steps of: the filling materials with different causes have different logging response characteristics, wherein the depth double lateral resistivity and the natural gamma logging response are sensitive, the corresponding shale content is greatly different, and the shale content is obtained by the natural gamma, so that intersection graphs of different cause types of the cave filling materials are constructed; and constructing a cave filling cause type identification boundary by utilizing Vsh and shallow lateral resistivity, wherein the expressions are respectively as follows:
the method for comprehensively evaluating the carbonate rock dissolved oil reservoir cave logging comprises the following steps of: different lithologic fillings have different logging response characteristics, and 6 different cave filling cause type identification charts are respectively constructed by combining the natural gamma GR, the deep lateral resistivity RD, the shallow lateral resistivity RS, the acoustic wave time difference AC, the neutron CNL and the density DEN according to the logging response characteristics of 5 lithologic fillings including mud, sand, glutenite and calcite, so that the interpretation standard of cave filling lithologic identification is established.
According to the comprehensive evaluation method for the carbonate rock dissolved oil reservoir well logging, the cavern in the research area is divided into three situations of unfilled, half-filled and full-filled according to the filling degree in the cavern.
The method for comprehensively evaluating the logging of the carbonate rock dissolved oil reservoir cave comprises the following steps of:
firstly, performing pairwise intersection by using natural gamma GR, deep lateral resistivity RD, shallow lateral resistivity RS, neutron CNL, density DEN and acoustic wave time difference AC, and respectively establishing explanation charts of different filling degrees of the cave;
secondly, preferably selecting the natural gamma GR as a sensitive parameter reflecting different filling degrees of the cave, and calculating the filling degree of the cave; defining a parameter IVUGThe method is called as a natural gamma relative value, and quantitatively characterizes the cavern filling degree:
wherein X is a natural gamma curve value, XminIs a natural gamma limestone base line value, XmaxFilling the maximum gamma value of the cave for the pure mudstone;
finally, establishing a cave filling degree explanation plate and a standard; the higher the filling degree of the cavern, IVUGThe value is increased, and by using the parameter and the shallow lateral resistivity, a template for explaining the filling degree of the cave can be obtained, and an indication standard of different filling degrees of the cave is established.
And combining the logging response mechanism and the actual logging response of different reservoir types of the carbonate rock, realizing the explanation and the fine characterization of the cavern reservoir based on logging data, and forming a processing module. The specific embodiment is as follows:
identification of cave
(1) Well-log cross plot and sensitivity study
Fracture-cavity reservoir log response characteristics vary from reservoir type to reservoir type. The more sensitive logging methods include dual lateral resistivity, natural gamma, compensated neutron and sonic moveout. As shown in FIG. 1, the GR-RS intersection shows a significant difference between the shallow lateral resistivity of the cavity and the natural gamma; FIG. 2 is an AC-CNL cross-plot, and sonic logging and neutron logging of a cavern are also different from other logging. Cavernous reservoirs can be easily distinguished by using two intersection maps, and the GR-RS intersection map has better effect than the AC-CNL intersection map. Criteria for discriminating between different reservoir spaces are established using two intersection maps.
TABLE 1 identification of different reservoir types for fracture-cavern carbonate rock
| Reservoir volume space type | GR(API) | RD(Ω·m) | AC(g/cm3) | CNL(％) | Indication value |
| Dense layer | <12 | >900 | 48～49.5 | <0.25 | 0 |
| Fractured |
9～15 | 150～600 | 49.5～53 | 0.3～0.81 | 1 |
| Porous reservoir | 4～12 | 400～1200 | 48～51.6 | 0.3～0.81 | 2 |
| Fracture- |
9～15 | 30～100 200～400 | 48.7～55 | 0.1～1.51 | 3 |
| Filling cavities | 15～150 | 0～300 | 48～60 | 0.2～8.0 | 4 |
| Unfilled cavity | 6～16 | <30 | 52～75 | 1.3～9.0 | 5 |
In addition, in order to improve the accuracy of reservoir type identification such as cave and the like, an RBF neural network algorithm is also applied. An RBF is a three-layer forward network with a single hidden layer. The first layer is the input layer, which consists of log data sensitive to the reservoir type. The second layer is a hidden layer, and the transformation function of the neurons in the hidden layer, namely the radial basis function, is a nonnegative linear function which is radially symmetrical and attenuated to the central point, is a local response function and is superior to the original function of the global response. The third layer is the output layer, which responds to the input data, and here, the judgment of the output reservoir type, especially the judgment of the cave.
In the reservoir type discrimination, GR, RD, AC and CNL logging data sensitive to the reservoir type are selected as input, and the reservoir type determined according to geological information such as logging, well drilling and the like is selected as output. Using the input and output data of known results as data set, 80% was randomly selected as training set and 20% as testing set. After training and testing, the best network and its parameters are determined. And finally, judging by using the optimal network and the input unknown reservoir type logging data.
(2) Comprehensive analysis
And comprehensively analyzing by using a cross-plot method and an identification standard thereof and an RBF neural network, and adding crack identification information for constraint in the discrimination, wherein the input curves are natural gamma, deep lateral resistivity and crack porosity. And finally, carrying out comprehensive analysis on the identification and indication of the reservoir type to obtain a judgment result.
Secondly, identifying the cause type of the filling material
(1) Cross plot and sensitivity study
The cavity filling materials can be divided into three categories, namely mechanical sediment, gravity collapse accumulation and chemical sediment filling materials according to different causes. The fillings with different causes have different logging response characteristics, wherein the depth double lateral resistivity and the natural gamma logging response are sensitive, and the corresponding shale content is greatly different. The argillaceous content is usually determined by natural gamma.
According to the logging response characteristics of different filling material cause types, intersection charts of different cause types of the cave filling materials are constructed. Fig. 1 and 2 are identification plates constructed using Vsh and shallow lateral resistivity. From the plate it can be seen that mechanical deposits, gravity-collapsed deposits to chemical deposits have a tendency to decrease in argillaceous nature with increasing resistivity. The mechanical sediment is generally mainly filled with sand and mud, the resistivity is low, and the mud quality is high; the gravity collapsed accumulation is mainly breccite, and the resistivity is high; the chemical precipitates are mainly calcite and are characterized by low argillaceous and high resistivity. The unfilled cavities are characterized by low natural gamma and low resistivity. Utilizing a cave filling cause type identification module to construct a cave filling cause type identification indication curve, wherein the expressions are as follows:
TABLE 2 identification of cause type of cavern filling
| Type of filling | Vsh(％) | RS(Ω·m) | DEN(g/cm3) | Indication value |
| Is not filled with | <Y1 | <50 | 2.29～2.24 | 1 |
| Mechanical deposit fill | Y1<Vsh<Y2 | <100 | 1.86～2.43 | 2 |
| Gravity collapse stack | >Y2 | <400 | 2.32～2.74 | 3 |
| Chemical precipitation filling | <5％ | >4000 | >2.71 | 4 |
(2) Comprehensive analysis and calculation module
The method is used for writing the module to identify and indicate the cause type of the cave filling, and the input curve is shallow lateral direction, compensation density, shale content and reservoir type indication. The argillaceous content is obtained by natural gamma calculation.
Third, cave filling lithology discrimination
(1) Log response sensitivity study
Carbonate cavern type reservoir fillers can be classified into 5 types by lithology type, which are argillaceous, sandy, glutenite, and calcite, respectively. Different lithologic fillings have different logging response characteristics, and the sensitive logging methods include natural Gamma (GR), deep lateral Resistivity (RD), shallow lateral Resistivity (RS) and acoustic time difference (AC), neutron (CNL) and Density (DEN).
According to logging response characteristics of 5 lithologic fillings including argillaceous substances, sand argillaceous substances, sandy substances, glutenite and calcite, a 6-cave different filling cause type identification chart is respectively established by combining GR, RD, RS, AC, CNL and DEN in pairs. Fig. 4 and 5 are two exemplary cross-plates. From the cross-plot of the well log, the content of the mud gradually decreases and the shallow lateral resistivity gradually increases when the cavern is filled with the mud, sand, glutenite to calcite. The mud, sand mud and sand can be collectively called as sand mud filling, and are expressed as a natural gamma high value and a double lateral resistivity low value. Along with the increase of the argillaceous content, the acoustic wave time difference is increased, and the density value is reduced. When the breccia is filled, the natural gamma is a medium-low value, the depth bilateral resistivity is a high value, the acoustic time difference is low, the neutron value is low, and the density is high; when calcite is filled, the natural gamma is low, the bilateral resistivity is high, the acoustic time difference is small, the neutrons are low, and the density is high.
From the intersection map, the criteria for identifying the lithology of the cavity filler are realized, as shown in table 3.
TABLE 3 cavern filling lithology identification indications
| Type of filling | GR(API) | RS(Ω·m) | DEN(g/cm3) | Indication value |
| Is not filled with | ＜20 | ＜6 | 2.29～2.24 | 1 |
| Filling with mud | 30～110 | 0.5～25 | 1.86～2.43 | 2 |
| Sand and mud filling | 15～95 | 0～200 | 2.32～2.74 | 3 |
| Sand filling | 15～55 | 25～120 | 2.23～2.64 | 4 |
| Breccia filling | 7～20 | 40～5000 | 2.69～2.72 | 5 |
| Calcite filling | 2～8 | >7200 | 2.73～2.75 | 6 |
(3) Comprehensive analysis and calculation module
The method is used for writing the module cave filling lithology type for identification and indication, and the input curves include depth side direction, natural gamma, compensation density, sound wave time difference and compensation neutrons.
Fourth, evaluation of filling degree of cave
(1) Well log response characteristics and sensitivity studies
The reservoir properties of a cavern depend on the filling degree of the cavern in addition to the size and communication properties of the cavern, and only unfilled and semi-filled caverns have the capacity to store fluid. According to the filling degree in the cave, the cave type reservoir of the research area is divided into three situations of unfilled, half-filled and full-filled. The filling degree of the cave is different, and the natural gamma, the depth double lateral resistivity, the acoustic wave time difference, the neutron value, the density logging and the like have great difference.
(2) Cavern filling degree calculation
And performing pairwise intersection by using natural Gamma (GR), depth bilateral resistivity (RD and RS), neutron (CNL), Density (DEN) and acoustic time difference (AC) to respectively establish recognition charts of different filling degree types of the cave. It can be known from the intersection identification chart that natural Gamma (GR) and neutron (CNL) are sensitive to the filling degree of the cavern, and the higher the filling degree of the cavern is, the higher the natural gamma value is, and the higher the neutron value is. Therefore, natural Gamma (GR) is preferred as a sensitivity curve reflecting different filling degrees of the cavern, and the filling degree of the cavern is calculated.
Defining a parameter IVUGThe method is called as a natural gamma relative value, and quantitatively characterizes the cavern filling degree:
wherein, X is a natural gamma curve value; xminIs the base line value of natural gamma limestone. XmaxFilling pure mudstone with the maximum gamma value of the cave.
As the degree of cavern filling is higher, IVUGThe values are increased and a cavern filling degree identification quantity can be obtained by using the parameters and the shallow lateral resistivity, see fig. 6, and indication standards of different filling degrees of the cavern are established, as shown in table 4.
TABLE 4 identification of cavern filling level
| Type of filling degree of cavity | IVUG(％) | Degree of filling (I)VUGt) Indicating assignment |
| Is not filled with | 0～20 | 1 |
| Semi-filled | 20～60 | 2 |
| Full filling | 60～100 | 3 |
(3) Comprehensive analysis and calculation module
The writing module carries out the identification and indication of the cavern filling degree by using the method. Firstly, quantitative calculation of the cavern filling degree is carried out, the input curve is indicated by uranium-free gamma, natural gamma and reservoir types, and the output result is indicated by the filling degree. Then, the type of the filling degree is judged, the input curve is a filling degree indicating curve, and the output result is filling degree type indication.
Fifthly, the concrete steps
(1) First, a determination of the reservoir type is made. And inputting logging data, and judging the type of the reservoir according to the deep lateral resistivity-natural gamma intersection diagram and the fracture porosity constraint to judge. And inputting GR, RD and PORF curves into the module, outputting a CAVE _2 curve, judging the type of the reservoir according to the characteristics of the output curve, and outputting the judgment instruction of the CAVE with emphasis.
(2) Then, the type of cause of the cavity filling is determined. Inputting logging data, calculating the shale content, and judging the cause type of the filling according to a shale content-shallow lateral intersection graph and a judgment standard thereof. And inputting RS, DEN, SH and CAVE _2 curves into the module, outputting a Filler curve, and judging the cause type of the Filler according to the characteristics of the output curve.
(3) Next, the type of the lithology of the cave filling material is judged. Inputting logging data, and judging the lithology type of the filling material according to the 6 intersection maps and the judgment standard thereof for judgment. RS, RD, GR, DEN, AC, CNL and CN curves are input into the module, a packing curve is output, and the lithology type of the filler is judged according to the characteristics of the output curve.
(4) And finally, calculating the cave filling degree. And inputting logging data, calculating the relative gamma content, calculating a filling degree indicating curve according to a corresponding formula, and obtaining the filling degree indicating curve through a filling degree dividing standard. KTH, GR and CAVE _2 curves are input into the filling degree calculation module, VUG1 and VUG2 curves are output, and curve VUG1 and curve VUG2 curves are input into the filling degree judgment module, and a curve VUGt curve is output. And comprehensively judging the total filling degree of a certain cave according to the output filling degree curve numerical value.
Example analysis
The Z well is a exploration well of the Ordovician system of the Tahe oil field, data processing is carried out by utilizing the scheme and the developed device (module), cave stratums are respectively identified, and indication curves of filling cause types, filling lithology types, filling degrees and the like are obtained through calculation. FIG. 7 is a diagram of comprehensive interpretation results of well logging of a cave, the well logging is interpreted as a sand-mud filled cave in 5555-5559.5 m, the filling degree is 60-85%, the cave is fully filled, and the result is matched with a logging conclusion; logging at 5587.5-5599.5 m is interpreted as a sand mud filled cave, the filling degree is 20-40%, the sand mud filled cave is a half-filled cave and is matched with a logging conclusion; the interpretation at 5599.5-5628 m is that the sand is filled, the filling degree is 70-95%, the full-filling cave is formed, and the interpretation result is consistent with the logging conclusion.
The present invention is not limited to the above-mentioned preferred embodiments, and any other products similar or identical to the present invention, which can be obtained by anyone based on the teaching of the present invention, fall within the protection scope of the present invention.
Claims (1)
1. A comprehensive evaluation method for carbonate rock solution reservoir cave logging is characterized by comprising the following steps: the logging comprehensive evaluation system main control module calls the cave reservoir type identification module to judge the reservoir type, calls the cave filling cause type identification module to judge the filling cause type, calls the cave filling type judgment module to judge the lithologic type of the filling, and calls the cave filling degree evaluation module to calculate the filling degree; the method comprises the following specific steps:
firstly, judging the type of a reservoir; inputting logging data, and judging the type of a reservoir according to a deep lateral resistivity-natural gamma intersection diagram and fracture porosity constraint; inputting natural gamma GR, deep lateral resistivity RD and fracture porosity PORF curves into the module, outputting a reservoir type discrimination indication curve CAVE _2, and identifying a CAVE according to the characteristics of the output curve;
secondly, judging the cause type of the cave filling material; inputting logging data, calculating the shale content, and judging the cause type of the filling according to a shale content-shallow lateral intersection graph and a judgment standard thereof; inputting shallow lateral resistivity RS, density DEN, argillaceous content SH and CAVE _2 curves in a module, outputting a Filler curve, and judging the cause type of the filling material according to the characteristics of the output curve;
thirdly, judging the lithology type of the cave filling material; inputting logging data, and judging the lithology type of the filling material according to the 6 intersection maps and the judgment standard thereof; inputting shallow lateral resistivity RS, deep lateral resistivity RD, natural gamma GR, density DEN, acoustic wave time difference AC and neutron CNL into the module, outputting a packing curve, and judging the lithology type of the filler according to the characteristics of the output curve;
fourthly, calculating the cave filling degree; inputting logging data, calculating the relative gamma content, calculating a filling degree indicating curve according to a corresponding formula, and obtaining the comprehensive judgment of the filling degree through a filling degree dividing standard; KTH, GR and CAVE _2 curves are input into the filling degree calculation module, VUG1 and VUG2 curves are output, and VUG1 and VUG2 curves are input into the filling degree type judgment module, and VUGt curves are output; comprehensively judging the total filling degree of a certain cave according to the output filling degree indication curve;
wherein, in the first step:
outputting discriminant indications of the caverns is realized on the basis of establishing discriminant criteria of different reservoir spaces; the method comprises the steps that logging response characteristics of a reservoir space of a fracture-cavity reservoir body are different along with different reservoir space types, and GR-RS and AC-CNL cross maps are established through a dual-lateral resistivity, natural gamma, compensated neutron and acoustic wave time difference logging method; the shallow lateral resistivity of the cave in the GR-RS intersection is obviously different from the natural gamma, the acoustic wave and neutron logging of the cave in the AC-CNL intersection are also different from other reservoir types, and the two intersection maps are used for establishing the discrimination standards of different reservoir spaces;
the cave reservoir type identification module carries out cave discrimination and comprehensive analysis by using an RBF neural network algorithm; the RBF is a three-layer forward network with a single hidden layer, wherein the first layer is an input layer and consists of logging data sensitive to the type of a reservoir; the second layer is a hidden layer, the transformation function of the neurons in the hidden layer, namely the radial basis function, is a nonnegative linear function which is radially symmetrical and attenuated to the central point, and the function is a local response function and is superior to the original global response function; the third layer is an output layer and responds to input data, wherein the output layer is used for judging the type of the cave reservoir;
wherein, in the second step:
the establishment method of the criterion for judging the cause type of the filling material comprises the following steps: the filling materials with different causes have different logging response characteristics, wherein the depth double lateral resistivity and the natural gamma logging response are sensitive, the corresponding shale content is greatly different, and the shale content is obtained by the natural gamma, so that intersection graphs of different cause types of the cave filling materials are constructed; and constructing a cave filling cause type identification boundary by utilizing Vsh and shallow lateral resistivity, wherein the expressions are respectively as follows:
Y1＝20.223RS-2.056(1)
Y2＝80.924RS-0.632(2)；
wherein, in the third step:
the establishment method of the criterion for judging the lithology type of the filler comprises the following steps: different lithologic fillings have different logging response characteristics, and 6 different cave filling cause type identification charts are respectively constructed by combining the natural gamma GR, the deep lateral resistivity RD, the shallow lateral resistivity RS, the acoustic wave time difference AC, the neutron CNL and the density DEN according to the logging response characteristics of 5 lithologic fillings including mud, sand, glutenite and calcite, so that the explanation standard of cave filling lithologic identification is established;
dividing the cave of the research area into three situations of unfilled, half-filled and full-filled according to the filling degree in the cave;
wherein, in the fourth step:
the cave filling degree evaluation module comprises the following implementation steps:
firstly, performing pairwise intersection by using natural gamma GR, deep lateral resistivity RD, shallow lateral resistivity RS, neutron CNL, density DEN and acoustic wave time difference AC, and respectively establishing explanation charts of different filling degrees of the cave;
secondly, selecting natural gamma GR as a sensitive parameter reflecting different filling degrees of the cave, and calculating the filling degree of the cave; defining a parameter IVUGThe method is called as a natural gamma relative value, and quantitatively characterizes the cavern filling degree:
wherein X is a natural gamma curve value, XminIs a natural gamma limestone base line value, XmaxFilling the maximum gamma value of the cave for the pure mudstone;
finally, establishing a cave filling degree explanation plate and a standard; the higher the filling degree of the cavern, IVUGThe value is increased, and by using the parameter and the shallow lateral resistivity, a template for explaining the filling degree of the cave can be obtained, and an indication standard of different filling degrees of the cave is established.
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