CN105158796A - Method and device for determining TOC content - Google Patents

Abstract

The invention provides a method and device for determining TOC content. The method comprises the following steps: establishing a TOC optimally-estimated mineral rock physical model by utilizing data of laboratory-measured X-ray diffraction data, organic carbon density and logging density curve and the like; obtaining accurate TOC distribution on a well through an optimization iteration method; establishing a TOC quantity version by utilizing actually-measured TOC data and an equivalent medium model; determining TOC sensitivity elastic parameters; and finally, carrying out prestack elastic parameter inversion on earthquake reflection gather obtained through OVT region processing. The method and device for determining the TOC content realize the purpose of quantitatively forecasting space distribution of the TOC content by utilizing well-seism integration through statistical fitting, and solve the problem of not high TOC content forecasting result; and through using the laboratory-measured data and the rock physical model effectively, the estimated result is not only matched with the laboratory-measured value at the places of rock samples, but also can predicate the TOC content at the places without sampling points accurately.

Description

Determine the method and apparatus of TOC content

Technical field

The present invention relates to geophysical prospecting for oil technology, be specifically related to the method and apparatus that one determines TOC (TotalOrganicCarbon, total organic carbon) content.

Background technology

At present, mainly utilize earthquake means to predict fine and close hydrocarbon-bearing pool, fine and close oil gas has two features: 1) due to Compacted rock, porosity and permeability is low, the distance that oil gas is arranged outward from hydrocarbon source rock, transport is very short, therefore be often stored in the reservoir of hydrocarbon source rock, namely oil gas has the feature of " short row, short fortune "; 2) due to fine and close lithology often complicated component, self be hydrocarbon source rock and reservoir, therefore sometimes store on the spot after oil-gas generation, namely oil gas has the feature of " spontaneous, storage certainly ".The main contents that above two features of fine and close oil gas determine fine and close prediction of oil-gas reserve are exactly the prediction characterizing abundance of organic matter index in rock.

Total organic carbon (TotalOrganicCarbon, TOC) content refers to the mass percent of organic carbon in unit mass rock, is the index representing organic total amount with the content of organic carbon.Organic origin theory thinks oil and natural gas all by organic matter transformation, is therefore called hydrocarbon source rock by being rich in organic lithology.Due to organic matter mainly carbohydrates, therefore TOC content can as the important indicator measuring the content of organic matter.In petroleum prospecting, the mensuration of TOC content is often carried out in laboratory, is the important indicator that petroleum geologist judges hydrocarbon source rock Organic Matter Enrichment degree.

In recent years, along with the exploration and development energetically of unconventional petroleum resources, become the important means of prediction Effective source rocks distribution by geophysical techniques estimation TOC content distribution.The technology of TOC content mainly contains following two kinds of modes to utilize geophysics means to predict at present:

Mode 1) experimental determination obtain rock sample TOC containing after numerical quantity, be analyzed with the logging trace of the corresponding degree of depth, qualitatively determine the logging response character that TOC content is corresponding, this is the object in order to realize being predicted by logging response character TOC content.Utilize the seismic properties feature that the relation determination log response between seismic properties with log response is corresponding further, thus realize by seismic properties indirect predictions TOC content.

This mode is compared with single attribute Forecasting Methodology, and reliability is higher, and shortcoming is that the TOC curve error that logging trace statistical fit obtains is comparatively large, and it is too simple to set up the method contacted between seismic properties, and error is also very large.

Mode 2) in conjunction with geological analysis, utilize geological data directly to predict the space distribution of TOC content.This mode is compared with upper a kind of mode, and maximum feature take geological knowledge as background, can from macroscopically holding the validity predicted the outcome, but Longitudinal precision is very low, and the error that predicts the outcome is larger.

For the precision of prediction how improving TOC content, at present effective solution is not yet proposed.

Summary of the invention

Embodiments provide a kind of method determining TOC content, to reach the object of the precision of prediction improving TOC content, the method comprises:

Obtain logging trace and the experimental determination data in region to be analyzed;

According to described logging trace and described experimental determination data, estimate factor of porosity and the water saturation in described region to be analyzed;

X ray diffracting data analysis is carried out to the rock sample obtained from described region to be analyzed and obtains XRD data;

Described rock sample is analyzed, obtains the TOC assay data of described region to be analyzed rock sample;

Obtain volume and the quality of described rock sample, and obtain inorganic mineral density and TOC density according to described volume and Mass Calculation;

At the well point place in described region to be analyzed, corresponding relation between the TOC assay data determining described logging trace and described rock sample, and using the forecast model of described corresponding relation as described well point place TOC content data, obtain a continuous print TOC densimetric curve according to described forecast model matching;

Set up the rock skeleton model characterized by inorganic mineral density and TOC density, wherein, described inorganic mineral density deducts described TOC assay data by described XRD data and obtains, and described TOC density is the numerical value that described TOC densimetric curve characterizes;

According to described logging trace, described factor of porosity, described water saturation, described inorganic mineral density and described TOC density, set up the rock density that described rock skeleton model characterizes;

Densimetric curve in the rock density characterized according to described rock skeleton model and described logging trace, is set up objective function, utilizes optimality analysis, corrected by the mode of iteration correction to described TOC densimetric curve, obtain the TOC densimetric curve after correcting;

The quantitative relationship between speed and elastic modulus is set up according to described rock skeleton model;

Experimentally room measures the speed, elastic modulus, the TOC data that obtain to described rock sample, and described quantitative relationship determination micro-scale is to the elastic parameter of TOC sensitivity, and the TOC set up based on sensibility elasticity parameter measures version;

Described well point place is calculated to the elastic parameter of TOC sensitivity according to described TOC amount version and described logging trace, and according to described well point place to the TOC densimetric curve after the elastic parameter of TOC sensitivity and described correction, therefrom see the statistical relationship that dimension calculation obtains the degree of correlation between elastic parameter and TOC density;

Obtain the geological data in described region to be analyzed, and the process of OVT territory is carried out to described geological data;

Pre-stack seismic inversion is carried out to the geological data carried out after the process of OVT territory and described logging trace, obtains the data volume of the described elastic parameter to TOC sensitivity;

According to described statistical relationship, the data volume of the described elastic parameter to TOC sensitivity is converted to TOC content spatial distribution data body;

TOC content space distribution is obtained according to described TOC content spatial distribution data body.

In one embodiment, obtain the logging trace in region to be analyzed, comprising:

Obtain one or more well-log informations in described region to be analyzed, wherein, described well-log information comprises following one or combination in any: compressional wave time difference, shear wave slowness, density, neutron, resistivity, natural gamma, natural gamma spectra uranium;

Described one or more well-log information according to obtaining generates described logging trace.

In one embodiment, according to described logging trace, estimate factor of porosity and the water saturation in described region to be analyzed, comprising:

According to described logging trace, set up factor of porosity appraising model and water saturation appraising model:

S _w＝f _sw(y ₁,y ₂,…y _n)

Wherein, represent total porosity, S _wbe expressed as water saturation, x ₁, x ₂... x _nrepresent measured data and the logging trace for carrying out factor of porosity estimation, y ₁, y ₂... y _nrepresent the logging trace participating in estimation water saturation, n represents the number of the data participating in estimation, and f _swrepresent polynary mapping function;

Obtain factor of porosity according to described factor of porosity appraising model estimation, obtain water saturation according to described water saturation appraising model estimation.

In one embodiment, obtain inorganic mineral density and TOC density according to described volume and Mass Calculation, comprising:

According to following formulae discovery inorganic mineral density and TOC density:

Q _nk＝Q-Q _TOC

${\mathrm{\ρ}}_{nk}=\frac{Q_{nk}}{V}$

${\mathrm{\ρ}}_{TOC}=\frac{Q_{TOC}}{V}$

Wherein, Q represents the quality of dry core sample, Q _nkbe expressed as inorganic mineral quality, Q _tOCrepresent TOC quality, ρ _nkrepresent inorganic mineral density, ρ _tOCrepresent TOC density, V represents the volume of dry core sample.

In one embodiment, according to the densimetric curve in described rock density and described logging trace, set up objective function, utilize optimality analysis, by iteration correction TOC density correction rock density, to obtain the TOC densimetric curve after correcting, comprising:

Response equation is set up according to described rock skeleton model:

Wherein, ρ represents the lithosome density theory value that response equation calculates, and K represents TOC percent by volume, ρ _nkrepresent inorganic mineral density, ρ _tOCrepresent TOC density, represent total porosity, ρ _orepresent oil density, ρ _wrepresent water-mass density, S _wrepresent water saturation;

According to the densimetric curve in described rock density and described logging trace, set up following objective function:

minF(x)＝min(ρ-ρ _b) ²+g(x)

g(x)＝10 ⁶[|x|-x] ²+10 ⁶[|4-x|-(4-x)] ²

0≤x≤4

Wherein, F (x) represents objective function, ρ _brepresent practical logging density, g (x) represents bound term, and min represents and makes objective function reach minimal value by bound term;

Carry out iteration correction using TOC density as the x in described objective function, obtain the TOC densimetric curve after correcting.

In one embodiment, set up the quantitative relationship between speed and elastic modulus according to described rock skeleton model, comprising:

Bulk modulus and the modulus of shearing of described rock skeleton model is determined by Hashin-Shtrikman boundary;

By bulk modulus and the modulus of shearing of the dry rock of K-T formulae discovery;

The bulk modulus and modulus of shearing that are full of rock under regimen condition is calculated by Gassmann equation;

By being full of bulk modulus and the modulus of shearing of rock under regimen condition, set up the quantitative relationship between speed and elastic modulus.

The embodiment of the present invention additionally provides a kind of device determining TOC content, and to reach the object of the precision of prediction improving TOC content, this device comprises:

Data acquisition module, for obtaining logging trace and the experimental determination data in region to be analyzed;

Estimation block, for according to described logging trace and described experimental determination data, estimates factor of porosity and the water saturation in described region to be analyzed;

Analysis module, obtains XRD data for carrying out X ray diffracting data analysis to the rock sample obtained from described region to be analyzed;

TOC assay module, for analyzing described rock sample, obtains the TOC assay data of described region to be analyzed rock sample;

Density determination module, for obtaining volume and the quality of described rock sample, and obtains inorganic mineral density and TOC density according to described volume and Mass Calculation;

TOC densimetric curve fitting module, for the well point place in described region to be analyzed, corresponding relation between the TOC assay data determining described logging trace and described rock sample, and using the forecast model of described corresponding relation as described well point place TOC content data, obtain a continuous print TOC densimetric curve according to described forecast model matching;

Rock skeleton model building module, for setting up the rock skeleton model characterized by inorganic mineral density and TOC density, wherein, described inorganic mineral density deducts described TOC assay data by described XRD data and obtains, and described TOC density is the numerical value that described TOC densimetric curve characterizes;

Rock density determination module, for according to described logging trace, described factor of porosity, described water saturation, described inorganic mineral density and described TOC density, sets up the rock density that described rock skeleton model characterizes;

Correction module, for the densimetric curve in the rock density that characterizes according to described rock skeleton model and described logging trace, set up objective function, utilize optimality analysis, by the mode of iteration correction, described TOC densimetric curve is corrected, obtain the TOC densimetric curve after correcting;

Quantitative relationship determination module, for setting up the quantitative relationship between speed and elastic modulus according to described rock skeleton model;

TOC measures version and sets up module, described rock sample is measured to the speed, elastic modulus, the TOC data that obtain, and described quantitative relationship determination micro-scale is to the elastic parameter of TOC sensitivity for experimentally room, sets up the TOC amount version based on sensibility elasticity parameter;

Statistical relationship determination module, for calculating described well point place to the elastic parameter of TOC sensitivity according to described TOC amount version and described logging trace, and according to described well point place to the TOC densimetric curve after the elastic parameter of TOC sensitivity and described correction, therefrom see the statistical relationship that dimension calculation obtains the degree of correlation between elastic parameter and TOC density;

OVT territory processing module, for obtaining the geological data in described region to be analyzed, and carries out the process of OVT territory to described geological data;

Inverting module, for carrying out pre-stack seismic inversion to the geological data carried out after the process of OVT territory and described logging trace, obtains the data volume of the described elastic parameter to TOC sensitivity;

Modular converter, for according to described statistical relationship, is converted to TOC content spatial distribution data body by the data volume of the described elastic parameter to TOC sensitivity;

Determination module, for obtaining TOC content space distribution according to described TOC content spatial distribution data body.

In one embodiment, described data acquisition module comprises:

Well-log information acquiring unit, for obtaining one or more well-log informations in described region to be analyzed, wherein, described well-log information comprises following one or combination in any: compressional wave time difference, shear wave slowness, density, neutron, resistivity, natural gamma, natural gamma spectra uranium;

Logging trace generation unit, for generating described logging trace according to the described one or more well-log information obtained.

In one embodiment, described estimation block comprises:

Unit set up by model, for according to described logging trace, sets up factor of porosity appraising model and water saturation appraising model:

S _w＝f _sw(y ₁,y ₂,…y _n)

Wherein, represent total porosity, S _wbe expressed as water saturation, x ₁, x ₂... x _nrepresent measured data and the logging trace for carrying out factor of porosity estimation, y ₁, y ₂... y _nrepresent the logging trace participating in estimation water saturation, n represents the number of the data participating in estimation, and f _swrepresent polynary mapping function;

Evaluation unit, for obtaining factor of porosity according to described factor of porosity appraising model estimation, obtains water saturation according to described water saturation appraising model estimation.

In one embodiment, described correction module comprises:

Response equation sets up unit, for setting up response equation according to described rock skeleton model:

Wherein, ρ represents the lithosome density theory value that response equation calculates, and K represents TOC percent by volume, ρ _nkrepresent inorganic mineral density, ρ _tOCrepresent TOC density, represent total porosity, ρ _orepresent oil density, ρ _wrepresent water-mass density, S _wrepresent water saturation;

Objective function sets up unit, for according to the densimetric curve in described rock density and described logging trace, sets up following objective function:

minF(x)＝min(ρ-ρ _b) ²+g(x)

g(x)＝10 ⁶[|x|-x] ²+10 ⁶[|4-x|-(4-x)] ²

0≤x≤4

Wherein, F (x) represents objective function, ρ _brepresent practical logging density, g (x) represents bound term, and min represents and makes objective function reach minimal value by bound term;

Correcting unit, for carrying out iteration correction using TOC density as the x in described objective function, obtains the TOC densimetric curve after correcting.

In this example, utilize the X ray diffracting data of experimental determination, Organic Carbon Density, the data such as well logging densimetric curve set up the Minerals And Rocks physical model that TOC optimizes estimation, obtain aboveground TOC accurately by the mode of Optimized Iterative to distribute, then utilize actual measurement TOC data and equivalent medium mode to set up TOC and measure version, determine TOC sensibility elasticity parameter, prestack elastic parameter inversion carried out by seismic reflection road collection finally for the process of OVT territory, adopt the method for statistical fit, achieve the object utilizing well shake in conjunction with quantitative forecast TOC content space distribution, solve the not high technical matters that predicts the outcome of TOC content in prior art, by effectively using experimental determination data and petrophysical model, estimation result is not only coincide at rock sample place and experimental determination value, and TOC content without sampling point place can be predicted more accurately, can effectively check and correct the precision of the TOC content of logging trace prediction, greatly reduce the error of prediction.

Accompanying drawing explanation

Accompanying drawing described herein is used to provide a further understanding of the present invention, forms a application's part, does not form limitation of the invention.In the accompanying drawings:

Fig. 1 is the method flow diagram of the determination TOC content according to the embodiment of the present invention;

Fig. 2 is the seismic channel set data after processing according to the OVT territory of the embodiment of the present invention;

Fig. 3 is natural gamma spectra uranium curve according to the embodiment of the present invention and lithology sampling point statistical fit schematic diagram;

Fig. 4 is the uranium curve TOC according to the embodiment of the present invention, the TOC after petrophysical model corrects and actual measurement TOC error comparison diagram;

Fig. 5 sets up TOC according to the equivalent medium mode of the embodiment of the present invention to measure version process flow diagram;

Fig. 6 is according to the earthquake prediction TOC content of the embodiment of the present invention and checking well experimental determination sample value comparative analysis figure;

Fig. 7 is the apparatus structure block diagram of the determination TOC content according to the embodiment of the present invention.

Embodiment

For making the object, technical solutions and advantages of the present invention clearly understand, below in conjunction with embodiment and accompanying drawing, the present invention is described in further details.At this, exemplary embodiment of the present invention and illustrating for explaining the present invention, but not as a limitation of the invention.

Inventor considers, why error is comparatively large for the existing method determining TOC content, mainly there is following problem:

1) do not make full use of experimental determination data, except make use of rock sample TOC content analysis data, the micro-datas such as mineralogical composition content, physical parameter do not take in the impact that TOC content is estimated;

2) suitable petrophysical model is not set up;

3) seismic prediction technique for fine and close hydrocarbon-bearing pool Effective source rocks TOC content is considerably less, and earthquake prediction application is almost blank, and technology conventional is at present all analyze the qualitative relationships of TOC and log response.

For this reason, provide a kind of method determining TOC content in this example, utilize Rock physical analysis surge well to shake data quantitative prediction TOC content, indirectly characterize the distribution of fine and close hydrocarbon-bearing pool Effective source rocks.First, utilize laboratory rock sample determination data foundation based on the petrophysical model of mineral component, the TOC that matching obtains is corrected, obtains the best distribution of aboveground TOC; Then utilize the TOC data of actual measurement and equivalent medium mode to set up TOC and measure version, determine TOC sensibility elasticity parameter, and statistics obtains the quantitative relationship of the two; Finally, utilize OVT territory to process the seismic channel set obtained and carry out prestack inversion to obtain elastic parameter data volume, statistical relationship is utilized to change elastic parameter data volume thus obtain the space distribution of TOC, to realize the quantitative forecast of fine and close hydrocarbon-bearing pool Effective source rocks earthquake.

Concrete, as shown in Figure 1, this determines that the method for TOC content can comprise the following steps:

Step 101: the logging trace and the experimental determination data that obtain region to be analyzed;

Step 102: according to described logging trace and described experimental determination data, estimates factor of porosity and the water saturation in described region to be analyzed;

Step 103: X ray diffracting data analysis is carried out to the rock sample obtained from described region to be analyzed and obtains XRD data;

Step 104: analyze described rock sample, obtains the TOC assay data of described region to be analyzed rock sample;

Step 105: the volume and the quality that obtain described rock sample, and obtain inorganic mineral density and TOC density according to described volume and Mass Calculation;

Step 106: at the well point place in described region to be analyzed, corresponding relation between the TOC assay data determining described logging trace and described rock sample, and using the forecast model of described corresponding relation as described well point place TOC content data, obtain a continuous print TOC densimetric curve according to described forecast model matching;

Step 107: set up the rock skeleton model characterized by inorganic mineral density and TOC density, wherein, described inorganic mineral density deducts described TOC assay data by described XRD data and obtains, described TOC density is the numerical value that described TOC densimetric curve characterizes;

Step 108: according to described logging trace, described factor of porosity, described water saturation, described inorganic mineral density and described TOC density, sets up the rock density that described rock skeleton model characterizes;

Step 109: the densimetric curve in the rock density characterized according to described rock skeleton model and described logging trace, set up objective function, utilize optimality analysis, by the mode of iteration correction, described TOC densimetric curve is corrected, obtain the TOC densimetric curve after correcting;

Step 110: set up the quantitative relationship between speed and elastic modulus according to described rock skeleton model;

Step 111: experimentally room measures the speed, elastic modulus, the TOC data that obtain to described rock sample, and described quantitative relationship determination micro-scale is to the elastic parameter of TOC sensitivity, the TOC set up based on sensibility elasticity parameter measures version;

Step 112: calculate described well point place to the elastic parameter of TOC sensitivity according to described TOC amount version and described logging trace, and according to described well point place to the TOC densimetric curve after the elastic parameter of TOC sensitivity and described correction, therefrom see the statistical relationship that dimension calculation obtains the degree of correlation between elastic parameter and TOC density;

Step 113: the geological data obtaining described region to be analyzed, and the process of OVT territory is carried out to described geological data;

Step 114: carry out pre-stack seismic inversion to the geological data carried out after the process of OVT territory and described logging trace, obtains the data volume of the described elastic parameter to TOC sensitivity;

Step 115: according to described statistical relationship, is converted to TOC content spatial distribution data body by the data volume of the described elastic parameter to TOC sensitivity;

Step 116: obtain TOC content space distribution according to described TOC content spatial distribution data body.

Consider that logging trace is the well location data characterizing well situation, logging trace can be made up of multiple well-log information, and such as, horizontal compressional wave time difference, density, resistivity etc. can as well-log informations.Therefore, in above-mentioned steps 101, the logging trace obtaining region to be analyzed can comprise: the one or more well-log informations obtaining described region to be analyzed, wherein, described well-log information can include but not limited to following one or combination in any: compressional wave time difference, shear wave slowness, density, neutron, resistivity, natural gamma, natural gamma spectra uranium; Then, then according to the one or more well-log informations obtained generate logging trace.

But it should be noted that above-mentioned cited well-log information is only to better the present invention is described, do not form inappropriate limitation of the present invention, when reality uses, can also adopt other well-log information, the application is not construed as limiting this.

In order to describe better the method for the above-mentioned TOC of determination content, provide a specific embodiment in this example, but it should be noted that this specific embodiment is only to better the present invention is described, do not form inappropriate limitation of the present invention.

In this example, determine that the method for TOC content can comprise:

S1: adopt comprehensive, high-density sampling, to collect high-quality geological data.Consider that region to be analyzed is tight stratum, therefore can carry out the process of OVT territory to geological data, carry out denoising, protect width, fidelity process, to form the common midpoint gather comprising position angle and offset distance information.Fig. 2 is pre-stack time migration road, section point OVT territory, the tight stratum collection in region to be analyzed, wherein, oblique line represents azimuthal variation information, and horizontal line represents offset distance information, vertical line represents and road collection is divided into 5 regions, the reflective information of different orientations during each region representation same offset distance.This road collection comprising different offset distance and different azimuth angle information can improve the precision of prestack inversion elastic parameter.

Wherein, the process of OVT territory is that to form OVT slice of vector, this data processing method effectively can reduce the multi-solution in data handling procedure to geological data according to perpendicular offset and the layout again of seisline distance.

S2: gather the well-log informations such as the compressional wave time difference in this district, shear wave slowness, density, neutron, resistivity, natural gamma, natural gamma spectra uranium, obtain logging trace.

Wherein, some logging trace can utilize many attributes carry out nonlinear neural network training obtain.Such as: shear wave slowness curve just can be obtained by other well-log information training.But the prerequisite of the training of this many attributes nonlinear neural network is: a bite well must be had at least can to collect this curve.

S3: by the logging trace collected, utilizes multivariate statistical method can set up following factor of porosity and water saturation forecast model:

S _w＝f _sw(y ₁,y ₂,…y _n)

Wherein, represent total porosity, S _wrepresent water saturation, x ₁, x ₂... x _nrepresent the experimental determination data and logging trace that participate in estimation, y ₁, y ₂... y _nrepresent the logging trace participating in estimation, n represents the number of parameters participating in estimation, and f _swrepresent the polynary mapping function of estimation total porosity and water saturation respectively.

Such as, in this example, factor of porosity estimation equation is:

Wherein, represent total porosity, 2.85 is experimental determination matrix density, and 1.09 is experimental determination fluid density, ρ _obsfor well logging measures density.

The water saturation forecast model set up is:

$S_w=-37.281g\left(CNL\right)-0.66\sqrt{RD}-52974.4\frac{1}{DT}+0.093SDT+0.012{\mathrm{GR}}^2$

R＝0.737

Wherein, S _wfor water saturation, CNL is well logging compensated neutron curve, RD is well logging resistivity curve, DT is well logging sonic differential time curve, SDT is well logging shear wave slowness curve, and GR is gamma ray curve of logging well, and the degree of correlation of predicted value and actual value reaches 0.737, the goodness of fit is higher, can meet the demand of subsequent prediction work.

S4: carry out X ray diffracting data (XRD, X-Raydiffraction) analysis to the rock sample obtained, can obtain mineralogical composition percentage composition data, as shown in table 1 below is the part XRD data of certain well point interval.

Table 1

S5: based on the mineralogical composition percentage composition data obtained, analyze rock sample in laboratory, obtains rock core sampling point TOC content data.

S6: the volume of experimental determination rock sample and quality, calculate inorganic mineral density and Organic Carbon Density (TOC density), as the parameter in follow-up petrophysical model, particularly, can according to following formulae discovery inorganic mineral density and Organic Carbon Density:

Q _nk＝Q-Q _TOC

${\mathrm{\ρ}}_{nk}=\frac{Q_{nk}}{V}$

${\mathrm{\ρ}}_{TOC}=\frac{Q_{TOC}}{V}$

Wherein, Q represents rock sample gross mass, Q _nkrepresent inorganic mineral quality, Q _tOCrepresent and measure organic carbon content.

S7: utilize linear or between Nonlinear Statistical method determination natural gamma spectra uranium curve and rock core sampling point TOC density data relation at well point place, set up following TOC content prediction model:

ρ _TOC＝f(U)

In this example, the TOC content prediction model set up is:

ρ _TOC＝10 ^{(0.4104-60.74/(1+U/0.007565))}

R＝0.875

Wherein, ρ _tOCrepresent TOC density, U represents natural gamma spectra uranium curve, and f represents mapping function.Be illustrated in figure 3 rock sample TOC density and uranium curve statistical fit correlation schematic diagram in many mouthfuls of wells, as can be seen from Figure, the two is relation exponentially, and the degree of correlation reaches 0.875, illustrates that forecast model is comparatively reasonable.

Further, in order to solve the considerably less problem of laboratory actual measurement TOC content sampling point data, by the means of statistics, a continuous print Organic Carbon Density curve can tentatively be obtained.

S8: the rock skeleton model setting up an inorganic mineral and organic carbon composition, as shown in Figure 5.Wherein, the inorganic mineral density calculated in above-mentioned S6 is as the inorganic mineral density in model, and the TOC density that in above-mentioned S7, matching obtains is as the Organic Carbon Density in model.

The object of rock skeleton model, mainly in order to correct the Organic Carbon Density curve obtained in S7, contains discharge curve to obtain minimum, that other estimation point place precision of predictions are a high TOC of rock sampling point place error.

Wherein, the above-mentioned Minerals And Rocks skeleton pattern based on mineralogical composition percentage composition, inorganic mineral constituents in model deducts TOC determination data by XRD determining data and obtains, because XRD and TOC determination data unit is mass percent, therefore can be understood as each composition percentage in lithologic unit quality, thus set up TOC normalization response equation and be:

In this example, response equation is specifically as follows:

Wherein, ρ represents the lithosome density value that response equation calculates, ρ _nkrepresent inorganic mineral density, ρ _orepresent oil density, ρ _wrepresent water-mass density, ρ _tOCrepresent TOC density, represent total porosity, S _wrepresent water saturation, K represents TOC percent by volume.In this response equation, the Section 1 in the polynomial expression on the right of equation represents rock skeleton volume density, and Section 2 represents fluid volume density.

In order to the TOC density obtained matching in S7 corrects, can by setting up objective function, by ρ _tOCiterate as parametric variable, carry out optimization estimation, thus make the density p that calculates in S8 and practical logging density p _binfinitely close, thus make the TOC after correcting mate the best with laboratory sampling point TOC measured value.If ρ _tOCfor variable x, in optimization computation, objective function is:

minF(x)＝min(ρ-ρ _b) ²+g(x)

Wherein, x is ρ _tOC, F (x) is objective function, and g (x) is bound term, objective function can be made to reach minimal value by bound term.At experiment block, experimental determination TOC numerical value is substantially in [0,4] scope, and therefore constraint condition can be set as:

0≤x≤4

Then bound term can be expressed as:

g(x)＝10 ⁶[|x|-x] ²+10 ⁶[|4-x|-(4-x)] ²

Like this when optimizing the x calculated and not meeting constraint condition, bound term will be a very large numerical value, make objective function not reach minimal value.

ρ in objective function _bobtained by S2, and S _wobtained by S3, ρ _tOCand ρ _nktentatively obtained by S6, ρ _oand ρ _wfor actual production measurement data.

As shown in Figure 4, for uranium curve TOC, TOC after petrophysical model corrects and actual measurement TOC error comparison diagram, contrast display: the TOC that calculates is much smaller with aboveground TOC error of surveying after petrophysical model corrects, can meet the needs that latter earthquake is predicted.

The life measured containing discharge curve and rock sample model assessment TOC, the Hydrocarbon yield point interpolation curve that falls apart contrasts, display: the two distribution trend is consistent, and the TOC content that primary rock producing hydrocarbon, Hydrocarbon yield calculate compared with large regions comparatively greatly, proves the model calculation comparatively accurate and effective.

S9: utilize actual measurement TOC data and equivalent medium mode to set up TOC and measure version, determine TOC sensibility elasticity parameter from micro-scale, wherein, this sensibility elasticity parameter can be modulus of shearing * density (μ ρ).

Be illustrated in figure 5 and adopt equivalent medium mode to set up speed and elastic modulus relation process flow diagram.Particularly, can comprise: first, adopt bulk modulus and the modulus of shearing of Hashin-Shtrikman boundary determination rock skeleton, this boundary has the advantages that span is little, computational accuracy is high; Secondly, adopt bulk modulus and the modulus of shearing of K-T formulae discovery dry core sample, the method is applicable to factor of porosity situation little especially; Then, under adopting the calculating of Gassmann equation to be full of regimen condition, the bulk modulus of rock and modulus of shearing; Finally, the quantitative relationship between speed and elastic modulus is calculated.

On the basis determining speed and elastic modulus relation, just can analyze the relation between the various elastic parameter in aboveground actual measurement TOC sampling point place, set up TOC and measure version, thus determine that TOC sensibility elasticity parameter is modulus of shearing * density (μ ρ) from micro-scale.

S10: utilize logging trace to calculate μ ρ to well point place zone of interest, the TOC curve obtained after utilizing model tuning, therefrom see the relation of dimensional analysis μ ρ and TOC, objective interval corrects TOC and the μ ρ degree of correlation obtained can reach 0.89, therefore can meet the demand of subsequent prediction completely.

In this example, linear statistical method is adopted to obtain fitting formula:

C _TOC＝-0.02954*μρ+5.21639

R＝0.89

TOC curve after curve matching obtained and model tuning contrasts, and can find that both distribution trends are consistent, numerical values recited is close, therefore, prove that statistical fit result is effective.

S11: the seismic channel set utilizing OVT territory to process to obtain and well-log information carry out pre-stack seismic inversion, obtains the data volume of elastic parameter μ ρ.

S12: utilize the statistical relationship that S10 determines, is converted to the data volume of TOC content space distribution by elastic parameter data volume.As shown in Figure 6, be earthquake prediction TOC content and checking well sample point measured value comparison diagram, the two has good consistance, also just proves that the method is effective.

Generally speaking, be exactly obtain log data and relevant laboratory data, then related data matching is utilized to obtain an initial TOC densimetric curve, set up rock skeleton model, the effect of this model is exactly correct above-mentioned TOC densimetric curve, estimation obtains one and thinks closest to actual TOC densimetric curve, EFFECTIVE MEDIUM THEORY etc. is subsequently utilized to obtain being suitable for evaluating objects, to the elastic parameter of TOC sensitivity, aboveground curve is utilized to calculate TOC sensibility elasticity parameter, determine the statistical relationship between this elastic parameter and TOC density, final utilization well logging, this elastic parameter of seismic data inversion, then utilize statistical relationship above that elastic parameter is converted to TOC density, so just obtain the space distribution of TOC density.

In the above-described embodiments, take full advantage of experimental determination data, comprising: the petrophysical parameter etc. of rock core sampling point TOC content data, XRD analysis data, Estimation of porosity and water saturation needs; By the method for rock physics modeling, establish the deterministic dependence between laboratory rock sample determination data and well logging density, thus the TOC content prediction result at well point place is not only coincide with laboratory rock sample measurement result, also ensure that the accuracy of other depth estimation result of well point, avoid the randomness in current correlation technique.Further, the TOC utilizing actual measurement TOC data and equivalent medium mode to set up based on mineral constituent measures version, determines TOC sensibility elasticity parameter, adds up the relation established between TOC and sensitive parameter.In embodiments of the present invention, take full advantage of experimental data, geological data and log data, achieve Rock physical analysis to estimate and the combination of earthquake prediction three with well logging, reach the object that micro-data instructs macro-forecast, and in production application, show that the method has good geological effect in the prediction of fine and close hydrocarbon-bearing pool Effective source rocks.

Based on same inventive concept, additionally provide a kind of device determining TOC content in the embodiment of the present invention, as described in the following examples.Similar to determining the method for TOC content owing to determining the principle that the device of TOC content is dealt with problems, therefore determine that the enforcement of the device of TOC content see the enforcement of method determining TOC content, can repeat part and repeat no more.Following used, term " unit " or " module " can realize the software of predetermined function and/or the combination of hardware.Although the device described by following examples preferably realizes with software, hardware, or the realization of the combination of software and hardware also may and conceived.Fig. 7 is a kind of structured flowchart of the device of the determination TOC content of the embodiment of the present invention, as shown in Figure 7, comprise: data acquisition module 701, estimation block 702, analysis module 703, TOC assay module 704, density determination module 705, TOC densimetric curve fitting module 706, rock skeleton model building module 707, rock density determination module 708, correction module 709, quantitative relationship determination module 710, TOC measures version and sets up module 711, statistical relationship determination module 712, OVT territory processing module 713, inverting module 714, modular converter 715 and determination module 716, below this structure is described.

Data acquisition module 701, for obtaining logging trace and the experimental determination data in region to be analyzed;

Estimation block 702, for according to described logging trace and described experimental determination data, estimates factor of porosity and the water saturation in described region to be analyzed;

Analysis module 703, obtains XRD data for carrying out X ray diffracting data analysis to the rock sample obtained from described region to be analyzed;

TOC assay module 704, for analyzing described rock sample, obtains the TOC assay data of described region to be analyzed rock sample;

Density determination module 705, for obtaining volume and the quality of described rock sample, and obtains inorganic mineral density and TOC density according to described volume and Mass Calculation;

TOC densimetric curve fitting module 706, for the well point place in described region to be analyzed, corresponding relation between the TOC assay data determining described logging trace and described rock sample, and using the forecast model of described corresponding relation as described well point place TOC content data, obtain a continuous print TOC densimetric curve according to described forecast model matching;

Rock skeleton model building module 707, for setting up the rock skeleton model characterized by inorganic mineral density and TOC density, wherein, described inorganic mineral density deducts described TOC assay data by described XRD data and obtains, and described TOC density is the numerical value that described TOC densimetric curve characterizes;

Rock density determination module 708, for according to described logging trace, described factor of porosity, described water saturation, described inorganic mineral density and described TOC density, sets up the rock density that described rock skeleton model characterizes;

Correction module 709, for the densimetric curve in the rock density that characterizes according to described rock skeleton model and described logging trace, set up objective function, utilize optimality analysis, by the mode of iteration correction, described TOC densimetric curve is corrected, obtain the TOC densimetric curve after correcting;

Quantitative relationship determination module 710, for setting up the quantitative relationship between speed and elastic modulus according to described rock skeleton model;

TOC measures version and sets up module 711, described rock sample is measured to the speed, elastic modulus, the TOC data that obtain, and described quantitative relationship determination micro-scale is to the elastic parameter of TOC sensitivity for experimentally room, sets up the TOC amount version based on sensibility elasticity parameter;

Statistical relationship determination module 712, for calculating described well point place to the elastic parameter of TOC sensitivity according to described TOC amount version and described logging trace, and according to described well point place to the TOC densimetric curve after the elastic parameter of TOC sensitivity and described correction, therefrom see the statistical relationship that dimension calculation obtains the degree of correlation between elastic parameter and TOC density;

OVT territory processing module 713, for obtaining the geological data in described region to be analyzed, and carries out the process of OVT territory to described geological data;

Inverting module 714, for carrying out pre-stack seismic inversion to the geological data carried out after the process of OVT territory and described logging trace, obtains the data volume of the described elastic parameter to TOC sensitivity;

Modular converter 715, for according to described statistical relationship, is converted to TOC content spatial distribution data body by the data volume of the described elastic parameter to TOC sensitivity;

Determination module 716, for obtaining TOC content space distribution according to described TOC content spatial distribution data body.

In one embodiment, data acquisition module 701 can comprise:

Well-log information acquiring unit, for obtaining one or more well-log informations in described region to be analyzed, wherein, described well-log information comprises following one or combination in any: compressional wave time difference, shear wave slowness, density, neutron, resistivity, natural gamma, natural gamma spectra uranium;

Logging trace generation unit, for generating described logging trace according to the described one or more well-log information obtained.

In one embodiment, estimation block 702 can comprise: unit set up by model, for according to described logging trace, sets up factor of porosity appraising model and water saturation appraising model:

S _w＝f _sw(y ₁,y ₂,…y _n)

Wherein, represent total porosity, S _wbe expressed as water saturation, x ₁, x ₂... x _nrepresent measured data and the logging trace for carrying out factor of porosity estimation, y ₁, y ₂... y _nrepresent the logging trace participating in estimation water saturation, n represents the number of the data participating in estimation, and f _swrepresent polynary mapping function;

Evaluation unit, for obtaining factor of porosity according to described factor of porosity appraising model estimation, obtains water saturation according to described water saturation appraising model estimation.

In one embodiment, correction module 709 can comprise: response equation sets up unit, for setting up response equation according to described rock skeleton model:

Wherein, ρ represents the lithosome density theory value that response equation calculates, and K represents TOC percent by volume, ρ _nkrepresent inorganic mineral density, ρ _tOCrepresent TOC density, represent total porosity, ρ _orepresent oil density, ρ _wrepresent water-mass density, S _wrepresent water saturation;

Objective function sets up unit, for according to the densimetric curve in described rock density and described logging trace, sets up following objective function:

minF(x)＝min(ρ-ρ _b) ²+g(x)

g(x)＝10 ⁶[|x|-x] ²+10 ⁶[|4-x|-(4-x)] ²

0≤x≤4

Wherein, F (x) represents objective function, ρ _brepresent practical logging density, g (x) represents bound term, and min represents and makes objective function reach minimal value by bound term;

Correcting unit, for carrying out iteration correction using TOC density as the x in described objective function, obtains the TOC densimetric curve after correcting.

In another embodiment, additionally provide a kind of software, this software is for performing the technical scheme described in above-described embodiment and preferred implementation.

In another embodiment, additionally provide a kind of storage medium, store above-mentioned software in this storage medium, this storage medium includes but not limited to: CD, floppy disk, hard disk, scratch pad memory etc.

From above description, can find out, the embodiment of the present invention achieves following technique effect: the X ray diffracting data utilizing experimental determination, Organic Carbon Density, the data such as well logging densimetric curve set up the Minerals And Rocks physical model that TOC optimizes estimation, obtain aboveground TOC accurately by the mode of Optimized Iterative to distribute, then utilize actual measurement TOC data and equivalent medium mode to set up TOC and measure version, determine TOC sensibility elasticity parameter, prestack elastic parameter inversion carried out by seismic reflection road collection finally for the process of OVT territory, adopt the method for statistical fit, achieve the object utilizing well shake in conjunction with quantitative forecast TOC content space distribution, solve the not high technical matters that predicts the outcome of TOC content in prior art, by effectively using experimental determination data and petrophysical model, estimation result is not only coincide at rock sample place and experimental determination value, and TOC content without sampling point place can be predicted more accurately, can effectively check and correct the precision of the TOC content of logging trace prediction, greatly reduce the error of prediction.

Obviously, those skilled in the art should be understood that, each module of the above-mentioned embodiment of the present invention or each step can realize with general calculation element, they can concentrate on single calculation element, or be distributed on network that multiple calculation element forms, alternatively, they can realize with the executable program code of calculation element, thus, they can be stored and be performed by calculation element in the storage device, and in some cases, step shown or described by can performing with the order be different from herein, or they are made into each integrated circuit modules respectively, or the multiple module in them or step are made into single integrated circuit module to realize.Like this, the embodiment of the present invention is not restricted to any specific hardware and software combination.

The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for a person skilled in the art, the embodiment of the present invention can have various modifications and variations.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (10)

1. determine a method for TOC content, it is characterized in that, comprising:

Obtain logging trace and the experimental determination data in region to be analyzed;

According to described logging trace and described experimental determination data, estimate factor of porosity and the water saturation in described region to be analyzed;

X ray diffracting data analysis is carried out to the rock sample obtained from described region to be analyzed and obtains XRD data;

Described rock sample is analyzed, obtains the TOC assay data of described region to be analyzed rock sample;

Obtain volume and the quality of described rock sample, and obtain inorganic mineral density and TOC density according to described volume and Mass Calculation;

At the well point place in described region to be analyzed, corresponding relation between the TOC assay data determining described logging trace and described rock sample, and using the forecast model of described corresponding relation as described well point place TOC content data, obtain a continuous print TOC densimetric curve according to described forecast model matching;

Set up the rock skeleton model characterized by inorganic mineral density and TOC density, wherein, described inorganic mineral density deducts described TOC assay data by described XRD data and obtains, and described TOC density is the numerical value that described TOC densimetric curve characterizes;

According to described logging trace, described factor of porosity, described water saturation, described inorganic mineral density and described TOC density, set up the rock density that described rock skeleton model characterizes;

Densimetric curve in the rock density characterized according to described rock skeleton model and described logging trace, is set up objective function, utilizes optimality analysis, corrected by the mode of iteration correction to described TOC densimetric curve, obtain the TOC densimetric curve after correcting;

The quantitative relationship between speed and elastic modulus is set up according to described rock skeleton model;

Experimentally room measures the speed, elastic modulus, the TOC data that obtain to described rock sample, and described quantitative relationship determination micro-scale is to the elastic parameter of TOC sensitivity, and the TOC set up based on sensibility elasticity parameter measures version;

Described well point place is calculated to the elastic parameter of TOC sensitivity according to described TOC amount version and described logging trace, and according to described well point place to the TOC densimetric curve after the elastic parameter of TOC sensitivity and described correction, therefrom see the statistical relationship that dimension calculation obtains the degree of correlation between elastic parameter and TOC density;

Obtain the geological data in described region to be analyzed, and the process of OVT territory is carried out to described geological data;

Pre-stack seismic inversion is carried out to the geological data carried out after the process of OVT territory and described logging trace, obtains the data volume of the described elastic parameter to TOC sensitivity;

According to described statistical relationship, the data volume of the described elastic parameter to TOC sensitivity is converted to TOC content spatial distribution data body;

TOC content space distribution is obtained according to described TOC content spatial distribution data body.

2. the method for claim 1, is characterized in that, obtains the logging trace in region to be analyzed, comprising:

Obtain one or more well-log informations in described region to be analyzed, wherein, described well-log information comprises following one or combination in any: compressional wave time difference, shear wave slowness, density, neutron, resistivity, natural gamma, natural gamma spectra uranium;

Described one or more well-log information according to obtaining generates described logging trace.

3. the method for claim 1, is characterized in that, according to described logging trace, estimates factor of porosity and the water saturation in described region to be analyzed, comprising:

According to described logging trace, set up factor of porosity appraising model and water saturation appraising model:

S _w＝f _sw(y ₁,y ₂,…y _n)

Wherein, represent total porosity, S _wbe expressed as water saturation, x ₁, x ₂... x _nrepresent measured data and the logging trace for carrying out factor of porosity estimation, y ₁, y ₂... y _nrepresent the logging trace participating in estimation water saturation, n represents the number of the data participating in estimation, and f _swrepresent polynary mapping function;

Obtain factor of porosity according to described factor of porosity appraising model estimation, obtain water saturation according to described water saturation appraising model estimation.

4. the method for claim 1, is characterized in that, obtains inorganic mineral density and TOC density, comprising according to described volume and Mass Calculation:

According to following formulae discovery inorganic mineral density and TOC density:

Q _nk＝Q-Q _TOC

${\mathrm{\ρ}}_{nk}=\frac{Q_{nk}}{V}$

${\mathrm{\ρ}}_{TOC}=\frac{Q_{TOC}}{V}$

Wherein, Q represents the quality of dry core sample, Q _nkbe expressed as inorganic mineral quality, Q _tOCrepresent TOC quality, ρ _nkrepresent inorganic mineral density, ρ _tOCrepresent TOC density, V represents the volume of dry core sample.

5. the method for claim 1, is characterized in that, according to the densimetric curve in described rock density and described logging trace, set up objective function, utilize optimality analysis, by iteration correction TOC density correction rock density, to obtain the TOC densimetric curve after correcting, comprising:

Response equation is set up according to described rock skeleton model:

Wherein, ρ represents the lithosome density theory value that response equation calculates, and K represents TOC percent by volume, ρ _nkrepresent inorganic mineral density, ρ _tOCrepresent TOC density, represent total porosity, ρ _orepresent oil density, ρ _wrepresent water-mass density, S _wrepresent water saturation;

According to the densimetric curve in described rock density and described logging trace, set up following objective function:

minF(x)＝min(ρ-ρ _b) ²+g(x)

g(x)＝10 ⁶[|x|-x] ²+10 ⁶[|4-x|-(4-x)] ²

0≤x≤4

Wherein, F (x) represents objective function, ρ _brepresent practical logging density, g (x) represents bound term, and min represents and makes objective function reach minimal value by bound term;

Carry out iteration correction using TOC density as the x in described objective function, obtain the TOC densimetric curve after correcting.

6. the method for claim 1, is characterized in that, sets up the quantitative relationship between speed and elastic modulus, comprising according to described rock skeleton model:

Bulk modulus and the modulus of shearing of described rock skeleton model is determined by Hashin-Shtrikman boundary;

By bulk modulus and the modulus of shearing of the dry rock of K-T formulae discovery;

The bulk modulus and modulus of shearing that are full of rock under regimen condition is calculated by Gassmann equation;

By being full of bulk modulus and the modulus of shearing of rock under regimen condition, set up the quantitative relationship between speed and elastic modulus.

7. determine a device for TOC content, it is characterized in that, comprising:

Data acquisition module, for obtaining logging trace and the experimental determination data in region to be analyzed;

Estimation block, for according to described logging trace and described experimental determination data, estimates factor of porosity and the water saturation in described region to be analyzed;

Analysis module, obtains XRD data for carrying out X ray diffracting data analysis to the rock sample obtained from described region to be analyzed;

TOC assay module, for analyzing described rock sample, obtains the TOC assay data of described region to be analyzed rock sample;

Density determination module, for obtaining volume and the quality of described rock sample, and obtains inorganic mineral density and TOC density according to described volume and Mass Calculation;

TOC densimetric curve fitting module, for the well point place in described region to be analyzed, corresponding relation between the TOC assay data determining described logging trace and described rock sample, and using the forecast model of described corresponding relation as described well point place TOC content data, obtain a continuous print TOC densimetric curve according to described forecast model matching;

Rock skeleton model building module, for setting up the rock skeleton model characterized by inorganic mineral density and TOC density, wherein, described inorganic mineral density deducts described TOC assay data by described XRD data and obtains, and described TOC density is the numerical value that described TOC densimetric curve characterizes;

Rock density determination module, for according to described logging trace, described factor of porosity, described water saturation, described inorganic mineral density and described TOC density, sets up the rock density that described rock skeleton model characterizes;

Correction module, for the densimetric curve in the rock density that characterizes according to described rock skeleton model and described logging trace, set up objective function, utilize optimality analysis, by the mode of iteration correction, described TOC densimetric curve is corrected, obtain the TOC densimetric curve after correcting;

Quantitative relationship determination module, for setting up the quantitative relationship between speed and elastic modulus according to described rock skeleton model;

TOC measures version and sets up module, described rock sample is measured to the speed, elastic modulus, the TOC data that obtain, and described quantitative relationship determination micro-scale is to the elastic parameter of TOC sensitivity for experimentally room, sets up the TOC amount version based on sensibility elasticity parameter;

Statistical relationship determination module, for calculating described well point place to the elastic parameter of TOC sensitivity according to described TOC amount version and described logging trace, and according to described well point place to the TOC densimetric curve after the elastic parameter of TOC sensitivity and described correction, therefrom see the statistical relationship that dimension calculation obtains the degree of correlation between elastic parameter and TOC density;

OVT territory processing module, for obtaining the geological data in described region to be analyzed, and carries out the process of OVT territory to described geological data;

Inverting module, for carrying out pre-stack seismic inversion to the geological data carried out after the process of OVT territory and described logging trace, obtains the data volume of the described elastic parameter to TOC sensitivity;

Modular converter, for according to described statistical relationship, is converted to TOC content spatial distribution data body by the data volume of the described elastic parameter to TOC sensitivity;

Determination module, for obtaining TOC content space distribution according to described TOC content spatial distribution data body.

8. device as claimed in claim 7, it is characterized in that, described data acquisition module comprises:

Well-log information acquiring unit, for obtaining one or more well-log informations in described region to be analyzed, wherein, described well-log information comprises following one or combination in any: compressional wave time difference, shear wave slowness, density, neutron, resistivity, natural gamma, natural gamma spectra uranium;

Logging trace generation unit, for generating described logging trace according to the described one or more well-log information obtained.

9. device as claimed in claim 7, it is characterized in that, described estimation block comprises:

Unit set up by model, for according to described logging trace, sets up factor of porosity appraising model and water saturation appraising model:

S _w＝f _sw(y ₁,y ₂,…y _n)

Wherein, represent total porosity, S _wbe expressed as water saturation, x ₁, x ₂... x _nrepresent measured data and the logging trace for carrying out factor of porosity estimation, y ₁, y ₂... y _nrepresent the logging trace participating in estimation water saturation, n represents the number of the data participating in estimation, and f _swrepresent polynary mapping function;

Evaluation unit, for obtaining factor of porosity according to described factor of porosity appraising model estimation, obtains water saturation according to described water saturation appraising model estimation.

10. device as claimed in claim 7, it is characterized in that, described correction module comprises:

Response equation sets up unit, for setting up response equation according to described rock skeleton model:

Wherein, ρ represents the lithosome density theory value that response equation calculates, and K represents TOC percent by volume, ρ _nkrepresent inorganic mineral density, ρ _tOCrepresent TOC density, represent total porosity, ρ _orepresent oil density, ρ _wrepresent water-mass density, S _wrepresent water saturation;

Objective function sets up unit, for according to the densimetric curve in described rock density and described logging trace, sets up following objective function:

minF(x)＝min(ρ-ρ _b) ²+g(x)

g(x)＝10 ⁶[|x|-x] ²+10 ⁶[|4-x|-(4-x)] ²

0≤x≤4

Wherein, F (x) represents objective function, ρ _brepresent practical logging density, g (x) represents bound term, and min represents and makes objective function reach minimal value by bound term;

Correcting unit, for carrying out iteration correction using TOC density as the x in described objective function, obtains the TOC densimetric curve after correcting.