CN109283596A - A method for interpreting physical properties of carbonate reservoirs - Google Patents

Abstract

The present invention provides a kind of carbonate reservoir physical property meanss of interpretation, its method includes: for carbonate reservoir physical property means of interpretation, comprehensive sound wave, neutron, density and Electric Log Data, carbonate formation matrix porosity in log is come with fracture porosity data separation；The nonsingular linear equal for unknown number and equation number just determines equation group, searches for an initial value close to solution by particle swarm algorithm, obtains convergent mineral constituent content based on Nonlinear Constrained Optimization Method and porosity accurately solves.The beneficial effects of the present invention are: technical solution provided by the present invention realizes the fine description to carbonate reservoir, be conducive to reservoirs exploration evaluation and production practices；The laboratory experience value for solving each mineral of rock chooses error to the error of explanation results, optimizes WELL LITHOLOGY and physical property interpretation process.

Description

A method for interpreting physical properties of carbonate reservoirs

technical field

The invention relates to the field of geological exploration, in particular to a method for interpreting physical properties of carbonate rock reservoirs.

Background technique

In addition to geological and experimental data, drilling data such as well logging and well logging are the most reliable and basic geological data that can continuously reflect the physical properties of underground rocks. As an important source of underground rock information, geophysical logging data plays an increasingly important role in the interpretation of physical properties such as formation lithology and porosity. The interpretation of physical properties of logging data is the basis for reservoir numerical simulation and sweet spot prediction, and also provides a basis for geological studies such as sedimentary facies division and sedimentary environment discrimination. The results of physical property interpretation are of great significance for reservoir evaluation and geological model division, and also play an important role in various stages of oil and gas exploration and development. Therefore, the study of accurate logging physical property interpretation method has important research significance.

At present, the commonly used reservoir logging physical property interpretation methods are mainly divided according to the type and quantity of minerals contained in the formation, and most of them are vague general methods, and there is no fine reservoir interpretation method for specific rock types and pore types. As the common data for interpretation of reservoir physical properties and lithology, the sub-he density logging does not consider the coverage of logging curve information in the current logging physical property interpretation methods. ), the interpretation precision is not enough; in addition, in the quantitative interpretation of rock porosity and lithological composition by numerical method, for the non-singular linear exact equations with equal number of unknowns and equations, The choice of laboratory empirical values of the minerals that make up the rock has a decisive influence on the interpretation results, and it is difficult to find appropriate empirical parameters to ensure that the interpretation results are in the normal range.

The logging information directly or indirectly reflects a certain aspect of the rock properties under certain assumptions, and the logging value of a certain point in the rock is the sum of the contribution values of the homogeneous parts of the rock. Therefore, a comprehensive logging interpretation model is needed to convert logging information into geological information, that is, to obtain rock porosity, rock skeleton and matrix mineral compositions of different components through a rock volume model of rock macroscopic properties. The sliding wave theory of sonic logging shows that the first wave of sonic waves propagates along the borehole first, and the porosity of sonic logging can only reflect the porosity of low-angle truncated fractures and rock matrix with uniformly distributed pores; Because of the method, the density logging can only reflect the part of the pores that are in contact during the advancement process; therefore, for the carbonate reservoirs with strong heterogeneity, the acoustic wave and density logging information cannot reflect the total porosity of the rock. , can only reflect the homogeneous porosity in the rock matrix. Neutron logging porosity actually reflects the total hydrogen index of the rock. When the formation contains natural gas, the existence of the "digging effect" requires gas-bearing correction to obtain an accurate total formation porosity.

SUMMARY OF THE INVENTION

In order to solve the above problems, the present invention provides a method for interpreting the physical properties of a carbonate rock reservoir, and a method for interpreting the physical properties of a carbonate rock reservoir, which mainly includes the following steps:

S101: Acquire logging data and logging data of a carbonate reservoir, where the logging data includes acoustic logging data, density logging data, neutron logging data, and resistivity logging data;

S102: Calculate the argillaceous mineral content V _sh of the carbonate reservoir by using formula (1):

In the above formula, V _sh is the argillaceous mineral content of the rock, GCUR is the stratigraphic constant, and SH is the natural gamma ray intensity index of the target layer;

S103: According to the conductivity of the drilling fluid filtrate in the mud logging data, use the Archie formula to calculate the rock fracture porosity φ _c of the carbonate reservoir. The calculation formula is shown in formula (2):

In the above formula, φ _c is the rock fracture porosity, σ _LLD and σ _LLS are the deep lateral conductivity and shallow lateral conductivity, respectively, which are the reciprocal of the resistivity logging data, and σ _mf and σ _w are the drilling fluid filtrate, respectively. Conductivity and formation water conductivity are logging data, _mf is the porosity index of fractures, which is a priori value;

S104: According to the rock argillaceous mineral content V _sh and the rock fracture porosity φ _c , the macro rock volume balance model is used to calculate the rock matrix porosity φ _s of the carbonate reservoir and the minerals in the carbonate reservoir. The composition volume content _Vi , the calculation formula is shown in formula (3):

In the above formula, φ _s is the rock matrix porosity, Vi is the volume content of the _i -th mineral, Δt _f , Δt _sh , Δt _ima and Δt are the fluid time difference, shale time difference, and the time of the i-th mineral, respectively. Difference value and sonic time difference value in sonic logging data; ρ _f , ρ _sh , ρ _ima and ρ are fluid density value, shale density value, ith mineral density value and density value in density logging data, respectively ; CNL _f , CNL _sh , CNL _ima and CNL are fluid neutron value, argillaceous neutron value, ith mineral neutron value and neutron value in neutron logging data, respectively; where Δt, ρ and CNL are respectively is the logging data of sonic logging, density logging and neutron logging, Δt _f , Δt _sh , Δt _ima , ρ _f , ρ _sh , ρ _ima , CNL _f , CNL _sh and CNL _ima are prior values; i =1,2,3,...,N, where N is the number of mineral species in the carbonate reservoir;

S105: Take the rock matrix porosity φ _s , the rock fracture porosity φ _c and the volume content _Vi of each mineral composition as the final interpretation result of carbonate reservoir physical properties.

Further, the logging data further includes: natural gamma logging data; in step S102, the calculation formula of SH is as shown in formula (4):

In the above formula, GR _max and GR _min are the maximum and minimum values of the natural gamma curve, respectively, GR is the natural gamma reading of the shale target layer, and is the natural gamma logging data; GR _max and GR _min are the first test value.

Further, in step S102, the value of the formation constant GCUR is 2.

Further, in step S104, the mineral content in the carbonate rock includes: argillaceous content, calcite content, dolomite content and gypsum salt content; when the argillaceous content is known, V _i represents three mineral contents respectively: Calcite content V ₁ , dolomite content V ₂ and gypsum salt content V ₃ , and the calculation steps of the macroscopic rock volume balance model are as follows:

S201: Arranged from formula (3) to obtain a non-singular linear exact equation system with the number of unknowns equal to the number of equations; the unknowns include: φ _s , V ₁ , V ₂ and V ₃ ;

S202: Search the non-singular linear exact definite equation system through the particle swarm algorithm to obtain an initial value close to the solution;

S203: According to the initial value, the nonlinear constrained optimization method is used to further solve the non-singular linear just-definite equation system, and obtain accurate solutions of rock matrix porosity φ _s and V ₁ , V ₂ , and V ₃ .

The beneficial effects brought by the technical solution provided by the present invention are as follows: the technical solution provided by the present invention has an obvious distinguishing effect on the matrix porosity and fracture porosity of carbonate rock, and realizes the fine-tuning of carbonate rock reservoirs. It is beneficial to reservoir exploration evaluation and production practice; in the quantitative interpretation of carbonate rock porosity and lithological composition by numerical method, for non-singular linear well-defined equations with equal number of unknowns and equations, particle swarm algorithm Searching for an initial value close to the solution, based on nonlinear constraint optimization, a convergent correct solution of mineral composition content and porosity is obtained, which solves the error of the selection error of the laboratory experience value of each mineral in the rock to the interpretation result, which is relatively disturbed by human factors. Small, optimized the logging lithology and physical property interpretation process.

Description of drawings

The present invention will be further described below in conjunction with the accompanying drawings and embodiments, in which:

Fig. 1 is a flow chart of a method for explaining physical properties of carbonate reservoirs in an embodiment of the present invention;

Fig. 2 is a schematic diagram of the interpretation result of the carbonate reservoir physical properties of the second member of the Jialingjiang Formation of Shuangmiao 1 in the embodiment of the present invention;

Fig. 3 is a schematic diagram showing the interpretation result of the carbonate reservoir physical properties of the third member of the Feixianguan Formation in Shuangmiao 1 according to the embodiment of the present invention.

Detailed ways

In order to have a clearer understanding of the technical features, objects and effects of the present invention, the specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

Embodiments of the present invention provide a method for interpreting physical properties of carbonate reservoirs.

Please refer to FIG. 1. FIG. 1 is a flowchart of a method for interpreting physical properties of carbonate rock reservoirs in an embodiment of the present invention, which specifically includes the following steps:

S101: Acquire logging data and logging data of a carbonate reservoir, where the logging data includes acoustic logging data, density logging data, neutron logging data, and resistivity logging data;

S102: Calculate the argillaceous mineral content V _sh of the carbonate reservoir by using formula (1):

In the above formula, V _sh is the argillaceous mineral content of the rock, GCUR is the stratigraphic constant, and SH is the natural gamma ray intensity index of the target layer;

S103: According to the conductivity of the drilling fluid filtrate in the mud logging data, use the Archie formula to calculate the rock fracture porosity φ _c of the carbonate reservoir. The calculation formula is shown in formula (2):

In the above formula, φ _c is the rock fracture porosity, σ _LLD and σ _LLS are the deep lateral conductivity and shallow lateral conductivity, respectively, which are the reciprocal of the resistivity logging data, and σ _mf and σ _w are the drilling fluid filtrate, respectively. Conductivity and formation water conductivity are logging data, _mf is the porosity index of fractures, which is a priori value;

S104: According to the rock argillaceous mineral content V _sh and the rock fracture porosity φ _c , the macro rock volume balance model is used to calculate the rock matrix porosity φ _s of the carbonate reservoir and the minerals in the carbonate reservoir. The composition volume content _Vi , the calculation formula is shown in formula (3):

In the above formula, φ _s is the rock matrix porosity, Vi is the volume content of the _i -th mineral, Δt _f , Δt _sh , Δt _ima and Δt are the fluid time difference, shale time difference, and the time of the i-th mineral, respectively. Difference value and sonic time difference value in sonic logging data; ρ _f , ρ _sh , ρ _ima and ρ are fluid density value, shale density value, ith mineral density value and density value in density logging data, respectively ; CNL _f , CNL _sh , CNL _ima and CNL are fluid neutron value, argillaceous neutron value, i-th mineral neutron value and neutron value in neutron logging data, respectively; where Δt, ρ and CNL are respectively is the logging data of sonic logging, density logging and neutron logging, Δt _f , Δt _sh , Δt _ima , ρ _f , ρ _sh , ρ _ima , CNL _f , CNL _sh and CNL _ima are prior values; i =1,2,3,...,N, where N is the number of mineral species in the carbonate reservoir;

S105: Take the rock matrix porosity φ _s , the rock fracture porosity φ _c and the volume content _Vi of each mineral composition as the final interpretation result of carbonate reservoir physical properties.

The logging data further includes: natural gamma logging data; in step S102, the calculation formula of SH is shown in formula (4):

In the above formula, GR _max and GR _min are the maximum and minimum values of the natural gamma curve, respectively, GR is the natural gamma reading of the shale target layer, and is the natural gamma logging data; GR _max and GR _min are the first test value.

In step S102, the value of the formation constant GCUR is 2.

In step S104, the mineral content in the carbonate rock includes: argillaceous content, calcite content, dolomite content and gypsum salt content; when the argillaceous content is known, V _i respectively represents three mineral contents: calcite content V _1. The dolomite content V ₂ and the gypsum salt content V ₃ , and the calculation steps of the macroscopic rock volume balance model are as follows:

S201: Arranged by formula (3) to obtain a non-singular linear exact equation system with the number of unknowns equal to the number of equations; the unknowns include: φ _s , V ₁ , V ₂ and V ₃ ;

S202: Search the non-singular linear exact definite equation system through the particle swarm algorithm to obtain an initial value close to the solution;

S203: According to the initial value, the nonlinear constrained optimization method is used to further solve the non-singular linear just-definite equation system, and obtain accurate solutions of rock matrix porosity φ _s and V ₁ , V ₂ , and V ₃ .

In the embodiment of the present invention, some logging data are shown in the following table:

In order to highlight the technical effect, the present invention uses the provided technical solution to interpret the physical properties of the pore-fracture type rock reservoirs in the marine carbonate reservoirs of the Jialingjiang Formation and Feixianguan Formation. Figures 2 and 3 compare the Shuangmiao The relationship between the measured porosity of the carbonate reservoir cores of the second member of the Jialingjiang Formation and the third member of the Feixianguan Formation in Well 1 and the porosity of the rock matrix, rock fracture porosity and total porosity of the logging interpretation can be seen. The porosity value is basically consistent with the measured value of the core, and it also has an obvious distinguishing effect on matrix pores and fracture pores, which is consistent with the statistics of core pores, caves and fractures (Table 1). The second member of the Jialingjiang Formation is dominated by fractured reservoirs , a large number of semi-filled fractures and micro fractures are developed; while the third member of Feixianguan Formation is a pore-fracture reservoir with developed matrix pores and local fractures. The calculated rock composition content is in good agreement with the cuttings logging results.

Table 1 Statistical table of holes, holes and fractures in the carbonate reservoir drilling cores of Well Shuangmiao 1

The beneficial effects of the present invention are as follows: the technical solution provided by the present invention has an obvious distinguishing effect on the matrix porosity and fracture porosity of the carbonate rock, realizes the fine description of the carbonate rock reservoir, and is beneficial to the reservoir Exploration evaluation and production practice; in the quantitative interpretation of carbonate rock porosity and lithological composition by numerical method, for non-singular linear exact equations with equal number of unknowns and equations, a particle swarm algorithm is used to search for a solution close to the solution. Initial value, based on nonlinear constraint optimization, the convergent correct solution of mineral composition content and porosity is obtained, which solves the error of interpretation results caused by the selection error of laboratory experience value of each mineral in the rock, and is less affected by human factors, and optimizes logging Lithologic and physical property interpretation process.

The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection of the present invention. within the range.

Claims (4)

1. a kind of carbonate reservoir physical property means of interpretation, it is characterised in that: the following steps are included:

S101: obtaining the logging data and log data of carbonate reservoir, and the log data includes sound wave measuring well curve, close Spend log data, neutron well logging data and Electric Log Data；

S102: the rock shale mineral content V of carbonate reservoir is calculated using formula (1)_sh:

In above formula, V_shFor rock shale mineral content, GCUR is stratum constant, and layer natural gamma rays intensity refers to for the purpose of SH Number；

S103: according to the drilling fluid filtrate conductivity in logging data, carbonate reservoir is calculated using Archie formula Rock fracture porosity φ_c, shown in calculation formula such as formula (2):

In above formula, φ_cFor rock fracture porosity, σ_LLDAnd σ_LLSRespectively deep lateral conductivity rate and shallow lateral conductivity rate, are resistance The inverse of rate log data, σ_mfAnd σ_wRespectively drilling fluid filtrate conductivity and stratum water conductivity are logging data,_mfTo split The porosity exponent of seam is priori value；

S104: according to rock shale mineral content V_shWith rock fracture porosity φ_c, using macroscopical rock volume balance model, The matrix porosity φ of carbonate reservoir is calculated_sWith mineral composition volume content V each in carbonate reservoir_i, meter It calculates shown in formula such as formula (3):

In above formula, φ_sFor matrix porosity, V_iFor i-th kind of mineral volume content, Δ t_f、Δt_sh、Δt_imaDistinguish with Δ t For the interval transit time value in fluid time difference value, shale time difference value, i-th kind of mineral time difference value and sound wave measuring well curve；ρ_f、ρ_sh、ρ_ima With the density value that ρ is respectively in fluid density value, shale density value, i-th kind of mineral density value and density log data；CNL_f、 CNL_sh、CNL_imaIt is respectively subvalue in fluid, subvalue in shale, in i-th kind of mineral in subvalue and neutron well logging data with CNL Middle subvalue；Wherein Δ t, ρ and CNL is respectively the log data of acoustic logging, density log, neutron well logging, Δ t_f、Δt_sh、Δ t_ima、ρ_f、ρ_sh、ρ_ima、CNL_f、CNL_shAnd CNL_imaFor priori value；I=1,2,3 ..., N, N are the mineral in carbonate reservoir Number of species；

S105: by matrix porosity φ_s, rock fracture porosity φ_cWith each mineral composition volume content V_i, as final Carbonate reservoir physical property explanation results.

2. a kind of carbonate reservoir physical property means of interpretation as described in claim 1, it is characterised in that: the log data is also It include: gamma ray log data；In step S102, shown in the calculation formula of SH such as formula (4):

In above formula, GR_maxAnd GR_minRespectively gamma ray curve maximum and minimum, GR are the nature of target zone containing shale gal Horse reading is gamma ray log data；GR_max、GR_minFor priori value.

3. a kind of carbonate reservoir physical property means of interpretation as described in claim 1, it is characterised in that: in step S102, ground The value of layer constant GCUR is 2.

4. a kind of carbonate reservoir physical property means of interpretation as described in claim 1, it is characterised in that: in step S104, carbon Carbonate Rocks Minerals content includes: shale content, calcite content, dolomite content mixing paste salt content；Known to shale content In the case where, V_iRespectively indicate three kinds of mineral contents: calcite content V₁, dolomite content V₂Mixing paste salt content V₃,；Macroscopical rock Steps are as follows for the calculating of stone volumetric balance model:

S201: the unknown number number nonsingular linear equal with equation number is obtained by formula (3) arrangement and just determines equation group；It is described not Know that number includes: φ_s、V₁、V₂And V₃；

S202: just determining equation group to the nonsingular linear by particle swarm algorithm and scan for, and obtains one close to solution Initial value；

S203: according to the initial value, it is further that equation group is just determined to the nonsingular linear using Nonlinear Constrained Optimization Method It solves, obtains matrix porosity φ_sAnd V₁、V₂、V₃Exact Solutions.