WO2017084454A1 - 一种地层组分最优化确定方法及装置 - Google Patents

一种地层组分最优化确定方法及装置 Download PDF

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WO2017084454A1
WO2017084454A1 PCT/CN2016/101643 CN2016101643W WO2017084454A1 WO 2017084454 A1 WO2017084454 A1 WO 2017084454A1 CN 2016101643 W CN2016101643 W CN 2016101643W WO 2017084454 A1 WO2017084454 A1 WO 2017084454A1
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expression
formation
response equation
log
logging
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PCT/CN2016/101643
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English (en)
French (fr)
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冯周
李宁
武宏亮
王华峰
冯庆付
王克文
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中国石油天然气股份有限公司
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Priority to US15/749,290 priority Critical patent/US11010507B2/en
Priority to EP16865632.0A priority patent/EP3379433B1/en
Publication of WO2017084454A1 publication Critical patent/WO2017084454A1/zh

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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E21B49/02—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells by mechanically taking samples of the soil
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B41/00—Equipment or details not covered by groups E21B15/00 - E21B40/00
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V99/00—Subject matter not provided for in other groups of this subclass

Definitions

  • the invention relates to a method and a device for determining the optimization of formation components, and belongs to the technical field of oil and gas exploration and logging.
  • Quantitative determination of formation components is the basis and key to reservoir logging interpretation. Through the determination of formation minerals and fluid content, and determining the parameters such as formation porosity and saturation, it can directly guide effective reservoir identification, oil and gas layer prediction analysis and oilfield regional evaluation, which is of great significance for oil and gas field exploration and development.
  • the commonly used method for determining formation component content using well logging data is the well logging optimization method, which combines all logging information, errors and regional geological experience into a multi-dimensional information complex, using the most mathematical fields.
  • the optimization technique is used to obtain the optimal interpretation results that satisfy the formation conditions.
  • the basis for the optimal processing method for logging is the various logging response equations established by the formation model.
  • the logging response equation characterizes the quantitative relationship between the logging response and the formation parameters, and can be measured by the logging response equation. Determine the degree of compliance between the optimization process and the actual formation.
  • the logging response equations generally only adopt a predetermined number of fixed forms. Although these fixed forms are designed to provide users with a variety of model equations, the following problems still exist in practical applications: First, the existing log response equations are based on traditional classical models, so The scope of application is limited, and the application effect is not satisfactory in many complex types of reservoirs.
  • the predetermined model formula cannot be applied in all oil fields and strata because different research areas have their specific forms; finally, in logging interpretation, usually It also includes many empirical formulas based on petrophysical analysis data.
  • the existing optimization processing methods lack the processing ability of these formulas, thus limiting the application range and processing accuracy of the method.
  • the invention solves the problem that the logging response equation model existing in the existing logging optimization processing method has a fixed, limited application range and poor processing precision, and further proposes a method and device for determining formation component optimization, including The following technical solutions:
  • a method for determining formation composition optimization comprising:
  • the formation rock component model is established according to the core analysis data and geological conditions of the stratum to be tested, and the logging curve determined by the participating model is determined;
  • An objective function of the optimization problem is established according to the parsed expression of the response equation, and the objective function is solved by an iterative algorithm to determine an optimal component content of the ground layer to be tested.
  • the logging curve includes: a natural gamma log curve, a deep lateral resistivity log, a shallow lateral resistivity log, and a density log. At least one of a curve, a neutron log curve, or an element capture spectroscopy log.
  • the logging response equation is:
  • t ci represents the value of the log response equation; Indicates the composition of the formation minerals and fluids of the formation to be tested; An expression that represents the log response equation and includes formation mineral and fluid component variables, numbers, operators, and parameter symbols.
  • the parsing and recording storage of each response equation expression in the formation component optimization determining method of the present invention includes:
  • Each element in the suffix expression is recorded by a data structure and saved as a storage structure of a dynamic array
  • the storage structure of the suffix expression is traversed, and a combination relationship between each element is determined according to a derivation rule, thereby determining a partial derivative form of the suffix expression.
  • the objective function of the optimization problem includes:
  • v * represents the value obtained when the objective function obtains the minimum value
  • tci represents the response equation value of each type of logging method determined according to the logging response equation
  • tmi represents the actual logging measurement response value
  • w i represents the weighting coefficient of the logging curve in the optimization model
  • n represents the number of logging curves solved.
  • the formation rock component model further includes an additional constraint condition, wherein the additional constraint condition is:
  • C k represents a matrix of constraint coefficients; Indicates the composition of the formation minerals and fluids of the formation to be tested; b k represents the constraint boundary.
  • solving the objective function by an iterative algorithm includes: converting the objective function into an expression of an unconstrained problem by a penalty function method:
  • M represents a penalty factor
  • And means the penalty function, when Meet the constraints Penalty when Does not satisfy the constraint Penalty And increases with the increase of M
  • cn represents the number of constraints.
  • the solution of the objective function by the iterative algorithm further includes: the iteration increment by the Levenberg-Marquardt algorithm can be expressed as;
  • J represents the Jacobian matrix of R
  • I represents the identity matrix
  • ⁇ represents the damping factor
  • the solution of the objective function by the iterative algorithm further includes:
  • the current iteration increment is obtained by iterative increment and transferred to the next iteration;
  • the optimal formation component content is solved.
  • a formation component optimization determining device comprising:
  • a logging curve determining unit configured to establish a formation rock component model according to core analysis data and geological conditions of the formation to be measured, and determine a logging curve determined by the participation model;
  • a response equation determining unit configured to determine a log response equation expression corresponding to the log curve determined by the participating model
  • An analysis unit configured to parse and record and store the log response equation expression
  • the optimal content determining unit establishes an objective function of the optimization problem according to the parsed expression of the response equation, and solves the objective function by an iterative algorithm to determine an optimal component content of the ground layer to be tested.
  • the beneficial effects of the invention are: by establishing a formation rock component model and determining a corresponding log response equation, and then parsing and recording the stored log response equation expression by expression analysis method, and then establishing an objective function of the optimization problem, The iterative algorithm is solved to obtain the optimal component content of the stratum to be tested, which not only can optimize the user-defined logging response equation, but also has a wide application range and high processing precision.
  • FIG. 1 is a flow chart showing a method for determining formation composition optimization by way of example.
  • FIG. 2 is a flow chart of a method for determining formation composition in the first embodiment.
  • FIG. 3 is a schematic diagram showing the results of an experimental measurement of a typical core water saturation-resistance increase rate provided in the first embodiment, wherein the abscissa indicates the tax saturation (Sw), and the ordinate indicates the resistance increase rate (I), the dot It indicates the combination of rock and electric experiments.
  • the real curve represents the fitting structure of the optimal saturation equation, and the dashed curve represents the fitting result of the Archie formula.
  • FIG. 5 is a comparison diagram of the results of the mineral and fluid component content of the A well formation and the experimental analysis results provided in the first embodiment.
  • FIG. 6 is a structural diagram of a formation composition optimization determining device provided in Embodiment 2;
  • FIG. 7 is an apparatus for obtaining the content of components of a formation rock provided by an embodiment of the present application.
  • the method for determining the formation composition of the present embodiment includes:
  • step 11 the formation rock component model is established according to the core analysis data and geological conditions of the stratum to be tested, and the logging curve determined by the participation model is determined.
  • the logging data and experimental analysis data that can be obtained include: conventional and electrical imaging logging data of the formation to be tested, and core and fluid experimental analysis data.
  • core fluid analysis data and geological conditions of the stratum to be tested, the main mineral composition types, trace mineral types and formation fluid types of the rocks in the stratum can be determined, and the formation rock component model for logging interpretation is established.
  • step 12 the log response equation expression corresponding to the log curve determined by the participation model is determined.
  • the optimal response equation and parameter values of each logging method to be treated are selected.
  • the response equation should be able to describe the corresponding logging curve response with higher precision. Changes in the composition of minerals and fluids with the formation.
  • step 13 the response equation expressions are stored and recorded.
  • the dynamic expression analysis method is used to analyze, separate and store the log response equation expression of each logging method, and then obtain the partial derivative form of the log response equation by partial derivative analysis.
  • it is only necessary to traverse each storage structure, and according to the operator type between each element, determine the combination relationship between the elements according to the derivation rule, and form a partial derivative form of the expression for each variable, and adopt a predetermined
  • the storage structure is recorded sequentially. For each stored suffix expression and partial derivative expression structure, the variables and parameter values are substituted, and the storage structures are traversed in turn, and the values of the corresponding expressions are directly obtained according to the suffix expression rules.
  • step 14 the objective function of the optimization problem is established according to the parsed response equation expression, and the objective function is solved by the iterative algorithm to determine the optimal component content of the formation to be tested.
  • the least square method may be used to establish an objective function of the optimization problem.
  • the objective function of the optimization problem can be transformed into an unconstrained problem by the penalty function, and then optimized by the Levenberg-Marquardt algorithm.
  • the Levenberg-Marquardt algorithm first gives a set of initial assumed regional component content values for each processing depth point, and performs the first iteration and substitutes the resolution recorded in step 13. Obtain the partial derivative matrix in the stored partial derivative expression, and then obtain the current iteration increment and transfer to the next iteration until the content of the component of each layer in the formation to be tested satisfies the accuracy requirement and meets the predetermined constraint condition. That is to solve the optimal formation component content.
  • the method for determining the formation composition of the present embodiment includes:
  • Step 21 acquiring logging data and experimental analysis data of the formation to be tested.
  • the logging data and experimental analysis data that can be obtained include: conventional and electrical imaging logging data of the formation to be tested, and core and fluid experimental analysis data.
  • the core experimental analysis may include data of different horizons and different lithologies in order to obtain the geological conditions of the stratum to be tested.
  • the core experimental analysis data may include coring description, physical property analysis data, lithology data, rock power, nuclear magnetic, acoustic wave, etc.; fluid experimental analysis data may include formation test data, formation water analysis data, etc., for identifying formation fluid types and strata Water properties.
  • step 22 a formation rock component model is established according to the logging data of the formation to be measured and the experimental analysis data.
  • the 6050.0-6090.0m section of the well is located in the Changxing Formation of the Permian.
  • the lithology of the Changxing Formation in this area is dominated by reef-bank facies, and local dolomitization forms high-quality dolomite reservoirs. .
  • continuous sealing and coring is carried out.
  • the core shows that the lithology is dominated by limestone, dolomitic limestone and gray dolomite.
  • Further karst analysis shows that the main mineral types in the formation include calcite and dolomite, and the clay mineral content is less.
  • the pore fluid composition is formation water and natural gas.
  • the formation rock component model established from the above data may include clay, calcite, dolomite, formation water, and natural gas.
  • the range of mineral and fluid component content included in the formation rock component model is defined as an additional constraint for subsequent optimization.
  • step 23 the log curve determined by the participation model is determined.
  • the natural gamma logging curve reflects the change of clay mineral content.
  • the density and neutron logging curves reflect the dolomite, calcite content and formation pore size, while the deep and shallow lateral resistivity logs The curve reflects the change in fluid composition in the pores of the undisturbed formation and the rinsing zone. Therefore, for the formation minerals and fluid types in the above-mentioned formation rock component model, selected
  • the logging curves determined by the participating models include natural gamma logging curves, deep lateral resistivity logging curves, shallow lateral resistivity logging curves, density logging curves, and neutron logging curves.
  • Step 24 determining a log response equation expression corresponding to the log curve.
  • an optimal logging response equation of each logging method of the to-be-tested formation is established, and the logging response equation should describe the corresponding logging curve response with high precision.
  • the variation of the mineral and fluid composition in the formation is to be determined.
  • the general form of the log response equation is:
  • t ci represents the value of the logging response equation for each type of logging method; Indicates the composition of the formation minerals and fluids of the formation to be tested; An expression that represents the log response equation and includes formation mineral and fluid component variables, numbers, operators, and parameter symbols.
  • the whole-diameter core of the well section is also subjected to a rock-electricity analysis experiment.
  • Fig. 3 the experimental results of the typical core water saturation-resistance increase rate are obtained.
  • the fitting result of the optimal truncated form of the general solution equation of the saturation equation by the Archie formula From the fitting results, it can be seen that in the stage of water saturation greater than 0.5, the Archie formula fitting result is close to the optimal saturation equation fitting result, but in the stage of water saturation below 0.5, the Archie formula fitting result and The measured data differs greatly, and the fitting results of the optimal saturation equation can accurately reflect the variation characteristics of the core Sw-I. Therefore, the present embodiment adopts an optimal saturation equation which more accurately reflects the electrical variation characteristics of the formation rocks.
  • t crt represents the value of the deep lateral resistivity
  • the unit is OHMM (ohm meters); a represents the lithology coefficient, in this example, the value is 1, no dimension; R w represents the formation water resistivity, which can be directly queried The formation water analysis data is obtained, the unit is OHMM; m is the formation cementation index, in this example, the value is 2, dimensionless; ⁇ is the formation porosity, which is the sum of all fluid components in the formation, the unit is V/V; w represents the water saturation of the original formation, which is the percentage of the formation water content in the total pore fraction, and the unit is V/V; p 1 , p 2 , and n 1 are the equation parameters that can be determined by predetermined experiments, which are respectively taken in this embodiment. 1/0.32494, 0.69126, 2.17399.
  • the shallow lateral resistivity log response equation can be determined as:
  • t crxo represents the value of shallow lateral resistivity
  • the unit is OHMM
  • a represents the lithology coefficient, in this example, the value is 1, no dimension
  • R mf represents the resistivity of the mud filtrate, and the formation water analysis data can be directly queried.
  • the unit is OHMM
  • m represents the formation cementation index, in this example, the value is 2, dimensionless
  • ⁇ represents the formation porosity, which is the sum of all fluid components in the formation, the unit is V/V
  • S xo represents the flushing zone
  • the water saturation is the percentage of the formation water content in the total pore fraction, the unit is V/V
  • p 1 , p 2 and n 1 are the equation parameters determined by the predetermined experiment. In the present embodiment, 1/0.32494 and 0.69126 are respectively taken. , 2.17399.
  • t crt A*RW*(1/(P1*((UWAT+VCLAY*WCLP)/(UWAT+UGAS+VCLAY*WCLP) ⁇ N1))+P2)/((UWAT+UGAS+VCLAY*WCLP) ⁇ M)
  • UWAT and UGAS indicate the content of water and gas components in the original formation
  • XWAT and XGAS indicate the content of water and gas components in the rinse zone
  • VCLAY indicates the content of clay components
  • UWAT, UGAS, XWAT, XGAS and VCLAY are all measured.
  • the well responds to the variable element in the equation expression;
  • WCLP represents the clay bound water porosity, and for the fixed clay type, the parameters are determined.
  • step 25 each response equation expression is parsed and recorded according to the expression parsing method.
  • the process of parsing the expression includes three stages: 1) expression element analysis; 2) expression separation and storage; and 3) expression partial form calculation and storage.
  • stage 1 the variables, numbers, operation symbols, and parameter symbol elements included in the expression string of the log response equation established in step 24 are classified and matched, and the expression definition is legal. After the elements contained in the expression are matched, the grammatical structure of the expression itself is determined. The result of the expression and the parameters are only the values of the variables and parameters.
  • stage 2 through the matching identification of the elements (numbers, parentheses, operators, variables, parameters, etc.) of the expression in stage 1), the established log response equation expression is parsed, This includes the role of the parentheses in the expression, the precedence of the operators, the order in which the operators appear, and the exclusion of ambiguity, and is also escaped as a suffix expression for computer operations.
  • Each element in the suffix expression is Recorded in a data structure and placed in a dynamic array sequential storage.
  • the data structure can include three fields, the first field is a string type (string) for storing the expression element form, and the second field is a double-precision floating point type (double) for storing the element.
  • the value, the value of the numeric element is its own, the value of the variable, the parameter is given by the subsequent calculation;
  • the third field is an integer (int), which is used to identify the type of the operator and the operand, the constant is 0, the variable For 2, the defined operator is 1.
  • the storage structure corresponding to the data structure can be expressed as:
  • the advantage of this stage is that the converted suffix expression avoids the problem of the operation priority caused by different operators, parentheses, etc. in the original expression, which simplifies the calculation order; at the same time, only the expression needs to be used in the subsequent calculation process.
  • the storage structure can be scanned once, saving time.
  • stage 3 using the suffix expression parsed in stage 2), iterate through each storage structure, determine the relationship between each element and the variable, and then determine the combination between the elements according to the type of operator according to the derivative rule. The relationship ultimately forms a partial derivative of the expression for each variable and is sequentially recorded using the structure stored in stage 2).
  • the expression of the input is first analyzed by an expression check to check whether the expression is legal.
  • the deep lateral resistivity response equation expression contains variables (UWAT, UGAS, VCLAY), numbers, parameters (A, RW, P1, P2, N1, M, WCLP) and four operators, brackets, multiplication Square operator, etc.
  • Variables and parameters in expressions are legal, operators, and The number is complete and meets the calculation requirements.
  • the expression is parsed, all characters are read in order from left to right, and the syntax structure is parsed, and the escaping expression is stored as a suffix expression.
  • the suffix expression stored in the expression of the deep lateral resistivity log response equation is:
  • the storage structure of the deep lateral resistivity log response equation expression can be expressed as:
  • the partial derivative analysis is performed by traversing each storage structure.
  • the combination relationship between the elements is determined according to the derivation rule, and the partial derivative form of the expression for each variable can be formed, and the above storage structure is adopted.
  • step 26 an objective function of the optimization problem is established.
  • the least square method can be used to establish the objective function of the optimization problem.
  • the least square method is used to establish the optimal objective function.
  • the objective function established in this embodiment is:
  • each component in the above objective function may be limited to a certain range, and may also satisfy the additional constraint in step 22, and the general form of the additional constraint may be recorded as:
  • C k represents a matrix of constraint coefficients; Indicates the composition of the formation minerals and fluids of the formation to be tested; b k represents the constraint boundary.
  • all components in each layer may also satisfy at least the constraint that the sum of the components is 1 or the flushing zone is equal to the fluid content of the undisturbed formation, namely:
  • the optimization problem to be solved is transformed into a constrained nonlinear least squares problem consisting of the two objective functions established above and the above two constraints.
  • step 27 the objective function is solved by an iterative algorithm, and the optimal component content of the formation to be tested is obtained.
  • M represents a penalty factor
  • the iterative increment by the Levenberg-Marquardt algorithm can be expressed as:
  • J represents the Jacobian matrix of R
  • ie I represents the unit matrix
  • ⁇ represents the damping factor, which reflects the difference between the actual drop of the objective function and the first-order approximate descent at the current iteration point of the objective function, and can be adaptively adjusted during the iterative process.
  • the partial derivative form of each response equation for each layer variable has been parsed and stored in step 25.
  • a set of initially set formation component content values is first given (ie, in step 26)
  • the variable in the objective function of the established optimization problem, the initial value of the first depth point processed can be given randomly, and the depth point of the subsequent processing can take the processing result of the previous depth point as the initial value) for the first time.
  • the component value of each layer is substituted into the partial derivative expression stored in step 25 to obtain the matrix J, and then the current iteration increment is obtained by iterative increment and transferred to the next iteration.
  • the optimal formation component content is solved.
  • the content of minerals and fluid components in the formation can be determined, so that the formation lithology, favorable reservoir development sites and fluid properties can be directly determined, and the comprehensive evaluation of the reservoir can be guided.
  • Figure 5 shows the results of the formation composition of the A well and its comparison with the laboratory analysis results.
  • the first track in the figure shows the conventional natural gamma, photoelectric absorption cross-section index and caliper curve; the second track shows the depth; the third track shows the double lateral resistivity curve; the fourth track shows The three-pore curve; the fifth and sixth lanes respectively represent the calculated calcite and dolomite content compared with the core analysis results; the seventh lane represents the total amount of formation fluids obtained, ie total porosity and core analysis Porosity comparison; the eighth track represents the treated gas saturation and the closed coring result; the ninth track represents the treated formation lithology profile; and in the columns shown in Figure 5, the circle The dot symbol indicates the corresponding core analysis result.
  • the mineral content and formation fluid component content of the formation calcite and dolomite obtained by the present embodiment are in good agreement with the core analysis results, and the porosity error is generally less than 0.5% porosity, mineral content. And the saturation deviation is on average within 10%.
  • the formation component optimization determining apparatus includes:
  • the logging curve determining unit 61 is configured to establish a formation rock component model according to core analysis data and geological conditions of the ground layer to be tested, and determine a logging curve determined by the participation model;
  • a response equation determining unit 62 configured to determine a log response equation expression corresponding to the log curve determined by the participating model
  • the parsing unit 63 is configured to parse and record and store the log response equation expression
  • the optimal content determining unit 64 establishes an objective function of the optimization problem according to the parsed expression of the response equation, and solves the objective function by an iterative algorithm to determine an optimal component content of the ground layer to be tested.
  • the logging curve determining unit 61 can determine the main mineral composition type, trace mineral type, and formation fluid type of the rock of the formation to be measured according to the core, fluid analysis data and geological conditions of the formation to be measured, thereby establishing a well for interpretation of the well. Formation rock component model. Then, the response equation determining unit 62 selects the optimal response equation and the parameter value of each logging method to be processed according to the reservoir section core data fitting or the experience of the block area, and the response equation should be higher. The accuracy description corresponds to the variation of the log response and the mineral and fluid composition of the formation.
  • the analytical unit 63 uses a dynamic expression analysis method to analyze, separate and store the log response equation expression of each logging method, and then obtain a partial derivative form of the expression of the log response equation by partial derivative analysis;
  • a dynamic expression analysis method to analyze, separate and store the log response equation expression of each logging method, and then obtain a partial derivative form of the expression of the log response equation by partial derivative analysis;
  • the storage structure is sequentially recorded; the stored suffix expressions and partial derivatives
  • the expression structure in which the variables and parameter values are substituted, sequentially traverses each storage structure, and directly obtains the value of the corresponding expression according to the suffix expression rule.
  • the optimal function determining unit 64 converts the objective function of the optimization problem into an unconstrained problem by a penalty function, and then optimizes the solution by the Levenberg-Marquardt algorithm.
  • the Levenberg-Marquardt algorithm first gives a set of initial assumed regional component content values for each processing depth point, performs the first iteration, and substitutes into the analytical stored partial derivative expression recorded in step 13 and obtains The partial derivative matrix is then obtained by the current iteration increment and transferred to the next iteration until the component values of the layers in the formation to be tested satisfy the accuracy requirements and satisfy the predetermined constraint condition, that is, the optimal formation components are solved. content.
  • the embodiment of the invention further provides a computer readable storage medium for computer readable instructions, when executed, causing the processor to perform at least the following operations: establishing a formation according to core analysis data and geological conditions of the formation to be tested a rock component model, and determining a log curve determined by the participation model; determining a log response equation expression corresponding to the log curve determined by the participating model; parsing and recording and storing the log response equation expression;
  • the response equation expression establishes an objective function of the optimization problem, and solves the objective function by an iterative algorithm to determine an optimal component content of the formation to be tested.
  • the computer readable instructions cause a processor to parse an expression of the logging response equation and escape to a suffix expression for a computer operation; each of the suffix expressions The element is recorded by the data structure and saved as a storage structure of the dynamic array; traversing the storage structure of the suffix expression, and determining a combination relationship between each element according to the derivation rule, thereby determining a partial guide of the suffix expression form.
  • the computer readable instructions described above cause the processor to convert the objective function into an expression of an unconstrained problem by a penalty function method:
  • M represents a penalty factor
  • And means the penalty function, when Meet the constraints Penalty when Does not satisfy the constraint Penalty And increases with the increase of M
  • cn represents the number of constraints.
  • the computer readable instructions described above cause the processor to perform an iterative increment by the Levenberg-Marquardt algorithm as:
  • J represents the Jacobian matrix of R
  • I represents the identity matrix
  • ⁇ represents the damping factor
  • the computer readable instructions cause the processor to perform a first iteration of a set of initially set formation component content values for each processing depth point, and substituting the regional component content values into the analytical storage bias.
  • the matrix J is obtained; the current iteration increment is obtained by iterative increment and transferred to the next iteration; when the iterative stratum component value satisfies the accuracy requirement and the additional constraint is satisfied, the optimal stratigraphic group is solved. Sub-content.
  • An embodiment of the present invention further provides an apparatus for obtaining a rock component content of a formation.
  • the apparatus includes: a processor 701; and a memory 702 including computer readable instructions, when the computer readable instructions are executed
  • the processor performs the following operations: establishing a formation rock component model according to core analysis data and geological conditions of the ground layer to be tested, and determining a logging curve determined by the participating model; and determining a logging response corresponding to the logging curve determined by the participating model Equation expression; parsing and recording the stored log response equation expression; establishing an objective function of the optimization problem according to the parsed expression of the response equation, and solving the objective function by an iterative algorithm to determine the to-be-determined Measure the optimum component content of the formation.
  • the computer readable instructions cause a processor to parse an expression of the logging response equation and escape to a suffix expression for a computer operation; each of the suffix expressions The element is recorded and saved as a storage structure of the dynamic array through the data structure; the traversal The storage structure of the suffix expression is described, and the combination relationship between each element is determined according to the derivation rule, thereby determining the partial derivative form of the suffix expression.
  • the computer readable instructions described above cause the processor to convert the objective function into an expression of an unconstrained problem by a penalty function method:
  • M represents a penalty factor
  • And means the penalty function, when Meet the constraints Penalty when Does not satisfy the constraint Penalty And increases with the increase of M
  • cn represents the number of constraints.
  • the computer readable instructions described above cause the processor to perform an iterative increment by the Levenberg-Marquardt algorithm as:
  • J represents the Jacobian matrix of R
  • I represents the identity matrix
  • ⁇ represents the damping factor
  • the computer readable instructions cause the processor to perform a first iteration of a set of initially set formation component content values for each processing depth point, and substituting the regional component content values into the analytical storage bias.
  • the matrix J is obtained; the current iteration increment is obtained by iterative increment and transferred to the next iteration; when the iterative stratum component value satisfies the accuracy requirement and the additional constraint is satisfied, the optimal stratigraphic group is solved. Sub-content.

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Abstract

一种地层组分最优化确定方法及装置，属于油气勘探测井技术领域。所述方法包括：根据待测地层的岩心分析资料及地质条件建立地层岩石组分模型，确定参与模型确定的测井曲线（11）；确定参与模型确定的测井曲线对应的测井响应方程表达式（12）；解析并记录存储测井响应方程表达式（13），根据经过解析的响应方程表达式建立最优化问题的目标函数，并通过迭代算法求解目标函数，以确定待测地层的最优组分含量（14）。通过建立地层岩石组分模型并确定对应的测井响应方程，再通过表达式解析法解析并记录存储测井响应方程表达式，然后建立最优化问题的目标函数，通过迭代算法求解获得待测地层的最优组分含量，不仅能够对用户自定义的测井响应方程进行最优化处理，而且具有较高的处理精度。

Description

一种地层组分最优化确定方法及装置
本申请要求2015年11月18日递交的申请号为2015107953578、发明名称为“一种地层组分最优化确定方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及一种地层组分最优化确定方法及装置，属于油气勘探测井技术领域。
背景技术
地层组分定量确定是储层测井解释的基础和关键。通过对地层矿物、流体含量的确定，并据此确定地层孔隙度、饱和度等参数，能够直接指导有效储层识别、油气层预测分析以及油田区域评价，对油气田勘探开发具有十分重要的意义。
目前，利用测井资料确定地层组分含量通常采用的方法是测井最优化处理方法，它是将所有的测井信息、误差及地区地质经验综合成一个多维信息复合体，利用数学领域的最优化技术求取满足地层条件的最优解释结果。
用于测井的最优化处理方法的基础是依据地层模型建立的各类测井响应方程，该测井响应方程表征了测井响应与地层特性参数之间的定量关系，通过测井响应方程能够确定最优化处理结果与实际地层的符合程度。但是在现有的最优化处理方法中，测井响应方程普遍只是采用预先定义的几类固定的形式。尽管在这些固定的形式在设计时都尽量为用户提供了多种模型方程，但在实际应用中仍存在以下问题：首先，已有的测井响应方程都是基于传统经典模型建立的，因而其适用范围有限，在很多复杂类型储层中应用效果不理想；其次，预定的模型公式无法在所有油田、地层都适用，因为不同研究区存在其特定的形式；最后，在测井解释中，通常还包括了很多根据岩石物理分析资料建立的经验公式，现有的最优化处理方法中欠缺对这些公式的处理能力，因而限制了该方法的应用范围和处理精度。
发明内容
本发明为解决现有的测井最优化处理方法存在的测井响应方程模型固定、适用范围有限、处理精度较差的问题，进而提出了一种地层组分最优化确定方法及装置，具体包括如下的技术方案：
一种地层组分最优化确定方法，包括：
根据待测地层的岩心分析资料及地质条件建立地层岩石组分模型，并确定参与模型确定的测井曲线；
确定所述参与模型确定的测井曲线对应的测井响应方程表达式；
解析并记录存储所述测井响应方程表达式；
根据经过解析的所述响应方程表达式建立最优化问题的目标函数，并通过迭代算法求解所述目标函数，以确定所述待测地层的最优组分含量。
在本发明所述的地层组分最优化确定方法中，所述测井曲线包括：自然伽马测井曲线、深侧向电阻率测井曲线、浅侧向电阻率测井曲线、密度测井曲线、中子测井曲线或元素俘获能谱测井曲线中的至少一种。
在本发明所述的地层组分最优化确定方法中，所述测井响应方程为：
Figure PCTCN2016101643-appb-000001
其中，tci表示测井响应方程的值；
Figure PCTCN2016101643-appb-000002
表示待测地层的地层矿物及流体的组分含量；
Figure PCTCN2016101643-appb-000003
表示测井响应方程的表达式形式，并包括地层矿物和流体组分变量、数字、运算符以及参数符号。
在本发明所述的地层组分最优化确定方法中所述解析并记录存储各响应方程表达式包括：
对所述测井响应方程的表达式进行解析，并转义为用于计算机运算的后缀表达式；
将所述后缀表达式中的每个元素通过数据结构体记录并保存为动态数组的存储结构；
遍历所述后缀表达式的存储结构，并根据求导规则确定每个元素之间的组合关系，从而确定所述后缀表达式的偏导形式。
在本发明所述的地层组分最优化确定方法中，所述优化问题的目标函数包括：
Figure PCTCN2016101643-appb-000004
Figure PCTCN2016101643-appb-000005
其中，
Figure PCTCN2016101643-appb-000006
表示目标函数；v*表示使目标函数获得最小值时的取值；tci表示根据所述测井响应方程确定的各类测井方法的响应方程值；tmi表示实际测井测量响应值；wi表示所述测井曲线在最优化模型中的权重系数；n表示求解的测井曲线的数量。
在本发明所述的地层组分最优化确定方法中，所述地层岩石组分模型还包括附加约束条件，所述附加约束条件为：
Figure PCTCN2016101643-appb-000007
其中，
Figure PCTCN2016101643-appb-000008
表示约束条件；Ck表示约束条件系数矩阵；
Figure PCTCN2016101643-appb-000009
表示待测地层的地层矿物及流体的组分含量；bk表示约束条件边界。
在本发明所述的地层组分最优化确定方法中，通过迭代算法求解所述目标函数包括：通过惩罚函数法将所述目标函数转化为无约束问题的表达式：
Figure PCTCN2016101643-appb-000010
其中，M表示惩罚因子；
Figure PCTCN2016101643-appb-000011
并表示惩罚函数，当
Figure PCTCN2016101643-appb-000012
满足约束条件
Figure PCTCN2016101643-appb-000013
时，惩罚项
Figure PCTCN2016101643-appb-000014
当
Figure PCTCN2016101643-appb-000015
不满足约束条件
Figure PCTCN2016101643-appb-000016
时，惩罚项
Figure PCTCN2016101643-appb-000017
且随M的增大而增大；cn表示约束条件的数目。
在本发明所述的地层组分最优化确定方法中，通过迭代算法求解所述目标函数还包括：通过Levenberg-Marquardt算法进行迭代增量可以表示为；
(JTJ+μ·I)·h＝-JTR
其中，J表示R的Jacobi矩阵；I表示单位矩阵；μ表示阻尼因子，
Figure PCTCN2016101643-appb-000018
在本发明所述的地层组分最优化确定方法中，通过迭代算法求解所述目标函数还包括：
对每个处理深度点的一组初始设定的地层组分含量值进行第一次迭代，将各地层组分含量值代入解析存储的偏导表达式中，获得矩阵J；
通过迭代增量求取当前迭代增量并转入到下一次迭代；
当迭代地层组分值满足精度要求且满足附加约束条件时，即求解得到最优的地层组分含量。
一种地层组分最优化确定装置，包括：
测井曲线确定单元，用于根据待测地层的岩心分析资料及地质条件建立地层岩石组分模型，并确定参与模型确定的测井曲线；
响应方程确定单元，用于确定所述参与模型确定的测井曲线对应的测井响应方程表达式；
解析单元，用于解析并记录存储所述测井响应方程表达式；
最优含量确定单元，根据经过解析的所述响应方程表达式建立最优化问题的目标函数，并通过迭代算法求解所述目标函数，以确定所述待测地层的最优组分含量。
本发明的有益效果是：通过建立地层岩石组分模型并确定对应的测井响应方程，再通过表达式解析法解析并记录存储测井响应方程表达式，然后建立最优化问题的目标函数，通过迭代算法求解，从而获得待测地层的最优组分含量，不仅能够对用户自定义的测井响应方程进行最优化处理，适用范围较大，而且具有较高的处理精度。
附图说明
图1是以示例的方式示出了地层组分最优化确定方法的流程图。
图2是实施例一所述的地层组分最优化确定方法的流程图。
图3是实施例一提供的典型岩心含水饱和度-电阻增大率实验测量解结果示意图，其中的横坐标表示含税饱和度(Sw)，纵坐标表示电阻增大率(I)，圆点表示岩电实验结合，实曲线表示最优饱和度方程拟合结构，虚曲线表示Archie公式拟合结果。
图4是实施例一提供的表达式解析的流程图。
图5是实施例一提供的A井地层矿物及流体组分含量结果与实验分析结果对比图。
图6是实施例二提供的地层组分最优化确定装置的结构图；
图7是本申请实施例提供的一种获取地层岩石组分含量的设备。
具体实施方式
结合图1所示，本实施例提供的地层组分最优化确定方法包括：
步骤11，根据待测地层的岩心分析资料及地质条件建立地层岩石组分模型，并确定参与模型确定的测井曲线。
对于预定的待测地层，可以获取的测井数据及实验分析数据包括：待测地层的常规、电成像等测井数据以及岩心、流体实验分析数据。根据待测地层的岩心、流体分析数据及地质条件，可以确定待测地层的岩石的主要矿物组成类型、微量矿物类型以及地层流体类型，从而建立用于测井解释的地层岩石组分模型。
观察待处理层段测井曲线变化规律，优选出对地层矿物或流体变化具有明显响应特征的曲线参与模型最优化处理，并保证参与确定的曲线数量应尽量多于模型中矿物、流体组分的数量。
步骤12，确定参与模型确定的测井曲线对应的测井响应方程表达式。
根据储层段岩心实验数据拟合或研究区块地区经验，选定待处理层段各测井方法最优的响应方程与参数值，该响应方程应能以较高精度描述对应测井曲线响应与地层待求矿物、流体组分的变化规律。
步骤13，解析并记录存储各响应方程表达式。
采用动态表达式解析方法，对各测井方法的测井响应方程表达式进行解析、分离和存储，然后再通过偏导数解析获得测井响应方程的表达式的偏导形式。在解析过程中只需遍历每个存储结构，并根据各元素间的运算符类型，按求导规则确定元素间的组合关系，即可形成表达式对各变量的偏导形式，并采用预定的存储结构进行顺序记录。对存储的各后缀表达式及偏导数表达式结构，将其中的变量和参数值代入，依次遍历各存储结构，依据后缀表达式规则直接获得相应表达式的值。
步骤14，根据经过解析的响应方程表达式建立最优化问题的目标函数，并通过迭代算法求解目标函数，以确定待测地层的最优组分含量。
可选的，根据待测地层的各测井方法最优的测井响应方程的表达式，可采用最小二乘法建立最优化问题的目标函数。
对于通过最小二乘法建立的最优化问题的目标函数，可通过罚函数将该最优化问题的目标函数转换为无约束问题，再通过Levenberg-Marquardt算法进行最优化求解。
其中，Levenberg-Marquardt算法对每个处理的深度点，首先给定一组初始假定的各地层组分含量值，进行第一次迭代，代入步骤13记录的解析 存储的偏导表达式中并获得偏导矩阵，然后再获取当前迭代增量并转入到下一次迭代，直至待测地层的各地层组分含量值满足精度要求且满足预定约束条件式时，即求解得到最优的地层组分含量。
下面通过具体的实施例对所述的地层组分最优化确定方法进行详细说明：
实施例一
结合图2所示，本实施例提供的地层组分最优化确定方法包括：
步骤21，获取待测地层的测井数据及实验分析数据。
对于预定的待测地层，可以获取的测井数据及实验分析数据包括：待测地层的常规、电成像等测井数据以及岩心、流体实验分析数据。
其中的岩心实验分析可包括不同层位、不同岩性的资料，以便于获取待测地层的地质条件。岩心实验分析数据可包括取心描述、物性分析资料、岩化资料以及岩电、核磁、声波等；流体实验分析数据可包括地层测试资料、地层水分析资料等，用于识别地层流体类型及地层水性质。
步骤22，根据待测地层的测井数据及实验分析数据建立地层岩石组分模型。
以某油气田A井资料处理为例，该井6050.0-6090.0m井段位于二叠系长兴组，该区长兴组岩性以礁滩相灰岩为主，局部白云岩化形成优质白云岩储层。然后针对6064.0-6071.5m井段进行连续密闭取心，岩心显示该段岩性以灰岩、白云质灰岩、灰质白云岩为主。通过进一步岩化分析表明，该井段地层主要矿物类型包括方解石、白云石，粘土矿物含量较少，孔隙流体组成为地层水和天然气。根据以上数据建立的地层岩石组分模型可以包括粘土、方解石、白云石、地层水和天然气。
可选的，还可结合解释人员的经验认识，对该地层岩石组分模型中包含的矿物、流体组分含量范围进行限定，以作为后续优化处理的附加约束条件。
步骤23，确定参与模型确定的测井曲线。
通过常规测井可知，自然伽马测井曲线对粘土矿物含量变化反映明显，密度、中子测井曲线反映了白云石、方解石含量变化及地层孔隙大小，而深、浅侧向电阻率测井曲线体现了原状地层和冲洗带部分孔隙中流体成分的变化。因此，针对上述地层岩石组分模型中的地层矿物、流体类型，选定的 参与模型确定的测井曲线包括自然伽马测井曲线、深侧向电阻率测井曲线、浅侧向电阻率测井曲线、密度测井曲线以及中子测井曲线。
步骤24，确定测井曲线对应的测井响应方程表达式。
本实施例根据待测地层的岩心实验分析数据拟合，建立该待测地层各测井方法最优的测井响应方程，该测井响应方程应能以较高精度描述对应测井曲线响应与地层待求矿物、流体组分的变化规律，该测井响应方程的一般形式为：
Figure PCTCN2016101643-appb-000019
其中，tci表示各类测井方法的测井响应方程的值；
Figure PCTCN2016101643-appb-000020
表示待测地层的地层矿物及流体的组分含量；
Figure PCTCN2016101643-appb-000021
表示测井响应方程的表达式形式，并包括地层矿物和流体组分变量、数字、运算符以及参数符号。
下面以电阻率测井响应方程的确定过程为例，该井段全直径岩心还进行了岩电分析实验，根据图3所示的是典型岩心含水饱和度-电阻增大率实验测量结果，获得通过Archie公式与饱和度通解方程最优截短形式的拟合结果。从该拟合结果可知，在含水饱和度大于0.5的阶段，Archie公式拟合结果与最优饱和度方程拟合结果比较接近，但在含水饱和度低于0.5的阶段，Archie公式拟合结果与实测数据相差较大，最优饱和度方程拟合结果能准确反映岩心Sw-I的变化特征。因此，本实施例采用更能准确反映地层岩石电性变化特征的最优饱和度方程进行处理。
根据岩电实验拟合得到的最优饱和度方程为：
Figure PCTCN2016101643-appb-000022
则深侧向电阻率测井响应方程为：
Figure PCTCN2016101643-appb-000023
其中，tcrt表示深侧向电阻率的值，单位为OHMM(欧姆米)；a表示岩性系数，本实施例中取值为1，无量纲；Rw表示地层水电阻率，可直接查询地层水分析资料获得，单位为OHMM；m表示地层胶结指数，本实施例中取值为2，无量纲；φ表示地层孔隙度，为地层中所有流体组分总和，单位为V/V；Sw表示原状地层含水饱和度，为地层水含量占总孔隙部分百分比，单位为V/V；p1、p2、n1均为可通过预定实验确定的方程参数，在本实施例中分别取1/0.32494、0.69126、2.17399。
同理，可确定浅侧向电阻率测井响应方程为：
Figure PCTCN2016101643-appb-000024
其中，tcrxo表示浅侧向电阻率的值，单位为OHMM；a表示岩性系数，本实施例中取值为1，无量纲；Rmf表示泥浆滤液电阻率，可直接查询地层水分析资料获得，单位为OHMM；m表示地层胶结指数，本实施例中取值为2，无量纲；φ表示地层孔隙度，为地层中所有流体组分总和，单位为V/V；Sxo表示冲洗带含水饱和度，为地层水含量占总孔隙部分百分比，单位为V/V，p1、p2、n1均为通过预定实验确定的方程参数，在本实施例中分别取1/0.32494、0.69126、2.17399。
将上述的深侧向电阻率测井响应方程和浅侧向电阻率测井响应方程输入到最优化确定方法中的深、浅侧向电阻率测井响应方程，获得的表达式分别为：
tcrt＝A*RW*(1/(P1*((UWAT+VCLAY*WCLP)/(UWAT+UGAS+VCLAY*WCLP)^N1))+P2)/((UWAT+UGAS+VCLAY*WCLP)^M)
tcro＝A*RMF*(1/(P1*((XWAT+VCLAY*WCLP)/(XWAT+XGAS+VCLAY*WCLP)^N1))+P2)/((XWAT+XGAS+VCLAY*WCLP)^M)
其中，UWAT、UGAS表示原状地层中水、气组分含量，XWAT、XGAS表示冲洗带中水、气组分含量，VCLAY表示粘土组分含量，并且UWAT、UGAS、XWAT、XGAS和VCLAY都属于测井响应方程表达式中的变量元素；WCLP表示粘土束缚水孔隙度，对固定粘土类型，为确定参数。
步骤25，根据表达式解析方法解析并记录存储各响应方程表达式。
结合图4所示，该表达式解析的过程包括三个阶段：1)表达式元素分析；2)表达式分离和存储；3)表达式偏导形式计算与存储。
在阶段1)中，对步骤24建立的测井响应方程的表达式字符串中包含的变量、数字、运算符号、参数符号元素进行分类匹配，校验表达式定义是否合法。表达式中包含的元素匹配完成后，此时表达式自身的语法结构则已经确定，影响表达式结果的只是其中变量和参数的取值。
在阶段2)中，通过阶段1)中对表达式的元素(数字、括号、运算符、变量、参数等)的匹配识别，将建立的测井响应方程表达式进行语法分析， 具体包括表达式中括号的作用、操作符的优先级、各个操作符出现的次序以及排除多义性等，同时转义为用于计算机运算的后缀表达式，该后缀表达式中每个元素均采用数据结构体记录并放入动态数组顺序存储。该数据结构体可包括三个字段，第一个字段为字符串型(string)，用于存放表达式元素形式；第二个字段为双精度浮点型(double)，用于存放该元素的值，数字元素的值即为其本身，变量、参数的值通过后续计算中给定；第三个字段为整型(int)，用于标识运算符和操作数的类型，常量为0，变量为2，定义的运算符为1。该数据结构体对应的存储结构可表示为：
Figure PCTCN2016101643-appb-000025
该阶段的优点是：转换后的后缀表达式避免了原表达式中不同运算符、括号位置等引起的运算优先级的问题，简化了计算顺序；同时，在后续计算过程中只需要将表达式存储结构扫描一遍即可，节省了时间开支。
在阶段3)中，利用阶段2)解析出的后缀表达式，遍历每个存储结构，判断每个元素与变量之间的关系，再根据运算符类型，按照求导规则确定元素之间的组合关系，最终形成表达式对各变量的偏导形式，并采用阶段2)中存储的结构进行顺序记录。
下面以本实施例提供的深侧向电阻率测井响应方程表达式为例，对输入的表达式首先通过表达式校验分析其中的元素构成，检查表达式是否合法。通过分析，该深侧向电阻率响应方程表达式中包含变量(UWAT、UGAS、VCLAY)、数字、参数(A、RW、P1、P2、N1、M、WCLP)以及四则运算符、括号、乘方运算符等。表达式中的变量、参数使用合法，运算符、括 号等完备，符合计算要求。然后进行表达式解析，从左到右依次读入所有字符，并解析其语法结构，转义为后缀表达式进行存储。该深侧向电阻率测井响应方程表达式存储的后缀表达式为：
A RW*1 P1 UWAT VCLAY WCLP*+UWAT UGAS+VCLAY WCLP*+/N1^*/P2+*UWAT UGAS+VCLAY WCLP*+M^/
该深侧向电阻率测井响应方程表达式的存储结构可表示为：
Figure PCTCN2016101643-appb-000026
最后进行偏导数解析，只需遍历每个存储结构，根据各元素间运算符类型，按求导规则确定元素间组合关系，即可形成表达式对各变量的偏导形式，并采用上述存储结构进行顺序记录。对存储的各后缀表达式及偏导数表达式结构，只需要将其中变量、参数值代入，依次遍历各存储结构，依据后缀表达式规则可直接获得相应表达式的值。
步骤26，建立最优化问题的目标函数。
根据待测地层的各测井方法最优的测井响应方程的表达式，可采用最小二乘法建立最优化问题的目标函数。根据各测井响应方程的表达式，采用最小二乘法建立最优化目标函数。本实施例建立的目标函数为：
Figure PCTCN2016101643-appb-000027
Figure PCTCN2016101643-appb-000028
其中，
Figure PCTCN2016101643-appb-000029
表示目标函数；v*表示使目标函数获得最小值时的取值；tci表示根据测井响应方程确定的各类测井方法的响应方程值；tmi表示实际测井测量响应值；wi表示测井曲线在最优化模型中的权重系数，可根据测井曲线质量确定；n表示求解的测井曲线的数量。
可选的，上述目标函数中各组分含量可限定在一定的范围内，同时还可满足步骤22中的附加约束条件，该附加约束条件的一般形式可记为：
Figure PCTCN2016101643-appb-000030
其中，
Figure PCTCN2016101643-appb-000031
表示约束条件；Ck表示约束条件系数矩阵；
Figure PCTCN2016101643-appb-000032
表示待测地层的地层矿物及流体的组分含量；bk表示约束条件边界。
可选的，各地层中所有组分还可至少满足组分之和为1或者冲洗带与原状地层流体含量相等这两项约束条件，即：
VCALC+VDOLO+VCLAY+UWAT+UGAS＝1
UWAT+UGAS+VCLAY*WCLP＝XWAT+XGAS+VCLAY*WCLP
所需解决的最优化问题即转化为由上述建立的两个目标函数和上述两个约束条件组成的带约束条件的非线性最小二乘问题。
步骤27，通过迭代算法求解目标函数，并获得待测地层最优的组分含量。
首先，可通过惩罚函数法将上述带约束条件的非线性最小二乘问题转化为无约束问题的表达式：
Figure PCTCN2016101643-appb-000033
其中，M表示惩罚因子；
Figure PCTCN2016101643-appb-000034
表示惩罚函数，当
Figure PCTCN2016101643-appb-000035
满足约束条件
Figure PCTCN2016101643-appb-000036
时，惩罚项
Figure PCTCN2016101643-appb-000037
当
Figure PCTCN2016101643-appb-000038
不满足约束条件
Figure PCTCN2016101643-appb-000039
时，惩罚项
Figure PCTCN2016101643-appb-000040
且随M的增大而增大；cn表示约束条件的数目。
然后，对下述的公式通过Levenberg-Marquardt算法进行迭代增量计算：
Figure PCTCN2016101643-appb-000041
其中，
Figure PCTCN2016101643-appb-000042
当
Figure PCTCN2016101643-appb-000043
时，ω＝0；当
Figure PCTCN2016101643-appb-000044
时，ω＝1；
Figure PCTCN2016101643-appb-000045
而通过Levenberg-Marquardt算法进行迭代增量可以表示为：
(JTJ+μ·I)·h＝-JTR
其中，J表示R的Jacobi矩阵，即
Figure PCTCN2016101643-appb-000046
I表示单位矩阵；μ表示阻尼因子，反映了目标函数当前的迭代点时目标函数实际下降量与一阶近似下降量的差异，可在迭代过程中自适应调整。
由于tmi、bk均为与地层矿物、流体组分含量无关的常量(针对每个处理深度点)，则有：
Figure PCTCN2016101643-appb-000047
在上式中，各响应方程对各地层变量的偏导数形式已在步骤25中解析存储，对每个处理深度点，首先给定一组初始设定的地层组分含量值(即步骤26中建立的最优化问题的目标函数中的变量，处理的第一个深度点的初始值可随机给定，后续处理的深度点可将上一个深度点的处理结果作为初始值)，进行第一次迭代，将各地层组分含量值代入步骤25解析存储的偏导表达式中，即可获得到矩阵J，然后通过迭代增量求取当前迭代增量并转入到下一次迭代。当迭代地层组分值满足精度要求且满足上述的附带约束条件时，即求解得到最优的地层组分含量。根据该最优的地层组分含量可以确定地层矿物、流体组分含量，从而可直接确定地层岩性、有利储层发育部位及流体性质，进而指导储层综合评价。
图5所示的是A井地层组分含量成果图及其与实验室分析结果对比。图中第一道表示的是常规自然伽马、光电吸收截面指数和井径曲线；第二道表示的是深度；第三道表时的是双侧向电阻率曲线；第四道表示的是三孔隙曲线；第五道和第六道分别表示的是处理得到的方解石、白云石含量的与岩心分析结果对比；第七道表示的是处理得到地层流体总量，即总孔隙度与岩心分析孔隙度对比；第八道表示的是处理得到的含气饱和度与密闭取心结果对比；第九道表示的是处理得到的地层岩性剖面；而在图5所示的各列中，圆点符号表示相应的岩心分析结果。从图5中可见，本实施例处理得到的地层方解石、白云石等矿物含量及地层流体组分含量均与岩心分析结果具有较好的一致性，孔隙度误差一般小于0.5％孔隙度，矿物含量及饱和度偏差平均在10％以内。
实施例二
结合图6所示，本实施例提供的地层组分最优化确定装置包括：
测井曲线确定单元61，用于根据待测地层的岩心分析资料及地质条件建立地层岩石组分模型，并确定参与模型确定的测井曲线；
响应方程确定单元62，用于确定所述参与模型确定的测井曲线对应的测井响应方程表达式；
解析单元63，用于解析并记录存储所述测井响应方程表达式；
最优含量确定单元64，根据经过解析的所述响应方程表达式建立最优化问题的目标函数，并通过迭代算法求解所述目标函数，以确定所述待测地层的最优组分含量。
首先，测井曲线确定单元61可根据待测地层的岩心、流体分析数据及地质条件，确定待测地层的岩石的主要矿物组成类型、微量矿物类型以及地层流体类型，从而建立用于测井解释的地层岩石组分模型。然后由响应方程确定单元62根据储层段岩心实验数据拟合或研究区块地区经验，选定待处理层段各测井方法最优的响应方程与参数值，该响应方程应能以较高精度描述对应测井曲线响应与地层待求矿物、流体组分的变化规律。再通过解析单元63采用动态表达式解析方法，对各测井方法的测井响应方程表达式进行解析、分离和存储，然后再通过偏导数解析获得测井响应方程的表达式的偏导形式；在解析过程中只需遍历每个存储结构，并根据各元素间的运算符类型，按求导规则确定元素间的组合关系，即可形成表达式对各变量的偏导形式，并采用预定的存储结构进行顺序记录；对存储的各后缀表达式及偏导数 表达式结构，将其中的变量和参数值代入，依次遍历各存储结构，依据后缀表达式规则直接获得相应表达式的值。最后，对于通过最小二乘法建立的最优化问题的目标函数，可由最优含量确定单元64通过罚函数将该最优化问题的目标函数转换为无约束问题，再通过Levenberg-Marquardt算法进行最优化求解。其中，Levenberg-Marquardt算法对每个处理的深度点，首先给定一组初始假定的各地层组分含量值，进行第一次迭代，代入步骤13记录的解析存储的偏导表达式中并获得偏导矩阵，然后再获取当前迭代增量并转入到下一次迭代，直至待测地层的各地层组分含量值满足精度要求且满足预定约束条件式时，即求解得到最优的地层组分含量。
本具体实施方式提供的地层组分最优化确定方法具有以下优点：
1)通过将表达式的动态解析技术引入到测井最优化处理方法中，从而实现了对用户自定义形式响应方程的添加和最优化处理，进一步提升了最优化处理的适用范围和精度；
2)在测井最优化处理方法中采用Levenberg-Marquardt算法进行最优化求解，具有较高的效率和求解精度；
3)建立了一套完整的基于动态表达式解析技术的地层组分最优化确定方法和处理流程，在油田现场应用中取得了良好的应用效果，有效提升了地层矿物、流体含量的精度。
本发明实施例还提供了一种计算机可读指令的计算机可读存储介质，该计算机可读指令在被执行时使处理器至少执行以下操作：根据待测地层的岩心分析资料及地质条件建立地层岩石组分模型，并确定参与模型确定的测井曲线；确定所述参与模型确定的测井曲线对应的测井响应方程表达式；解析并记录存储所述测井响应方程表达式；根据经过解析的所述响应方程表达式建立最优化问题的目标函数，并通过迭代算法求解所述目标函数，以确定所述待测地层的最优组分含量。
在一个实施例中，上述计算机可读指令使处理器对所述测井响应方程的表达式进行解析，并转义为用于计算机运算的后缀表达式；将所述后缀表达式中的每个元素通过数据结构体记录并保存为动态数组的存储结构；遍历所述后缀表达式的存储结构，并根据求导规则确定每个元素之间的组合关系，从而确定所述后缀表达式的偏导形式。
在一个实施例中，上述计算机可读指令使处理器通过惩罚函数法将所述目标函数转化为无约束问题的表达式：
Figure PCTCN2016101643-appb-000048
其中，M表示惩罚因子；
Figure PCTCN2016101643-appb-000049
并表示惩罚函数，当
Figure PCTCN2016101643-appb-000050
满足约束条件
Figure PCTCN2016101643-appb-000051
时，惩罚项
Figure PCTCN2016101643-appb-000052
当
Figure PCTCN2016101643-appb-000053
不满足约束条件
Figure PCTCN2016101643-appb-000054
时，惩罚项
Figure PCTCN2016101643-appb-000055
且随M的增大而增大；cn表示约束条件的数目。
在一个实施例中，上述计算机可读指令使处理器通过Levenberg-Marquardt算法进行迭代增量可以表示为；
(JTJ+μ·I)·h＝-JTR
其中，J表示R的Jacobi矩阵；I表示单位矩阵；μ表示阻尼因子，
Figure PCTCN2016101643-appb-000056
在一个实施例中，上述计算机可读指令使处理器对每个处理深度点的一组初始设定的地层组分含量值进行第一次迭代，将各地层组分含量值代入解析存储的偏导表达式中，获得矩阵J；通过迭代增量获得当前迭代增量并转入到下一次迭代；当迭代地层组分值满足精度要求且满足附加约束条件时，即求解得到最优的地层组分含量。
本发明实施例还提供了一种获取地层岩石组分含量的设备，如图7所示，该设备包括：处理器701；和包括计算机可读指令的存储器702，计算机可读指令在被执行时使处理器执行以下操作：根据待测地层的岩心分析资料及地质条件建立地层岩石组分模型，并确定参与模型确定的测井曲线；确定所述参与模型确定的测井曲线对应的测井响应方程表达式；解析并记录存储所述测井响应方程表达式；根据经过解析的所述响应方程表达式建立最优化问题的目标函数，并通过迭代算法求解所述目标函数，以确定所述待测地层的最优组分含量。
在一个实施例中，上述计算机可读指令使处理器对所述测井响应方程的表达式进行解析，并转义为用于计算机运算的后缀表达式；将所述后缀表达式中的每个元素通过数据结构体记录并保存为动态数组的存储结构；遍历所 述后缀表达式的存储结构，并根据求导规则确定每个元素之间的组合关系，从而确定所述后缀表达式的偏导形式。
在一个实施例中，上述计算机可读指令使处理器通过惩罚函数法将所述目标函数转化为无约束问题的表达式：
Figure PCTCN2016101643-appb-000057
其中，M表示惩罚因子；
Figure PCTCN2016101643-appb-000058
并表示惩罚函数，当
Figure PCTCN2016101643-appb-000059
满足约束条件
Figure PCTCN2016101643-appb-000060
时，惩罚项
Figure PCTCN2016101643-appb-000061
当
Figure PCTCN2016101643-appb-000062
不满足约束条件
Figure PCTCN2016101643-appb-000063
时，惩罚项
Figure PCTCN2016101643-appb-000064
且随M的增大而增大；cn表示约束条件的数目。
在一个实施例中，上述计算机可读指令使处理器通过Levenberg-Marquardt算法进行迭代增量可以表示为；
(JTJ+μ·I)·h＝-JTR
其中，J表示R的Jacobi矩阵；I表示单位矩阵；μ表示阻尼因子，
Figure PCTCN2016101643-appb-000065
在一个实施例中，上述计算机可读指令使处理器对每个处理深度点的一组初始设定的地层组分含量值进行第一次迭代，将各地层组分含量值代入解析存储的偏导表达式中，获得矩阵J；通过迭代增量获得当前迭代增量并转入到下一次迭代；当迭代地层组分值满足精度要求且满足附加约束条件时，即求解得到最优的地层组分含量。
本具体实施方式是对本发明的技术方案进行清楚、完整地描述，其中的实施例仅仅是本发明的一部分实施例，而并不是全部的实施例。基于本发明中的实施例，本领域技术人员在没有经过创造性劳动的前提下所获得的所有其它实施方式都属于本发明的保护范围。

Claims (10)

  1. 一种地层组分最优化确定方法，其特征在于，包括：
    根据待测地层的岩心分析资料及地质条件建立地层岩石组分模型，并确定参与模型确定的测井曲线；
    确定所述参与模型确定的测井曲线对应的测井响应方程表达式；
    解析并记录存储所述测井响应方程表达式；
    根据经过解析的所述响应方程表达式建立最优化问题的目标函数，并通过迭代算法求解所述目标函数，以确定所述待测地层的最优组分含量。
  2. 如权利要求1所述的方法，其特征在于，所述测井曲线包括：自然伽马测井曲线、深侧向电阻率测井曲线、浅侧向电阻率测井曲线、密度测井曲线、中子测井曲线或元素俘获能谱测井曲线中的至少一种。
  3. 如权利要求2所述的方法，其特征在于，所述测井响应方程为：
    Figure PCTCN2016101643-appb-100001
    其中，tci表示测井响应方程的值；
    Figure PCTCN2016101643-appb-100002
    表示待测地层的地层矿物及流体的组分含量；
    Figure PCTCN2016101643-appb-100003
    表示测井响应方程的表达式形式，并包括地层矿物和流体组分变量、数字、运算符以及参数符号。
  4. 如权利要求1所述的方法，其特征在于，所述解析并记录存储各响应方程表达式包括：
    对所述测井响应方程的表达式进行解析，并转义为用于计算机运算的后缀表达式；
    将所述后缀表达式中的每个元素通过数据结构体记录并保存为动态数组的存储结构；
    遍历所述后缀表达式的存储结构，并根据求导规则确定每个元素之间的组合关系，从而确定所述后缀表达式的偏导形式。
  5. 如权利要求1所述的方法，其特征在于，所述优化问题的目标函数包括：
    Figure PCTCN2016101643-appb-100004
    Figure PCTCN2016101643-appb-100005
    其中，
    Figure PCTCN2016101643-appb-100006
    表示目标函数；v*表示使目标函数获得最小值时的取值；tci表示根据所述测井响应方程确定的各类测井方法的响应方程值；tmi表示实际测井测量响应值；wi表示所述测井曲线在最优化模型中的权重系数；n表示求解的测井曲线的数量。
  6. 如权利要求5所述的方法，其特征在于，所述地层岩石组分模型还包括附加约束条件，所述附加约束条件为：
    Figure PCTCN2016101643-appb-100007
    其中，
    Figure PCTCN2016101643-appb-100008
    表示约束条件；Ck表示约束条件系数矩阵；
    Figure PCTCN2016101643-appb-100009
    表示待测地层的地层矿物及流体的组分含量；bk表示约束条件边界。
  7. 如权利要求6所述的方法，其特征在于，通过迭代算法求解所述目标函数包括：通过惩罚函数法将所述目标函数转化为无约束问题的表达式：
    Figure PCTCN2016101643-appb-100010
    其中，M表示惩罚因子；
    Figure PCTCN2016101643-appb-100011
    并表示惩罚函数，当
    Figure PCTCN2016101643-appb-100012
    满足约束条件
    Figure PCTCN2016101643-appb-100013
    时，惩罚项
    Figure PCTCN2016101643-appb-100014
    当
    Figure PCTCN2016101643-appb-100015
    不满足约束条件
    Figure PCTCN2016101643-appb-100016
    时，惩罚项
    Figure PCTCN2016101643-appb-100017
    且随M的增大而增大；cn表示约束条件的数目。
  8. 如权利要求7所述的方法，其特征在于，通过迭代算法求解所述目标函数还包括：通过Levenberg-Marquardt算法进行迭代增量可以表示为：
    (JTJ+μ·I)·h＝-JTR
    其中，J表示R的Jacobi矩阵；I表示单位矩阵；μ表示阻尼因子，
    Figure PCTCN2016101643-appb-100018
  9. 如权利要求8所述的方法，其特征在于，通过迭代算法求解所述目标函数还包括：
    对每个处理深度点的一组初始设定的地层组分含量值进行第一次迭代，将各地层组分含量值代入解析存储的偏导表达式中，获得矩阵J；
    通过迭代增量获得当前迭代增量并转入到下一次迭代；
    当迭代地层组分值满足精度要求且满足附加约束条件时，即求解得到最优的地层组分含量。
  10. 一种地层组分最优化确定装置，其特征在于，包括：
    测井曲线确定单元，用于根据待测地层的岩心分析资料及地质条件建立地层岩石组分模型，并确定参与模型确定的测井曲线；
    响应方程确定单元，用于确定所述参与模型确定的测井曲线对应的测井响应方程表达式；
    解析单元，用于解析并记录存储所述测井响应方程表达式；
    最优含量确定单元，根据经过解析的所述响应方程表达式建立最优化问题的目标函数，并通过迭代算法求解所述目标函数，以确定所述待测地层的最优组分含量。
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