CN106338778B - A kind of shale petrofacies continuous prediction method based on well logging information - Google Patents

A kind of shale petrofacies continuous prediction method based on well logging information Download PDF

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CN106338778B
CN106338778B CN201610724474.XA CN201610724474A CN106338778B CN 106338778 B CN106338778 B CN 106338778B CN 201610724474 A CN201610724474 A CN 201610724474A CN 106338778 B CN106338778 B CN 106338778B
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蒋裕强
蒋婵
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Abstract

The invention discloses a kind of shale petrofacies continuous prediction method based on well logging information, the shale petrofacies continuous prediction method based on well logging information is that first shale Lithofacies Types are judged using the analytical test and thin slice data of coring well, the logging response character of shale Lithofacies Types can be reflected by finding out, with artificial neural network technology, using log shale Lithofacies Types are entered with Line Continuity prediction.Instant invention overcomes shale petrofacies are established only according to rock sample sampling analysis, the continuity attribute and its difference of shale are easily lost, the problem of causing shale attribute cognitive diversity；It is that a kind of cost is low and fast and effectively method using the method for well logging information prediction shale petrofacies.

Description

A kind of shale petrofacies continuous prediction method based on well logging information
Technical field
The invention belongs to shale gas development technique field, more particularly to a kind of shale petrofacies based on well logging information are continuously pre- Survey method.
Background technology
The division of shale petrofacies is more not to be applied to from origin cause of formation angular divisions, exploitation interval favourable to shale gas.
Rock sample sampling analysis establishes shale petrofacies, is easily lost the continuity attribute and its difference of shale, causes page Rock attribute cognitive diversity, with log interpretation method, there is also the explanation personnel's explanation results difference for judging to slip up more, different Problem.
The content of the invention
It is an object of the invention to provide a kind of shale petrofacies continuous prediction method based on well logging information, it is intended to solves rock Shale petrofacies are established in the analysis of stone sample, are easily lost the continuity attribute and its difference of shale, cause shale attribute to recognize The problem of difference.
The present invention is achieved in that a kind of shale petrofacies continuous prediction method based on well logging information, described based on survey The shale petrofacies continuous prediction method of well information is to utilize well logging information, using the analytical test and thin slice data of coring well to page Rock Lithofacies Types are judged, then according to the relation between shale petrofacies and logging response character, with artificial neural network Technology, establish the model using log parameter prediction shale Lithofacies Types；Space shale Lithofacies Types are entered with Line Continuity prediction.
Further, the shale petrofacies continuous prediction method based on well logging information comprises the following steps：
Step 1, shale Lithofacies Types are judged using the analytical test and thin slice data of coring well, by shale petrofacies It is divided into rich organic matter siliceous shale, rich organic matter carbonate matter shale, rich organic matter clay matter shale, the siliceous page of organic-lean 7 types such as rock, organic-lean's carbonate matter shale, organic-lean's clay matter shale, organic-lean's limestone, with A, B, C, D, E, F, G represents this 7 type, will be represented with 7 dimension row vectors after its quantification, as some sample point is determined as wherein certain one kind, Then the value in this one kind is 1, and the value in other classes is 0, such as a certain sample point belongs to A classes through judging, then uses Vectorial [1 00000 0] represent, belong to B classes, then are represented with vectorial [0 10000 0]；Then by log with The shale Lithofacies Types of corresponding depth are contrasted, and find out several Logging Curves that can reflect shale Lithofacies Types, as Predict the logging response character of shale Lithofacies Types；
Every kind of shale Lithofacies Types are found out with several corresponding well logging sample points, as establish forecast model Practise sample；
Shale Lithofacies Types and well-log information corresponding with depth using all sample points, with P log parameter [X1,X2,…,XP]TAs input, using corresponding shale Lithofacies Types as output, the artificial of prediction shale Lithofacies Types is established Neural network model；
Step 2, with artificial neural network technology, space shale petrofacies continuity is predicted, according to prediction result, drawn The shale lithofacies successions figure of the well section, and then determine the favourable exploitation interval of shale gas；
Step 3, according to prediction result, the shale lithofacies successions figure of the well section is drawn, and then determines the favourable of shale gas Develop interval.
Further, the model is (by taking A classes as an example)：
In formula：
M well logging sample points, use XAi=[XAi1,XAi2,…,XAip]TRepresent P log parameter of i-th of sample point；
I=mode numbers；
M=training modes sum；
XAi=type A the i-th training mode；
σ=smoothing parameter；
The dimension of P=metric spaces；
X=wants the parameter of some point of type of prediction.
Shale petrofacies continuous prediction method provided by the invention based on well logging information, overcome and sampled only according to rock sample Shale petrofacies are established in analysis, are easily lost the continuity attribute and its difference of shale, the problem of causing shale attribute cognitive diversity. The continuity prediction of the shale petrofacies of the present invention has important meaning for the favourable interval of preferred shale gas and Favorable exploration area, Such as the interval rich in organic matter and the block rich in organic matter are only favourable interval and block.The knowledge of traditional shale petrofacies It is not the method using core analysis and thin section identification, because coring well is less and coring cost is too high and analytical test item The more thin section identification of mesh is again time-consuming, and so being unfavorable for shale petrofacies, the prediction in (plane), the present invention examine in the vertical and laterally Consider the method using well logging information prediction shale petrofacies, be that a kind of cost is low and fast and effectively method, such as a bite well coring More than 1,000,000 yuan are at least wanted plus analytical test expense, and a bite borehole logging tool expense only needs 100,000 yuan or so.The present invention is according to page Relation between rock petrofacies and logging response character, with artificial neural network technology, establish and utilize log parameter prediction shale Lithographic model；Space shale petrofacies are entered Line Continuity prediction, not only greatly improve efficiency, and greatly saved into This.
Brief description of the drawings
Fig. 1 is the flow chart for the shale petrofacies continuous prediction method based on well logging information that the present invention implements offer.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to Limit the present invention.
The foundation that the present invention selection content of organic matter and two big parameter of mineral composition divide for shale petrofacies, first by organic matter Whether content marks off rich organic matter and organic-lean shale more than 2%；Again by clay, quartz and feldspar content, carbonate mine Thing content number establish the criteria for classifying of 7 kinds of shale Lithofacies Types, the present invention is using well logging information and passes through petrographical analysis Relation between the shale petrofacies of certification of registered capital material identification, with artificial neural network technology, establishes forecast model, carries out space shale The continuity prediction of petrofacies.Overcome and establish shale petrofacies only according to rock sample sampling analysis, be easily lost the continuity of shale Attribute and its difference, so as to cause the difference in shale attribute understanding.
The present invention is further illustrated below in conjunction with Fig. 1.
The shale petrofacies continuous prediction method based on well logging information of the embodiment of the present invention comprises the following steps：
Shale Lithofacies Types are judged using the analytical test and thin slice data of coring well, shale petrofacies are divided into It is rich organic matter siliceous shale, rich organic matter carbonate matter shale, rich organic matter clay matter shale, organic-lean's siliceous shale, poor 7 types such as organic matter carbonate matter shale, organic-lean's clay matter shale, organic-lean's limestone, in order to describe conveniently, below This 7 type is represented with A, B, C, D, E, F, G, will be represented after its quantification with 7 dimension row vectors, as some sample point is determined as Wherein certain is a kind of, then the value in this one kind is 1, and the value in other classes is 0, such as a certain sample point belongs to through judging In A classes, then represented with vectorial [1 00000 0], belong to B classes, then represented with vectorial [0 10000 0] ... ..., with This analogizes.
Then log is contrasted with the shale Lithofacies Types of corresponding depth, finding out several can reflect shale petrofacies The Logging Curves (parameter) of type, RT ... P such as sound wave AC, gamma HCGR, density DEN, resistivity log parameter, make To predict the logging response character (parameter) of shale Lithofacies Types.Facilitate below in order to describe with vectorial X=[X1,X2,…,XP]T Represent P log parameter.
Every kind of shale Lithofacies Types are found out with several corresponding well logging sample points, as establish forecast model Practise sample, as A classes have m log well sample point, then use XAi=[XAi1,XAi2,…,XAip]TRepresent the P of i-th of sample point of A classes Individual log parameter.Then the shale Lithofacies Types using all sample points and well-log information corresponding with depth, with P well logging Parameter [X1,X2,…,XP]TAs input, using corresponding shale Lithofacies Types as output, shale petrofacies class is predicted as establishing The learning sample of the artificial nerve network model of type.
Logging response character and distribution according to corresponding to different shale Lithofacies Types, utilize 7 kinds of shale rocks Learning sample corresponding to facies type establishes the forecast model of shale Lithofacies Types using probabilistic neural network method, such as A classes Forecast model is：
In formula:
I=mode numbers.
M=training modes sum.
XAi=type A the i-th training mode (sample point).
σ=smoothing parameter.
The dimension of P=metric spaces.
X=wants the parameter of some point of type of prediction.
The forecast model of B classes is：
The forecast model of G classes is：
It will predict that M log parameter of the well section of shale Lithofacies Types is substituted into above-mentioned model by depth order one by one, calculate Go out fA(X)、fB(X)、…、fG(X) maximum 1, is found out, if such as fA(x)=1, then the point is included into A classes, analogized with secondary.
According to prediction result, the shale lithofacies successions figure of the well section is drawn, and then determines the favourable development layer of shale gas Section.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention All any modification, equivalent and improvement made within refreshing and principle etc., should be included in the scope of the protection.

Claims (2)

  1. A kind of 1. shale petrofacies continuous prediction method based on well logging information, it is characterised in that the page based on well logging information Rock petrofacies continuous prediction method is that first shale Lithofacies Types are judged using the analytical test and thin slice data of coring well, so The model according to logging response character prediction shale Lithofacies Types is established afterwards；With artificial neural network technology, to space shale Lithofacies Types enter Line Continuity prediction；
    The shale petrofacies continuous prediction method based on well logging information comprises the following steps：
    Step 1, shale Lithofacies Types are judged using the analytical test and thin slice data of coring well, shale petrofacies are drawn It is divided into rich organic matter siliceous shale, rich organic matter carbonate matter shale, rich organic matter clay matter shale, the siliceous page of organic-lean Rock, organic-lean's carbonate matter shale, organic-lean's clay matter shale, the type of organic-lean's limestone 7, with A, B, C, D, E, F, G represents this 7 type, will be represented with 7 dimension row vectors after its quantification, as some sample point is determined as wherein certain one kind, then Value in this one kind is 1, and the value in other classes is 0, when a certain sample point belongs to A classes through judging, then with vector [1 00000 0] represent, belong to B classes, then represented with vectorial [0 10000 0]；Then by log with it is corresponding The shale Lithofacies Types of depth are contrasted, and several Logging Curves that can reflect shale Lithofacies Types are found out, as prediction The logging response character of shale Lithofacies Types；
    Step 2, every kind of shale Lithofacies Types are found out with several corresponding well logging sample points, as establishing forecast model Learning sample；
    Shale Lithofacies Types and well-log information corresponding with depth using all sample points, with P log parameter [X1, X2,…,XP]TAs input, using corresponding shale Lithofacies Types as output, the artificial neuron of prediction shale Lithofacies Types is established Network model；
    Step 3, with artificial neural network technology, space shale petrofacies continuity is predicted, according to prediction result, draws this The shale lithofacies successions figure of well section, and then determine the favourable exploitation interval of shale gas.
  2. 2. the shale petrofacies continuous prediction method based on well logging information as claimed in claim 1, it is characterised in that the A classes Model be：
    <mrow> <msub> <mi>f</mi> <mi>A</mi> </msub> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msup> <mrow> <mo>(</mo> <mn>2</mn> <mi>&amp;pi;</mi> <mo>)</mo> </mrow> <mrow> <mi>p</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> <msup> <mi>&amp;sigma;</mi> <mi>p</mi> </msup> </mrow> </mfrac> <mfrac> <mn>1</mn> <mi>m</mi> </mfrac> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </msubsup> <mi>exp</mi> <mo>&amp;lsqb;</mo> <mo>-</mo> <mfrac> <mrow> <msup> <mrow> <mo>(</mo> <mi>X</mi> <mo>-</mo> <msub> <mi>X</mi> <mrow> <mi>A</mi> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mrow> <mo>(</mo> <mi>X</mi> <mo>-</mo> <msub> <mi>X</mi> <mrow> <mi>A</mi> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mn>2</mn> <msup> <mi>&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> <mo>&amp;rsqb;</mo> <mo>;</mo> </mrow>
    In formula：
    M is well logging sample point, uses XAi=[XAi1,XAi2,…,XAip]TRepresent P log parameter of i-th of sample point；
    I=mode numbers；
    M=training modes sum；
    XAi=type A the i-th training mode；
    σ=smoothing parameter；
    The dimension of P=metric spaces；
    X=wants the parameter of some point of type of prediction；
    The model of B classes is：
    <mrow> <msub> <mi>f</mi> <mi>B</mi> </msub> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msup> <mrow> <mo>(</mo> <mn>2</mn> <mi>&amp;pi;</mi> <mo>)</mo> </mrow> <mrow> <mi>p</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> <msup> <mi>&amp;sigma;</mi> <mi>p</mi> </msup> </mrow> </mfrac> <mfrac> <mn>1</mn> <mi>m</mi> </mfrac> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </msubsup> <mi>exp</mi> <mo>&amp;lsqb;</mo> <mo>-</mo> <mfrac> <mrow> <msup> <mrow> <mo>(</mo> <mi>X</mi> <mo>-</mo> <msub> <mi>X</mi> <mrow> <mi>B</mi> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mrow> <mo>(</mo> <mi>X</mi> <mo>-</mo> <msub> <mi>X</mi> <mrow> <mi>B</mi> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mn>2</mn> <msup> <mi>&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> <mo>&amp;rsqb;</mo> <mo>;</mo> </mrow>
    The model of G classes is：
    <mrow> <msub> <mi>f</mi> <mi>G</mi> </msub> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msup> <mrow> <mo>(</mo> <mn>2</mn> <mi>&amp;pi;</mi> <mo>)</mo> </mrow> <mrow> <mi>p</mi> <mo>/</mo> <mn>2</mn> </mrow> </msup> <msup> <mi>&amp;sigma;</mi> <mi>p</mi> </msup> </mrow> </mfrac> <mfrac> <mn>1</mn> <mi>m</mi> </mfrac> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </msubsup> <mi>exp</mi> <mo>&amp;lsqb;</mo> <mo>-</mo> <mfrac> <mrow> <msup> <mrow> <mo>(</mo> <mi>X</mi> <mo>-</mo> <msub> <mi>X</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> <mi>T</mi> </msup> <mrow> <mo>(</mo> <mi>X</mi> <mo>-</mo> <msub> <mi>X</mi> <mrow> <mi>G</mi> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mn>2</mn> <msup> <mi>&amp;sigma;</mi> <mn>2</mn> </msup> </mrow> </mfrac> <mo>&amp;rsqb;</mo> <mo>.</mo> </mrow> 2
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CN107145938A (en) * 2017-05-27 2017-09-08 重庆科技学院 Reservoir rock median radius Forecasting Methodology based on well logging information
CN107423844B (en) * 2017-06-06 2018-06-26 西南石油大学 A kind of new method for predicting shale gas/tight gas wells recoverable reserves
CN107966546B (en) * 2017-11-21 2021-04-27 西南石油大学 Shale lithofacies plane distribution compiling method and shale exploration system
CN108229011B (en) * 2017-12-29 2021-03-30 中国地质大学（武汉） Shale lithofacies development master control factor judgment method, device and storage device
CN112630405A (en) * 2020-11-27 2021-04-09 中国石油大学(华东) Hydrocarbon source rock type identification method based on genetic algorithm driven support vector machine

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