CN114427457A - Method for determining logging pentasexual relation of tidal flat facies carbonate reservoir and logging evaluation method - Google Patents
Method for determining logging pentasexual relation of tidal flat facies carbonate reservoir and logging evaluation method Download PDFInfo
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Abstract
The invention discloses a method for determining logging pentasexual relation of a tidal flat facies carbonate reservoir and evaluating logging, which comprises the following steps: identifying the pore space structure type of a sample, and obtaining the parameter relationship between the geometric characteristics (pores, cracks and karst caves) of the pore structure of the sample and the lithology, electrical property, physical property and gas content of a reservoir, the method identifies the influence of the complex porosity structure of the reservoir on the reservoir logging quadriversality in the logging stage, thereby obtaining the correlation of the quintuplet of the reservoir of the carbonate rock of the tidal flat phase, in the subsequent reservoir evaluation process, establishing a multi-element nonlinear logging physical property model and an unfixed m-value saturation model for identifying the fluid property of the reservoir based on the determined correlation parameters, determining the logging evaluation standard, combining the logging parameters calculated by the model with the logging quality index, and carrying out rapid and accurate logging quantitative evaluation on the reservoir; therefore, accurate basic data support can be provided for subsequent development in the logging stage.
Description
Technical Field
The invention relates to the technical field of reservoir logging evaluation, in particular to a method for determining a quintuplet relationship of reservoir logging of a tidal flat phase carbonate rock and evaluating the logging.
Background
With the large-scale development (plain, dam, brook) of carbonate reservoir gas reservoirs, the exploration and development of carbonate reservoir logging become especially important, and the tidal plateau carbonate reservoir gas reservoir is taken as a new type of gas reservoir, so that the corresponding logging technology faces the difficult problems that a plurality of clastic rock reservoirs and reef reservoir reservoirs do not see, and the reservoirs have complex pore structures, special diagenesis and super-strong heterogeneity; in addition, the logging data recording is seriously lacked and the recording data scale error is large due to the adoption of the ultra-deep and highly-deviated well with the long target spacing, so that great challenges are brought to the fluid identification of the reservoir, and the research on the logging quantitative interpretation of the complex tidal plateau phase carbonate reservoir still belongs to the attack and hardening stage at present. The factors restrict the comprehensive evaluation of logging of the tidal flat facies carbonate reservoir to a certain extent, so that the gas reservoir is not known clearly and the development of technical policies are unreasonable. For example, chinese patent application No. 2019107456465 discloses a method, an apparatus, and a method for determining classification criteria of a tidal flat phase carbonate reservoir, which discloses a method, an apparatus, and a computer program product for performing reservoir evaluation and capacity prediction of a subsequent development area according to pore characteristics of an existing sample for an area that has been developed initially, but the reservoir evaluation and capacity prediction in a development stage depends on a series of parameters such as pores and physical properties in a logging stage, and once deviation occurs in logging data in the logging stage, unreasonable technical policy for subsequent development may be caused, and a great economic loss may be caused. Therefore, the research and evaluation of the fine logging method for the tidal plateau phase carbonate reservoir are the basis and the key for developing the benefit gas reservoir.
A regional explanation evaluation method is formed aiming at the carbonate reservoir well logging at present: one is to set up the conventional hole to ooze the saturated model according to the well logging quadrisexual relation of the conventional reservoir bed to the reef type reservoir bed, carry on the well logging to evaluate the reservoir bed, the procedure is: reservoir quadrisexual relation research, a conventional linear porosity model and a fixed saturation model. The method is a traditional well logging evaluation method, and has more bottleneck problems when evaluating the tidal plateau phase carbonate reservoir with strong heterogeneity. The second method is to divide the reservoir types into pore types and crack-pore types aiming at the karst weathering crust reservoir, and adopt different methods (Fisher, BP neural network and the like) to carry out effectiveness evaluation aiming at cracks according to different reservoir types. The third is to adopt special logging to develop logging evaluation aiming at the compact dolomite reservoir, and utilize special logging data to develop calculation and evaluation of reservoir logging parameters; and identifying the fractured-vuggy reservoir outside the borehole wall by using a remote detection acoustic imaging detection logging technology, and carrying out effectiveness evaluation on the reservoir. The evaluation method has higher requirements on well type and well hole conditions, has huge construction risk in the ultra-deep and highly-deviated well with long target spacing, and influences the investment benefit of block later-stage development and evaluation of production construction. In addition, the existing carbonate logging evaluation method mainly focuses on the aspect of fracture-cavity reservoir logging evaluation, physical property and fluid evaluation parameters are mainly obtained from acoustoelectric imaging logging information, and the influence of a complex porosity structure of a reservoir on reservoir logging quadriversal is not considered.
For tidal plateau phase carbonate reservoirs, ultra-deep and highly-inclined wells with long target intervals are mostly used, so that certain logging information (neutrons and density cannot be measured) is lacked in the logging stage, and the lacunarity of the logging information can influence the lithology identification and the mineral content calculation; the pore structure is different greatly, so that the single well hole permeability relationship is diversified; the electrical characteristics of the gas layer and the water layer are similar and are seriously inconsistent with the test result, so that the traditional well logging interpretation model and the fluid identification method have certain limitation and irrationality for the carbonate reservoir of the tidal flat phase.
Disclosure of Invention
The invention aims to solve the problems of limitation and irrationality of a traditional logging interpretation model and a fluid identification method for a tidal flat phase carbonate reservoir, and provides a method for determining a logging quintuplet relationship of the tidal flat phase carbonate reservoir and a logging evaluation method; the method can quickly and accurately calculate the mineral content and porosity of the tidal flat phase carbonate reservoir, and can evaluate the physical properties of the tidal flat phase carbonate reservoir in the logging stage.
In order to achieve the above object, the present invention provides the following technical solutions:
a method for determining a tide plateau phase carbonate pentasexual relationship, wherein the tide plateau phase carbonate pentasexual relationship is as follows: pore geometry and reservoir quarticity, comprising the steps of:
Identifying lithology, physical property, electrical property and fluid-containing characteristics of the sample;
identifying the type of a storage space of a sample, and dividing a pore structure of the storage;
obtaining characteristic parameters of lithology, physical property, electrical property and fluid-containing property of the reservoir and distribution range thereof through intersection analysis;
identifying the pore structure of the reservoir and dividing the pore geometrical characteristics of the reservoir;
and through the intersection analysis of the geometrical characteristics of the pores of the reservoir with the lithology, the physical property, the electrical property and the fluid content of the reservoir respectively, obtaining the relation parameters of the geometrical characteristics of the pores and the lithology, the physical property, the electrical property and the fluid content and the distribution range thereof, screening out the sensitive relation parameters for evaluating the effectiveness of the reservoir, and determining the value range of the sensitive relation parameters according to the distribution range of the screened sensitive relation parameters.
According to a specific embodiment, in the method for determining the pentasexual relationship between the tidal flat phase carbonate rocks, in the cross-section analysis process, the abscissa and the ordinate are taken as any two characteristics of the lithology, the physical property, the electrical property and the fluid property of the reservoir, and scatter points are marked in the coordinate system.
According to a specific embodiment, in the method for determining the penta-relationship between the tidal flat phase carbonate rocks, the geometrical characteristic of the pore space is taken as an abscissa and any one of the lithology, physical property, electrical property and fluid-containing property of the reservoir is taken as an ordinate during the cross-over analysis, and scatter points are marked in a coordinate system.
According to a specific embodiment, in the method for determining the quintet relationship of the tidal flat facies carbonate rock, the method for screening the sensitive relationship parameters for evaluating the effectiveness of the reservoir is as follows: if the scattered point concentrated area of at least one pore structure type can be distinguished, selecting two pore structure type parameters contained in the coordinate system as the sensitive relation parameters;
and determining the value range of the sensitive relation parameters by the following method: and demarcating the boundary of the scattered point concentration area of the sensitive relation parameter, and taking the parameter value range determined by the boundary as the value range of the sensitive relation parameter.
According to a specific embodiment, in the method for determining the penta-relationship of the tidal flat phase carbonate rock, the pore geometric characteristics are classified into any one of cracks, pores and holes.
In a further embodiment of the present invention, there is also provided a method for logging and evaluating a tidal flat phase carbonate reservoir, comprising the steps of:
determining the value range of the sensitive relation parameter by adopting the relation parameter determination method of the tidal flat phase carbonate rock pore geometric characteristics and reservoir bed tetragonality;
according to the value range of the sensitive relation parameter, calculating a reservoir mineral content parameter, and according to a mineral content parameter calculation result, calculating a reservoir physical property parameter and a reservoir fluid parameter;
According to the reservoir physical property parameter calculation result, preliminarily classifying the reservoir, and determining the logging quality evaluation index standard by combining the preliminary classification result and the reservoir fluid parameter calculation result;
and evaluating the logging quality of various reservoirs based on the logging quality evaluation index standard.
According to a particular embodiment, the mineral content parameter is calculated by:
Vi＝(Emli-Emlimin)/(Emlimax-Emlimin)
wherein, ViIs the volume percentage of mineral content; emliIs the elemental value of a mineral content, Emlimax、 EmliminThe maximum and minimum values of certain mineral content elements.
According to a specific embodiment, in the method for evaluating the logging of the tidal flat phase carbonate reservoir, the physical property parameter calculation includes a conventional logging porosity calculation, wherein the conventional logging porosity calculation is calculated by using a multivariate nonlinear porosity model:
wherein,
is porosity; eml, fitting AC into element logging to form sound wave value; eml, CNL is element logging fitting to form neutron value;
wherein, ViIs the volume percentage content of a certain mineral content; t is tiIs a sound wave skeleton value, LOCR, of a certain mineral contentiIs the neutron skeleton value of a certain mineral content.
According to a specific embodiment, in the method for evaluating the logging of the tidal flat phase carbonate reservoir, the physical property parameter calculation includes calculation of secondary porosity, and the calculation of the secondary porosity includes:
Acquiring imaging logging data, and performing histogram correction on polar plate data in the imaging logging data;
generating a static imaging logging image and a dynamic imaging logging image based on the data after the histogram correction;
converting the static imaging logging image into a peri-well visual porosity image by adopting static electrical imaging, and arranging according to the order of porosity from small to large to form a porosity spectrum;
and carrying out imaging spectrum slicing according to the distribution interval of the porosity spectrum, and calculating secondary slot hole parameters in the slice by adopting an Archie formula to obtain secondary porosity.
According to a specific implementation mode, in the method for evaluating the logging of the tidal flat phase carbonate reservoir, the reservoir fluid parameters are calculated by adopting an unfixed m-saturation model.
According to a specific embodiment, in the method for evaluating the logging of the tidal flat phase carbonate reservoir, the manner of preliminarily classifying the reservoir is as follows:
setting the measured porosity of the reservoir stratum as a, and defining the reservoir stratum as a type I reservoir stratum when a is more than or equal to 10%;
when a is more than or equal to 5% and less than 10%, defining the reservoir as a II reservoir;
when the a is more than or equal to 2% and less than 5%, defining the reservoir as a type III reservoir;
setting the calculated gas-water saturation Sg as b, and defining the gas layer when the b is more than or equal to 60% and less than or equal to 100%;
When the calculated Sg range is more than or equal to 40% and b is less than 60%, defining the Sg range as a gas-water same layer;
when the calculated Sg range is 0% ≦ b < 40%, defined as water layer.
Compared with the prior art, the invention has the beneficial effects that:
1. reservoir fifth-pore geometric characteristics (pore structure type and connectivity, pore size, fracture development degree, extensibility, occurrence and the like) and lithology, electrical property, physical property and fluid-containing property parameter relationships are qualitatively or semi-quantitatively researched by reservoir fifth relationship research, and the contradictions of large porosity calculation error, disorder relation between porosity and water saturation, mismatching between electrical property and gas-containing property and the like caused by traditional quadric relationship research can be overcome, so that reservoir logging evaluation can be favorably carried out on tidal flat phase carbonate rocks with complex pore structure types.
2. On the basis of reservoir pentagram relation research, lithology recognition, porosity calculation and non-fixed m value saturation calculation of a long-target-spacing ultra-deep highly-deviated well are carried out, and on the basis of well logging parameter calculation, a reservoir well logging evaluation standard is established, so that rapid and accurate well logging quantitative evaluation can be carried out on a reservoir.
Description of the drawings:
FIG. 1 shows a schematic representation of fracture dip angle versus pore consolidation index m (from a petroelectric experiment) for an exemplary embodiment of the present invention;
FIG. 2 shows a schematic diagram of porosity versus formation factor (from core) for an exemplary embodiment of the present invention;
FIG. 3 is a graph showing the degree of connectivity of erosion voids (holes) versus electrical properties in accordance with an exemplary embodiment of the present invention;
FIG. 4 illustrates a schematic diagram of a multi-component non-linear log porosity model for a tidal flat phase carbonate reservoir in accordance with an exemplary embodiment of the present invention;
FIG. 5 shows an electrographic porosity spectrum schematic of an exemplary embodiment of the invention;
FIG. 6 shows a schematic XRMI slotted hole slice of an exemplary embodiment of the present invention;
FIG. 7 shows a schematic representation of XRMI slot parameter calculation for an exemplary embodiment of the present invention;
FIG. 8 illustrates an imaging log porosity spectrum discriminative reservoir validity identification schematic of an exemplary embodiment of the present invention;
FIG. 9 shows a schematic view of a tidal flat phase carbonate reservoir non-stationary m-value saturation model fluid identification according to an exemplary embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to test examples and specific embodiments. It should be understood that the scope of the above-described subject matter is not limited to the following examples, and any techniques implemented based on the disclosure of the present invention are within the scope of the present invention.
The embodiment of the invention provides a method for researching logging quintuplet relation of a tidal flat facies carbonate reservoir and evaluating the logging.
The classification standard determination method comprises the following steps:
s110, identifying the type of a reservoir space of a sample, dividing a pore structure of a reservoir and determining the fifth characteristic of reservoir logging;
the fifth characteristic of reservoir logging, namely the pore space structure type, is identified by integrating the rock core and the slice, and the method mainly comprises the following steps: the major reasons are the karst pores and the karst caves, and the micro cracks and the karst fissures are commonly developed. Identifying the pore structure of the reservoir, and the fifth property (crack, pore and hole) of the divided reservoir logging;
s120, calculating sensitivity relation parameters of the quintuplet relation of the reservoir based on a rock electricity laboratory, a mercury intrusion experiment, a digital core and special logging data; as shown in fig. 1, a relationship curve of a crack inclination angle and a pore index and a relationship graph of a connectivity degree of a corrosion pore (hole) and an electrical property are obtained through a rock-electricity experiment.
The sensitive relation parameter table of the quintuple relation of partial reservoir logging obtained aiming at the stratum is shown in the table 1 and the table 2, the parameter relation table of gas content and electric property is shown in the table 1
TABLE 2 relationship between crack radial extension and electrical property
| Degree of extension | Storage and seepage significance | Extended length | Resistivity (RD) | RD/RS ratio |
| Extremely shallow extension | Without storage and seepage significance | Less than 0.3m | 8000 | -- |
| Shallow extension | Has a certain storage permeability | 0.3-0.5m | 2000-8000 | 5--10 |
| Moderate elongation | Has a certain storage permeability | 0.5-2.0m | 100-2000 | 3-10 |
| Deep extension | Good storage and seepage significance | Greater than 2.0m | 100-2000 | <3 |
After carrying out the research on the logging quintuple relation of the reservoir, carrying out the calculation of reservoir logging parameters based on the result, wherein the specific evaluation comprises the following steps:
s210, carrying out the well logging mineral content calculation without neutrons and density curves;
firstly, carrying out mineral element characteristic composition analysis, establishing correlation between element logging characteristic values (Ca, Mg, Si, Ca/Mg, Fe and the like) and 6 kinds of lithologies, selecting key sensitive element characteristic values and logging curves through main component analysis, and obtaining a linear regression formula for calculating mineral content by taking the obtained characteristic value contents of the key sensitive elements Ca, Mg, Si, AC, GR and RD as independent variables and the contents of calcite and dolomite as dependent variables.
S220, taking the mineral content calculation result as a basis, combining the actually-measured core physical property data to carry out core calibration, and carrying out reservoir logging physical property parameter calculation; and the physical property parameter calculation of the reservoir comprises conventional logging porosity calculation, secondary porosity calculation and nuclear magnetic resonance logging porosity calculation.
1. Conventional well logging porosity calculation: on the basis of mineral content calculation, the CNL and DEN curve values of a single well are fitted by combining the skeleton values of minerals, and the porosity of the reservoir is calculated by adopting a multi-element nonlinear porosity model. FIG. 4 shows a multivariate nonlinear porosity model fitted by an exemplary embodiment of the present invention:
Wherein,
is porosity; eml, fitting AC into element logging to form sound wave value; eml, CNL is element logging fitting to form neutron value;
wherein, ViIs the volume percentage content of a certain mineral content; t is tiIs a sound wave skeleton value, LOCR, of a certain mineral contentiIs the neutron skeleton value of a certain mineral content.
The non-linear equation group solution method adopts a C language development algorithm program completely oriented to the process, and the porosity calculation process is to set the gamma, sound wave and neutron logging values without uranium of the depth sampling point at present; the rock skeleton parameters and the fluid parameters are substituted to form a nonlinear equation set of the current sampling depth point, the equation set is solved to obtain the porosity-Phie of the rock at the current depth, the sampling interval of the logging information is 0.1m and is taken as a cycle step length, and the cycle is finished to the processing well section, so that the calculation result of the porosity of the continuous well section can be obtained.
2. Calculating the secondary porosity: the secondary porosity is calculated by a fracture-cave carving method, the method firstly carries out pretreatment such as correction and dynamic and static reinforcement on imaging logging data, then adopts an Archie model to invert a porosity spectrum, adopts a static image to carry out image slicing, and finally calculates fracture-cave reservoir parameters and identifies fluid properties.
(1) Imaging logging data preprocessing
And after the imaging logging data is subjected to de-encoding and depth correction, performing histogram correction on the data of the polar plate. Due to the influence of scale factors of the polar plates of the instrument and the like, the distribution range of original logging data of each polar plate is large in variation, peak values are unreasonable in distribution, and part of data may be distortion values which are not in accordance with the actual situation of the stratum, and the data of each polar plate needs to be standardized and corrected according to the analysis result. And (4) standardizing the distribution interval of the polar plate data by adopting a normal distribution function, and dividing the data into 0-255 level intervals. Polar plate data are normalized through normal function processing, and the data distribution center and the main peak height are relatively close. And (3) converting the standardized logging data, and performing image enhancement on the imaging logging data in a histogram enhancement mode to obtain a digital image with uniform image gray distribution and the level number of the digital image within the range of 0-255.
Firstly, each gray level rk is mapped to an expected gray level Sk by adopting a discrete function to obtain equalized data:
in the formula: n is the sum of the number of pixels in the image, njTo a gray level of rjR is the gray level of the input image, prFor the histogram data obtained above, k is 0,1,2 …, L-1, L being the number of discrete gray levels.
Secondly, performing function transformation on the balance data to obtain histogram specified data:
in the formula: z is the gray level of the output image. Through the above processing, the raw material isEach pixel r of datakMapping to a corresponding grey level SkThen, the S iskMapping to a final grey level value ZkThereby realizing histogram enhancement. Obtaining a static imaging log XRMI _ S using a full wellbore section as a window length1Obtaining a dynamic imaging log XRMI _ D using a window length1。
(2) Imaging porosity spectrum inversion
Generally, we calculate the reservoir water saturation based on porosity, resistivity, etc. data and using the Archie's formula or similar; when the water saturation and the resistivity of the stratum are known, the porosity of the reservoir can be obtained by the formula, and the effectiveness of the reservoir is judged according to the porosity. The imaging logging has high resolution and well coverage rate, so that the conductivity of the imaging logging is converted into apparent formation resistivity, and then the porosity of the reservoir is calculated, so that the method has high precision and good discrimination effect on the carbonate reservoir with strong heterogeneity.
Since the imaging log generally detects only the wash zone of the formation, the porosity of the formation can be approximated using the Archie's equation after obtaining the water saturation of the wash zone or estimating it using conventional data calculations. The concrete model is as follows:
In the formula, SXOSelecting the water saturation of the flushing zone calculated by the conventional well logging; rmf is mud resistivity, a, b, m and n are corresponding rock electrical experiment parameters of the Archie model, and Rxo is resistivity of the imaging electrode. Imaging logging data are scaled through full-diameter core analysis data, and imaging resistivity values are converted into corresponding original apparent porosity phi through the model. In actual processing, as the static electric imaging well logging data is processed by a unified standard in the whole well section, the static electric imaging is converted into a peri-well vision porosity image in the project, and the porosity spectrum is formed by arranging the porosities from small to large, and fig. 5 shows the imaging porosity spectrum of a test well (P1 well), wherein XRMI _ S1For static electrograms, XSPEC is the converted electrogram porosity spectrum.
(3) Imaging spectral slice
According to the distribution interval and the characteristics of the imaging spectrum, under the condition of no obvious water layer, the secondary pore can be determined and calculated by using the imaging spectrum; if the porosity spectrum is concentrated in the latter part, it is usually a water layer effect. Imaging spectral slicing is performed according to the spectral distribution interval of fig. 5, and secondary slot morphology XRMI _ T1 is extracted, and the slices are as shown in fig. 6.
(4) Seam hole parameter calculation
And (4) calculating secondary slot parameters in the slice by adopting an Archie formula according to the porosity spectrum and the slice calculated in the previous step to obtain parameters such as secondary porosity and the like, as shown in figure 7.
Further, fig. 8 shows that P1 well mine slope component image well logging porosity spectrum discriminates the effective achievement diagram of the reservoir (in the diagram, POR2 corresponds to the B region of the imaging spectrum, POR3 corresponds to the C region of the imaging spectrum), the section of the developed high-angle fracture is judged to be an effective reservoir if the effective porosity of 5845.2.5-5848 meters and 5820-5822 meters is larger; the porosity below 5823 m is very low, the reservoir effectiveness is poor, and the natural gas industrial capacity is obtained by the section of test, so that the method for calculating the imaging logging secondary porosity spectrum is accurate and reliable.
3. Nuclear magnetic porosity calculation: the method for obtaining the nuclear magnetic porosity under the laboratory condition comprises the steps of firstly, scaling a T2 spectrum measured by a saturated rock sample by using a standard sample (water), converting the nuclear magnetic signal intensity into the porosity, establishing the correlation between the conventional rock core porosity and the nuclear magnetic porosity, and calibrating the nuclear magnetic porosity by using the conventional rock core porosity.
S230, calculating and identifying reservoir fluid parameters based on the calculation results of S110, S210 and S220;
In a further embodiment of the invention, the reservoir fluid parameter calculation adopts non-fixed value m saturation calculation, on the basis of the result of the quintuplet relation research of S110, reservoir spaces are respectively selected as a dry layer and a water layer which mainly comprise matrix pores, low-angle seams, erosion pores and high-angle seams, and the value m is obtained through back calculation according to rock electricity experimental parameters and calculated porosity, so that a relation chart of different pore structure types m and formation resistivity Rt, namely a non-fixed value m calculation model, is obtained. FIG. 9 illustrates a non-fixed m-value computation model of an exemplary embodiment of the present invention. The method comprises the steps that a C language development algorithm program completely oriented to the process is adopted for a non-fixed m value calculation model solution, the m calculation process is that a resistivity value of a current set depth sampling point is substituted to form a nonlinear equation set of a current sampling depth point, the equation set is solved to obtain the porosity-Phie of a current depth rock, the sampling interval of logging information is 0.1m, the sampling interval is used as a circulation step length, circulation is finished to a processing well section, and then m value calculation of a continuous well section can be obtained; and substituting the calculated m into the corrected Archie formula to calculate the gas saturation.
S310, carrying out reservoir logging classification evaluation of different fluid properties; fluid classification is carried out according to the gas-water saturation, and preliminary classification is carried out on the reservoir according to the physical parameters calculated by logging:
Defining as a gas layer when the calculated Sg range is 50-75%;
when the calculated Sg range is 50-75%, defining the Sg range as a gas-water layer;
when the calculated Sg ranges from 50 to 75%, it is defined as a water layer.
When the measured porosity value of the reservoir is more than or equal to 10%, defining the reservoir as a type I reservoir;
when the measured porosity value of the reservoir ranges from 5% to 10%, defining the reservoir as a II type reservoir;
when the measured porosity value of the reservoir ranges from 2% to 5%, defining the reservoir as a III type reservoir;
further, the reservoirs are classified according to porosity, electrical data, fluid parameters: the logging data has multiple solutions, if the fluid property is judged by only one gas saturation index, the erroneous judgment is easily caused, therefore, the invention adopts multiple indexes to synchronously judge the fluid property and simultaneously meets multiple conditions, so that the multiple solutions can be reduced to a certain extent and become unique solutions, on one hand, the initial judgment is carried out by utilizing typical characteristics, on the other hand, the fluid is judged by utilizing the saturation, and on the other hand, the judgment is carried out by utilizing other dipole, nuclear magnetism and the like.
Therefore, the fluid is judged according to the gas-water saturation, simultaneously, the different fluid properties of the reservoir are preliminarily classified according to the logging electrical parameters, and the fluid properties are judged by combining the fluid properties and the logging electrical parameters.
The electrical characteristics are represented by RD: 100-: 30-2000 (omega. m), the CNL of more than 8 percent is classified as the gas-water layer, the electrical characteristics are that the RD is less than 100 (omega. m), and the CNL of more than 8 percent is classified as the water layer.
And then, preliminarily classifying the reservoirs according to the physical property parameters calculated by logging:
a porosity value greater than or equal to 10% is classified as a type I reservoir; porosity values ranging from 5% to 10% are classified as class II reservoirs; porosity values in the range of 2% -5% were classified as a type III reservoir.
And S320, evaluating the logging quality of various reservoirs by taking the classification result as a basis and combining with the reservoir fluid parameters.
And calculating the reservoir fluid parameters of various reservoirs from high to low by combining the porosity classification standard. Specifically, the reservoir types are divided for reservoirs of each fluid property, and the evaluation index standards of the well logging quality at the superior level, the medium level and the inferior level are respectively established and are shown in table 3.
TABLE 3 well logging quality index partition table
The results obtained from the evaluation are shown in table 4:
TABLE 4 classification evaluation table for logging of tidal flat phase carbonate rock reservoir in Chuanxi Leikou slope group
In conclusion, by the classification standard determining method and the evaluation method, the physical properties of the reservoir in the region can be rapidly evaluated, the evaluation of the fluid properties can be rapidly carried out on the basis of the physical property evaluation result, the evaluation of the logging quality of the reservoir can be finally carried out, and the reservoir productivity can be preliminarily determined.
Example 2
In a further embodiment of the present invention, reservoir quintety parameters of the target block are calculated by using the reservoir quintety relation research method and the evaluation method described in embodiment 1, and logging parameters and a logging curve of the target block are obtained. And analyzing the influence factors of the reservoir productivity, considering factors such as reservoir space type, horizon difference, physical properties, fluid properties and the like, and establishing a corresponding block productivity prediction model by combining the logging information with the tested productivity and utilizing various algorithms such as a neural network technology and the like.
According to the determined prediction model, the drilling of the block is subjected to single-well productivity prediction by combining an interpretation model and reservoir discrimination, and a prediction result and an actual test result are listed in a table 5. Statistics shows that the 20 layers and the 17 layers are coincident, the coincidence rate is 85%, the non-resistance flow capacity prediction coincidence rate is relatively good, the error rate is controlled to be 2-19%, and the average error rate is only 9.6%. From the logging quality index, the quality index can indicate the water-bearing reservoir, the prediction result is relatively close to the actual result, and the prediction coincidence rate is good.
TABLE 5 evaluation of logging quality and productivity prediction of main well regions
Practice proves that the capacity prediction model established based on the reservoir quintuple relation and accurate quintuple parameter calculation at this time is more in line with objective practice and can be used for objectively evaluating the potential production capacity of the reservoir. In table 5, the predicted productivity of some reservoirs is greater than the actual productivity, and the analysis suggests that the effect may be due to the acidizing fracturing process of the reservoir or reservoir pollution. The result of the productivity prediction can be understood as an ideal acid fracturing process condition and an upper limit of the gas production capacity (non-resistance flow) under the condition that the reservoir is free from pollution, and under the condition that the influence of the two conditions is small, the productivity prediction is stable and reliable under the condition that the logging curve and the evaluation method obtained by the reservoir pentagon determination method provided by the embodiment 1 can truly reflect the original purpose of the reservoir.
The above description is intended to be illustrative of the preferred embodiment of the present invention and should not be taken as limiting the invention, but rather, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (10)
1. The method for determining the logging pentasexual relation of the tidal plateau phase carbonate reservoir is characterized by comprising the following steps of:
identifying lithology, physical property, electrical property and fluid-containing characteristics of the sample;
identifying the type of a reservoir space of a sample, and dividing a pore structure of the reservoir;
obtaining characteristic parameters of lithology, physical property, electrical property and fluid-containing property of the reservoir and distribution range thereof through intersection analysis;
identifying the pore structure of the reservoir and dividing the pore geometrical characteristics of the reservoir;
and through the intersection analysis of the geometrical characteristics of the pores of the reservoir and the lithology, the physical property, the electrical property and the fluid-containing property of the reservoir, obtaining the relation parameters of the geometrical characteristics of the pores and the lithology, the physical property, the electrical property and the fluid-containing property and the distribution range thereof, screening out the sensitive relation parameters for evaluating the effectiveness of the reservoir, and determining the value range of the sensitive relation parameters according to the distribution range of the screened sensitive relation parameters.
2. The method for determining a quintuplet relationship for a tidal plateau phase carbonate reservoir logging as claimed in claim 1, wherein: in the intersection analysis process, any two characteristics of lithology, physical property, electrical property and fluid-containing property of a reservoir are taken as an abscissa and an ordinate, and scattered points are marked in a coordinate system;
the cross analysis of the geometrical characteristics of the pores passing through the reservoir and the lithology, physical property, electrical property and fluid-containing property of the reservoir respectively comprises the following steps: the geometrical characteristic of the pores is taken as an abscissa, any one of the characteristics of the lithology, the physical property, the electrical property and the fluid-containing property of the reservoir is taken as an ordinate, and scatter points are marked in a coordinate system.
3. The method for determining a quintuplet relationship for a tidal flat phase carbonate reservoir log of claim 1, wherein the screening of sensitive relationship parameters for evaluating the effectiveness of the reservoir is performed by: if the scattered point concentrated area of at least one pore structure type can be distinguished, selecting two pore structure type parameters contained in the coordinate system as the sensitive relation parameters;
and determining the value range of the sensitive relation parameters by the following method: and demarcating the boundary of the scattered point concentration area of the sensitive relation parameter, and taking the parameter value range determined by the boundary as the value range of the sensitive relation parameter.
4. The method for determining the quintuplet relationship of a tidal flat phase carbonate reservoir of any of claims 1 to 3, wherein the pore geometry is classified as any of fracture, pore, and hole.
5. The logging evaluation method for the tidal flat phase carbonate reservoir is characterized by comprising the following steps of:
determining the value range of the sensitive relation parameter by adopting the method for determining the logging pentagon of the tide plateau phase carbonate reservoir according to any one of claims 1 to 4;
according to the value range of the sensitive relation parameter, calculating a reservoir mineral content parameter, and according to a mineral content parameter calculation result, calculating a reservoir physical property parameter and a reservoir fluid parameter;
according to the reservoir physical property parameter calculation result, preliminarily classifying the reservoir, and determining the logging quality evaluation index standard by combining the preliminary classification result and the reservoir fluid parameter calculation result;
and evaluating the logging quality of various reservoirs based on the logging quality evaluation index standard.
6. The method for well logging evaluation of a tidal flat phase carbonate reservoir as claimed in claim 5, wherein the mineral content parameter is calculated by:
Vi＝(Emli-Emlimin)/(Emlimax-Emlimin)
Wherein, ViIs the volume percentage of mineral content; emliIs the elemental value of a mineral content, Emlimax、EmliminThe maximum and minimum values of certain mineral content elements.
7. The method for well logging evaluation of a tidal flat phase carbonate reservoir of claim 6, wherein the physical property parameter calculations comprise conventional well logging porosity calculations, wherein the conventional well logging porosity calculations are calculated using a multivariate non-linear porosity model:
wherein,
is porosity; eml, fitting AC into element logging to form sound wave value; eml, CNL is element logging fitting to form neutron value;
Eml:
wherein, ViIs the volume percentage content of a certain mineral content; t is tiIs a sound wave skeleton value, LOCR, of a certain mineral contentiIs the neutron skeleton value of a certain mineral content.
8. The method for evaluating a tidal flat phase carbonate reservoir log of claim 7, wherein the physical property parameter calculations comprise calculations of secondary porosity, the calculations of secondary porosity comprising:
acquiring imaging logging data, and performing histogram correction on polar plate data in the imaging logging data;
generating a static imaging logging image and a dynamic imaging logging image based on the data after the histogram correction;
Converting the static imaging logging image into a peri-well visual porosity image by adopting static electrical imaging, and arranging according to the order of porosity from small to large to form a porosity spectrum;
and carrying out imaging spectrum slicing according to the distribution interval of the porosity spectrum, and calculating secondary slot hole parameters in the slice by adopting an Archie formula to obtain secondary porosity.
9. The method for well logging evaluation of a tidal flat phase carbonate reservoir of claim 5, wherein the reservoir fluid parameters are calculated using an unfixed m-saturation model.
10. The method for well logging evaluation of a tidal flat phase carbonate reservoir of claim 9, wherein the reservoir is initially classified by:
setting the measured porosity of the reservoir as a, and defining the reservoir as a type I reservoir when a is more than or equal to 10%;
when a is more than or equal to 5% and less than 10%, defining the reservoir as a II reservoir;
when the a is more than or equal to 2% and less than 5%, defining the reservoir as a type III reservoir;
setting the calculated gas-water saturation Sg as b, and defining the gas layer when the b is more than or equal to 60% and less than or equal to 100%;
when the calculated Sg range is more than or equal to 40% and b is less than 60%, defining the Sg range as a gas-water layer;
when the calculated Sg range is 0% ≦ b < 40%, defined as water layer.
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