CN114876454A - Method and system for identifying complex lithologic water flooded layer of highly deviated well - Google Patents
Method and system for identifying complex lithologic water flooded layer of highly deviated well Download PDFInfo
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP 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
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
- E21B47/02—Determining slope or direction
- E21B47/022—Determining slope or direction of the borehole, e.g. using geomagnetism
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
- E21B47/04—Measuring depth or liquid level
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention relates to a method and a system for identifying a complex lithologic water flooded layer of a highly deviated well, which are characterized by comprising the following steps: acquiring well deviation data of each sampling point of a highly deviated well to be detected, and determining a well track of the highly deviated well; calculating to obtain the content and the porosity of rock mineral components of the complex lithologic reservoir in the highly deviated well and the vertical well; establishing a resistivity forward model of the highly-deviated well to be detected, and determining apparent resistivities of different detection depths; based on the determined well track, according to the logging data of the highly deviated well to be detected and the determined apparent resistivity of different detection depths, carrying out iterative inversion to obtain the resistivity of the target interval of the highly deviated well to be detected; and comparing the logging data of the highly deviated well to be detected and the target interval corresponding to the vertical well, the rock mineral component content, the porosity and the resistivity obtained by inversion of the highly deviated well to be detected, and identifying the flooding layer of the highly deviated well to be detected.
Description
Technical Field
The invention relates to the field of flooded layer identification, in particular to a method and a system for identifying a complex lithologic flooded layer of a highly deviated well.
Background
In order to reduce the operation cost to the maximum extent and improve the development efficiency of oil and gas fields, special process wells such as highly-deviated wells or horizontal wells appear successively. The borehole environment of a highly deviated well is very complex, and the logging response is influenced by a plurality of factors, so that the measured value of the resistivity is greatly different from the resistivity of an undisturbed stratum, namely the resistivity measured by a logging instrument cannot truly reflect the resistivity of the undisturbed stratum. Particularly in complex lithologic strata such as carbonate rock, volcanic rock, metamorphic rock and the like, the resistivity measured by a logging instrument is the comprehensive reflection of the strong heterogeneity of a rock framework, fluid and a highly deviated well in the strata, so that the oil layer and the water flooded layer in the highly deviated well of the complex lithologic strata are difficult to accurately identify only based on the resistivity measured in the highly deviated well.
To accurately identify the fluid properties in highly deviated wells, resistivity inversion is required to highlight the contribution of the fluid to the resistivity. At present, resistivity inversion mainly aims at one-dimensional and two-dimensional inversion of a stratum (a conventional vertical well) with good rotational symmetry of a borehole axis, is not suitable for resistivity inversion of a highly-deviated well or a horizontal well with an asymmetric stratum structure, and cannot provide information such as layer boundary distance and anisotropy. The one-dimensional inversion mainly considers the influence of the borehole size and the invasion degree of the drilling fluid; the two-dimensional inversion mainly takes into account the effects of borehole size, drilling fluid invasion and upper and lower surrounding rock resistivities. For the resistivity inversion of a highly deviated well or a horizontal well with an asymmetric stratum structure, three-dimensional inversion is mainly adopted, and besides the influences of the size of a well hole, the invasion degree of drilling fluid and the resistivity of upper and lower surrounding rocks, the influences of factors such as a well inclination angle and anisotropy need to be considered. At present, the research of one-dimensional and two-dimensional resistivity inversion problems is systematic and deep, and is simple and easy to implement in practical application. For three-dimensional inversion, the current single-boundary inversion is feasible, the double-boundary and multi-boundary inversion starts just, and the consideration on the influence of factors such as a well inclination angle and anisotropy is insufficient.
The method has the advantages that the fluid properties in the highly deviated well of the complex lithologic stratum can be accurately identified, the adjustment of the well track of the highly deviated well can be guided, and whether drilling needs to be finished in advance or not is prompted, so that a foundation is laid for smooth and efficient drilling of the highly deviated well, the operation cost is greatly saved, and meanwhile, the development benefit of an oil and gas field is improved. However, no effective method for accurately identifying and forming a water flooded layer in a highly deviated well of a complex lithologic stratum exists at present.
Disclosure of Invention
In view of the above problems, the present invention provides a method and a system for identifying a complicated lithologic water flooded layer of a highly deviated well, which can accurately identify the water flooded layer in the highly deviated well of the complicated lithologic stratum.
In order to achieve the purpose, the invention adopts the following technical scheme: in a first aspect, a method for identifying a complex lithologic water flooded layer of a highly deviated well is provided, which includes:
acquiring well deviation data of each sampling point of a highly deviated well to be detected, and determining a well track of the highly deviated well;
respectively measuring logging data of a target interval of a straight well in a highly deviated well to be measured and an adjacent well thereof, and calculating to obtain the rock mineral component content and the porosity of a complex lithologic reservoir in the highly deviated well and the straight well;
quantitatively analyzing the influence of each factor of the highly deviated well to be detected on the resistivities of different detection depths, establishing a resistivity forward model of the highly deviated well to be detected, and determining the apparent resistivities of the different detection depths;
based on the determined well track, according to the logging data of the highly deviated well to be detected and the determined apparent resistivity of different detection depths, carrying out iterative inversion to obtain the resistivity of the target interval of the highly deviated well to be detected;
and comparing the logging data of the highly deviated well to be detected and the target interval corresponding to the vertical well, the rock mineral component content, the porosity and the resistivity obtained by inversion of the highly deviated well to be detected, and identifying the water flooded layer of the highly deviated well to be detected.
Further, the acquiring well deviation data of each sampling point of the highly deviated well to be detected and determining the well track of the highly deviated well includes:
acquiring well deviation data of each sampling point of a highly deviated well to be detected, wherein the well deviation data comprises depth measurement, vertical depth, a well deviation angle and an azimuth angle;
and obtaining the position of each sampling point of the highly deviated well to be detected according to the acquired well deviation data of the highly deviated well to be detected, and further determining to obtain the well track of the highly deviated well to be detected.
Further, the well log data includes natural gamma, natural potential, deep resistivity, shallow resistivity, bulk density, neutron porosity, compressional moveout, electrical imaging, and array acoustic.
Further, the quantitative analysis of the influence of each factor of the highly deviated well to be detected on the resistivity of different detection depths, the establishment of the resistivity forward model of the highly deviated well to be detected, and the determination of the apparent resistivity of different detection depths comprises:
quantitatively analyzing the influence of each factor of the highly deviated well to be detected on the resistivity at different detection depths to obtain a chart of the influence of each factor on the resistivity at different detection depths;
and establishing a resistivity forward model of the highly-deviated well to be detected, and determining apparent resistivities of different detection depths according to the charts of influences of various factors on the resistivities of the different detection depths.
Further, the establishing of the resistivity forward model of the highly deviated well to be detected and the determining of the apparent resistivity of different detection depths according to the charts of the influence of various factors on the resistivity of different detection depths include:
considering the parameters of the stratum model, setting the distribution range of the corresponding parameters, and establishing a resistivity forward model of the highly deviated well to be measured;
and forward calculating to obtain apparent resistivities of different detection depths according to the established resistivity forward model of the highly deviated well to be detected and the obtained chart of the influence of each factor on the resistivities of different detection depths.
Further, the obtaining of the resistivity of the target interval of the highly deviated well to be detected through iterative inversion based on the determined well track and according to the logging data of the highly deviated well to be detected and the determined apparent resistivity of different detection depths includes:
preprocessing the logging data of the highly deviated well to be detected, and segmenting the preprocessed logging data;
acquiring the logging data after the ith section of pretreatment;
performing inversion by adopting a damping least square method based on the determined well track according to the logging data after the i-th section of preprocessing and the determined apparent resistivity of different detection depths to obtain the resistivity of the target interval of the highly-deviated well to be detected;
determining whether the inversion of all the sections is finished, and if so, outputting the resistivity; otherwise, acquiring the logging data after the i +1 th section of preprocessing and carrying out inversion processing.
Further, the contrast survey data, rock mineral composition content, porosity and the resistivity that the great deviation well inversion that awaits measuring of great deviation well and the corresponding vertical well target interval obtained, discern the waterflooding layer of the great deviation well that awaits measuring, include:
when the content, the porosity and the resistivity of the rock mineral components of the target interval of the highly deviated well to be detected after inversion are smaller than the change of the content, the porosity and the resistivity of the rock mineral components corresponding to the target interval which is the same as that of the adjacent well vertical well, the complex lithology reservoir in the target interval is an oil layer;
and when the content, the porosity and the resistivity of the target interval of the highly deviated well to be detected after inversion and the change of the content, the porosity and the resistivity of the target interval of the highly deviated well before corresponding inversion are smaller than a preset threshold value, and the resistivity of the target interval of the highly deviated well to be detected after inversion is smaller than the resistivity of the same target interval of the vertical well of the adjacent well, the complex lithological reservoir in the target interval is a water flooded layer.
In a second aspect, a system for identifying a complex lithologic water flooded layer of a highly deviated well is provided, which includes:
the borehole trajectory determination module is used for acquiring well deviation data of each sampling point of the highly deviated well to be detected and determining the borehole trajectory of the highly deviated well;
the logging data measuring module is used for respectively measuring logging data of a target interval of a straight well in a highly deviated well to be measured and an adjacent well thereof, and calculating to obtain the rock mineral component content and the porosity of a complex lithologic reservoir in the highly deviated well and the straight well;
the apparent resistivity determining module is used for quantitatively analyzing the influence of various factors of the highly deviated well to be detected on the resistivities at different detection depths, establishing a resistivity forward model of the highly deviated well to be detected and determining the apparent resistivities at different detection depths;
the inversion module is used for carrying out iterative inversion to obtain the resistivity of the target interval of the highly deviated well to be detected according to the well logging data of the highly deviated well to be detected and the determined apparent resistivity of different detection depths on the basis of the determined well track;
and the identification module is used for comparing the logging data of the highly deviated well to be detected and the target interval of the corresponding vertical well, the rock mineral component content, the porosity and the resistivity obtained by inversion of the highly deviated well to be detected, and identifying the water flooded layer of the highly deviated well to be detected.
In a third aspect, a processing device is provided, which includes computer program instructions, where the computer program instructions, when executed by the processing device, are configured to implement the steps corresponding to the method for identifying a complex lithologic water flooded layer of a highly deviated well.
In a fourth aspect, a computer readable storage medium is provided, where the computer readable storage medium stores computer program instructions, where the computer program instructions, when executed by a processor, are configured to implement the steps corresponding to the method for identifying a complex lithologic water flooded layer of a highly deviated well.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the invention provides an effective, simple and practical mode while ensuring accurate identification of the water flooded layer in the highly deviated well of the complex lithologic stratum.
2. The method can guide the adjustment of the well track of the highly deviated well, and prompt whether the drilling needs to be finished in advance, thereby greatly saving the operation cost and improving the development benefit of the oil and gas field.
In conclusion, the method can be widely applied to the field of identification of the water flooded layer.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Like reference numerals refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic flow chart of a method provided by an embodiment of the present invention;
FIG. 2 is a schematic flow diagram of resistivity inversion while drilling provided by an embodiment of the invention;
FIG. 3 is a schematic view of a borehole trajectory for a highly deviated M-field well according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the well logging results of the interval of interest A of the highly deviated well in M oil field according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the well logging results of the M oil field highly deviated well A, the adjacent well, the vertical well B and the well;
FIG. 6 is a schematic representation of the while drilling phase resistivity versus invasion depth provided by an embodiment of the present invention;
fig. 7 is a schematic diagram of a comprehensive interpretation result of logging in an interval of an order a of a highly deviated well in an M-field according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless specifically identified as an order of performance. It should also be understood that additional or alternative steps may be used.
Although the terms first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be only used to distinguish one element, component, region, layer or section from another region, layer or section. Terms such as "first," "second," and other numerical terms when used herein do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.
For convenience of description, spatially relative terms, such as "inner", "outer", "lower", "upper", and the like, may be used herein to describe one element or feature's relationship to another element or feature as illustrated in the figures. Such spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures.
The method and the system for identifying the complex lithologic water flooded layer of the highly deviated well provided by the embodiment of the invention are used for accurately identifying the complex lithologic water flooded layer of the highly deviated well, and the method for identifying the complex lithologic water flooded layer of the highly deviated well based on resistivity inversion is formed, so that the water flooded layer in the highly deviated well of the complex lithologic stratum can be accurately identified, and the development benefit of an oil-gas field is effectively improved.
Example 1
As shown in fig. 1, the present embodiment provides a method for identifying a complex lithologic water flooded layer of a highly deviated well, which includes the following steps:
1) acquiring well deviation data of each sampling point of a highly deviated well to be detected, and determining a well track of the highly deviated well, wherein the well deviation data specifically comprises the following steps:
1.1) acquiring well deviation data of each sampling point of the highly deviated well to be detected, wherein the well deviation data comprises depth measurement, vertical depth, well deviation angle, azimuth angle and the like.
1.2) obtaining the position of each sampling point of the highly-deviated well to be detected according to the acquired well deviation data of the highly-deviated well to be detected, further determining to obtain the well track of the highly-deviated well to be detected, and determining the influence of the well deviation angle on the resistivity at a specific certain depth based on the well track of the highly-deviated well to be detected, wherein the change of the resistivity is caused by the well deviation angle or the change of fluid, thereby providing basis for identifying the water flooded layer.
2) And measuring logging data of the target interval of the highly deviated well to be measured by a logging instrument, and calculating to obtain the rock mineral component content and the porosity of the complex lithologic reservoir in the highly deviated well.
In particular, the log data includes natural gamma, natural potential, deep resistivity, shallow resistivity, bulk density, neutron porosity, compressional moveout, electrical imaging, and array acoustic, among others.
The way to calculate the rock mineral component content and porosity of complex lithologic reservoirs in deviated wells is many, for example:
where ρ is b 、ρ f 、ρ bc1 、ρ bc2 Respectively representing rock bulk density, fluid bulk density and two rock minerals C 1 And C 2 The bulk density of (a); phi is a N 、φ Nf 、
Respectively representing the neutron porosity value of rock, the neutron porosity value of fluid and two rock minerals C 1 And C 2 Neutron porosity response value of (a); phi, V c1 、V c2 Respectively representing porosity and two rock minerals C 1 And C 2 Is the volume percentage of the rock.
3) Selecting a straight well from adjacent wells of the highly deviated well to be measured, measuring by a logging instrument to obtain logging data of the same target interval of the straight well, and determining the rock mineral component content and the porosity of a complex lithologic reservoir in the straight well.
4) Numerical simulation quantitative analysis is carried out on the influences of factors such as borehole size, inclination angle, mud invasion, surrounding rock thickness, target layer thickness and anisotropy of the highly deviated well to be detected on the resistivities at different detection depths, a chart of the influences of the factors on the resistivities at different detection depths is obtained, and only if the influences of the factors on the resistivities are determined, whether the change of the resistivities is caused by the factors or the change of the fluid can be determined, so that a basis is provided for identifying the flooded layer.
5) Establishing a resistivity forward model of the highly-deviated well to be detected, and determining apparent resistivity of different detection depths according to the chart of influence of various factors on the resistivity of different detection depths, wherein the specific steps are as follows:
and 5.1) considering stratum model parameters such as borehole size, well inclination angle, mud resistivity, target layer horizontal resistivity, anisotropy coefficient, upper surrounding rock resistivity, lower surrounding rock resistivity, target layer thickness, layer boundary distance and the like, setting the distribution range of the corresponding parameters, and establishing a resistivity forward model of the highly deviated well to be measured.
And 5.2) forward calculating to obtain apparent resistivities of different detection depths according to the established resistivity forward model of the highly-deviated well to be detected and the obtained chart of the influence of each factor on the resistivities of different detection depths.
6) As shown in fig. 2, based on the determined borehole trajectory, according to the logging data of the highly deviated well to be measured and the apparent resistivity of different detection depths determined in step 5), performing iterative inversion to obtain the resistivity of the target interval of the highly deviated well to be measured, which specifically includes:
6.1) preprocessing the logging data of the highly deviated well to be detected, and segmenting the preprocessed logging data:
6.1.1) preprocessing the logging data of the highly deviated well to be detected, wherein the preprocessing comprises borehole correction, smooth filtering, automatic layering, model initial value acquisition, logging curve constraint and the like.
6.1.2) segmenting the preprocessed logging data.
6.2) obtaining the logging data after the i-th section of preprocessing.
6.3) carrying out inversion by adopting a damping least square method based on the determined well track according to the logging data after the i-th section of preprocessing and the apparent resistivity of different detection depths determined in the step 5) to obtain the resistivity of the target interval of the highly-deviated well to be detected.
Specifically, the sum of squares of differences between formation model response data (i.e., apparent resistivities at different detection depths determined in step 5) and measured data (i.e., pre-processed logging data) is taken as an objective function, and the variation of formation model parameters (including borehole size, inclination angle, mud resistivity, target layer horizontal resistivity, anisotropy coefficient, upper surrounding rock resistivity, lower surrounding rock resistivity, target layer thickness and layer boundary distance) is iteratively calculated by adopting a damped least square method so that the response data gradually approaches the measured data until a given iterative convergence condition is met, thereby realizing segmented iteration and continuous inversion.
6.4) determining whether the inversion of all the segments is finished, and if so, outputting an inversion result, namely the resistivity; otherwise, entering step 6.2) to obtain the logging data after the i +1 th section of preprocessing.
7) And comparing the logging data of the highly deviated well to be detected and the target interval corresponding to the vertical well, the rock mineral component content, the porosity and the resistivity obtained by inversion of the highly deviated well to be detected, and identifying the water flooded layer of the highly deviated well to be detected.
Specifically, when the content, porosity and resistivity of the rock mineral component after inversion of the target interval of the highly deviated well to be detected are similar to the content, porosity and resistivity of the rock mineral component corresponding to the same target interval of the vertical well of the adjacent well, the complex lithology reservoir in the target interval is an oil layer, wherein the similarity is that the change is smaller than a preset threshold value, for example, the change of the resistivity is smaller than 20%; and when the content, porosity and resistivity of the rock mineral components of the target interval of the highly deviated well to be detected after inversion are similar to the content, porosity and resistivity of the logging data of the rock mineral components of the target interval of the highly deviated well before corresponding inversion, and the resistivity of the target interval of the highly deviated well to be detected after inversion is smaller than the resistivity of the same target interval of the vertical well of the adjacent well, the complex lithological reservoir in the target interval is a water flooded layer.
The complex lithologic water flooded layer identification method for the highly deviated well is explained in detail by taking the carbonate water flooded layer of the highly deviated well of the M oil field as a specific embodiment:
1) well deviation data of each sampling point of the M oil field highly deviated well A is obtained, the well track of the highly deviated well is determined, and as shown in figure 3, a highly deviated section is below 3500M of the highly deviated well A.
2) Logging data of a target interval of a highly deviated well A of the M oil field are measured through a logging instrument, rock mineral component content and porosity of a complex lithologic reservoir in the highly deviated well A are obtained through calculation, logging shows that the lithologic property of the highly deviated well A is limestone, namely in a stratum volume model, except the porosity and the shale content, the rest is the limestone content, and the method is shown in figure 4.
The mud content, the porosity and the limestone content of the limestone reservoir in the highly deviated well A can be obtained through software processing interpretation such as Geolol, GeoFrame, Forward, Lead and the like. As shown in fig. 4, wherein, the 1 st track is the measurement depth of the highly deviated well a; the 2 nd path is the vertical depth of the highly deviated well A; the 3 rd channel is natural gamma and represents lithology characteristics of the stratum; the 4 th path is a deep and shallow resistivity logging curve, the electrical characteristics of the stratum are described, and the deep resistivity is about 3-4 omega.m below the vertical depth 3840 m; the 5 th path is longitudinal wave time difference and reflects the physical property characteristics of the stratum; the 6 th path is a lithologic section calculated by logging, and mainly comprises limestone and mudstone; the 7 th lane is logging lithology, shown as limestone; lane 8 is the content and total content of the various hydrocarbon components; and the 9 th lane is the oil-bearing property of the logging.
3) Selecting one straight well B in adjacent wells of the M oil field highly deviated well A, measuring by a logging instrument to obtain logging data of a target interval of the straight well B, and calculating to obtain the rock mineral component content and porosity of a complex lithologic reservoir in the straight well B.
The mud content, the porosity and the limestone content of the limestone reservoir in the vertical well B can be obtained through software processing and interpretation of Geolol, GeoFrame, Forward, Lead and the like, as shown in figure 5, wherein the 1 st channel is the measurement depth of the vertical well B; the 2 nd channel is the vertical depth of the vertical well B; the 3 rd channel is natural gamma and represents lithology characteristics of the stratum; the 4 th path is a deep and shallow resistivity logging curve, the electrical characteristics of the stratum are described, and the vertical depth is 3840m, and the deep resistivity is about 10 omega.m; the 5 th path is longitudinal wave time difference and reflects the physical property characteristics of the stratum; the 6 th path is a lithologic section calculated by logging, and mainly comprises limestone and mudstone; the 7 th lane explains the results for the well log as the reservoir.
4) And (3) carrying out numerical simulation quantitative analysis on the influences of factors such as the borehole size, the inclination angle, the mud invasion, the surrounding rock thickness, the target layer thickness and the anisotropy of the highly deviated well A on the resistivity at different detection depths to obtain a chart of the influences of the factors on the resistivity at different detection depths. When the formation resistivity was 10 Ω · m, the flushing zone resistivity was 2 Ω · m, and the mud penetration depth was 0.5m, the phase depth resistivity at 2MHz decreased from 10 Ω · m to 4 Ω · m, as shown in fig. 6.
5) Considering the formation model parameters such as the borehole size, the well inclination angle, the mud resistivity, the horizontal resistivity of a target layer, the anisotropy coefficient, the resistivity of an upper surrounding rock, the resistivity of a lower surrounding rock, the thickness of the target layer, the layer boundary distance and the like, setting the distribution range of corresponding parameters, establishing a resistivity forward model of the carbonate rock formation of the M oil field, and respectively obtaining the apparent resistivities of corresponding different detection depths in a fixed cycle manner on the basis of 9 parameters in the table 1 and the obtained chart of the influence of each factor on the resistivities of different detection depths as shown in the following table 1:
table 1: resistivity forward modeling parameter while drilling for M oil field carbonate rock stratum
6) And based on the determined well track, obtaining the resistivity of the target interval of the highly-deviated well to be detected through iterative inversion according to the logging data of the highly-deviated well to be detected and the apparent resistivity of different detection depths determined in the step 5).
7) Comparing the logging data of the highly deviated well to be detected and the corresponding vertical well target interval, the rock mineral component content, the porosity and the resistivity obtained by inversion of the highly deviated well to be detected, and identifying the flooding layer of the highly deviated well to be detected:
the inversion result of the phase depth resistivity of the M oil field highly deviated well A while drilling is shown in figure 7 as the 4 th channel, the vertical depth is 3840.3-3843.5M, the resistivity in the logging data before inversion is 3-4 omega M, and the resistivity after inversion is 10 omega M; the depth resistivity of the adjacent vertical well B of the highly deviated well A at the vertical depth of 3840m is about 10 omega m. Therefore, based on the inversion of resistivity while drilling, the logging comprehensively explains that the vertical depth of the highly deviated well A is 3843.5m or more, and the vertical depth is 3843.5m or less, so that the highly deviated well A is an oil layer and a water flooded layer.
As shown in fig. 7, wherein, the 1 st track is the measurement depth of the highly deviated well a; the 2 nd path is the vertical depth of the highly deviated well A; the 3 rd channel is natural gamma and represents lithology characteristics of the stratum; the 4 th path is a logging curve of the phase depth and the shallow resistivity before inversion and the phase depth resistivity after inversion, and the electrical characteristics of the stratum are described; the 5 th path is longitudinal wave time difference and reflects the physical property characteristics of the stratum; the 6 th path is a lithologic section calculated by logging, and mainly comprises limestone and mudstone; the 7 th lane is logging lithology, shown as limestone; lane 8 is the content and total content of the various hydrocarbon components; the 9 th track is the oil-bearing property of logging; and the 9 th path is a comprehensive interpretation conclusion of logging, including an oil layer and a water flooded layer.
Example 2
The embodiment provides a complicated lithologic water flooded layer identification system of highly deviated well, includes:
and the well track determining module is used for acquiring well deviation data of each sampling point of the highly deviated well to be detected and determining the well track of the highly deviated well.
And the logging data measuring module is used for respectively measuring logging data of a target interval of a straight well in the highly deviated well to be measured and the adjacent well thereof, and calculating to obtain the rock mineral component content and the porosity of the complex lithologic reservoir in the highly deviated well and the straight well.
And the apparent resistivity determining module is used for quantitatively analyzing the influence of each factor of the highly deviated well to be detected on the resistivities at different detection depths, establishing a resistivity forward model of the highly deviated well to be detected and determining the apparent resistivities at different detection depths.
And the inversion module is used for carrying out iterative inversion to obtain the resistivity of the target interval of the highly-deviated well to be detected according to the logging data of the highly-deviated well to be detected and the determined apparent resistivity of different detection depths on the basis of the determined well track.
And the identification module is used for comparing the logging data of the highly deviated well to be detected and the target interval of the corresponding vertical well, the rock mineral component content, the porosity and the resistivity obtained by inversion of the highly deviated well to be detected, and identifying the water flooded layer of the highly deviated well to be detected.
Example 3
This embodiment provides a processing device corresponding to the method for identifying a complex lithologic water flooded layer of a highly deviated well provided in this embodiment 1, where the processing device may be a processing device for a client, such as a mobile phone, a laptop, a tablet computer, a desktop computer, and the like, to execute the method of embodiment 1.
The processing equipment comprises a processor, a memory, a communication interface and a bus, wherein the processor, the memory and the communication interface are connected through the bus so as to complete mutual communication. The memory stores a computer program that can be run on the processing device, and the processing device executes the complex lithologic water flooded layer identification method of the highly deviated well provided in the embodiment 1 when running the computer program.
In some implementations, the Memory may be a high-speed Random Access Memory (RAM), and may also include non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
In other implementations, the processor may be any type of general-purpose processor such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), and the like, and is not limited herein.
Example 4
This embodiment provides a computer program product corresponding to the method for identifying a complex lithologic water flooded layer of a highly deviated well provided in this embodiment 1, and the computer program product may include a computer readable storage medium carrying computer readable program instructions for executing the method for identifying a complex lithologic water flooded layer of a highly deviated well described in this embodiment 1.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any combination of the foregoing.
The above embodiments are only used for illustrating the present invention, and the structure, connection mode, manufacturing process, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.
Claims (10)
1. A method for identifying a complex lithologic water flooded layer of a highly deviated well is characterized by comprising the following steps:
acquiring well deviation data of each sampling point of a highly deviated well to be detected, and determining a well track of the highly deviated well;
respectively measuring logging data of a target interval of a straight well in a highly deviated well to be measured and an adjacent well thereof, and calculating to obtain the rock mineral component content and the porosity of a complex lithologic reservoir in the highly deviated well and the straight well;
quantitatively analyzing the influence of each factor of the highly deviated well to be detected on the resistivities of different detection depths, establishing a resistivity forward model of the highly deviated well to be detected, and determining the apparent resistivities of the different detection depths;
based on the determined well track, according to the logging data of the highly deviated well to be detected and the determined apparent resistivity of different detection depths, carrying out iterative inversion to obtain the resistivity of the target interval of the highly deviated well to be detected;
and comparing the logging data of the highly deviated well to be detected and the target interval corresponding to the vertical well, the rock mineral component content, the porosity and the resistivity obtained by inversion of the highly deviated well to be detected, and identifying the water flooded layer of the highly deviated well to be detected.
2. The method for identifying the complex lithologic water flooded layer of the highly deviated well as claimed in claim 1, wherein the obtaining the well deviation data of each sampling point of the highly deviated well to be tested and determining the borehole trajectory of the highly deviated well comprises:
acquiring well deviation data of each sampling point of the highly deviated well to be detected, wherein the well deviation data comprises depth measurement, vertical depth, well deviation angle and azimuth angle;
and obtaining the position of each sampling point of the highly deviated well to be detected according to the acquired well deviation data of the highly deviated well to be detected, and further determining to obtain the well track of the highly deviated well to be detected.
3. The method of claim 1, wherein the log data comprises natural gamma, natural potential, deep resistivity, shallow resistivity, bulk density, neutron porosity, compressional moveout, electrical imaging, and arrayed acoustic waves.
4. The method for identifying the complex lithologic water flooded layer of the highly deviated well as claimed in claim 1, wherein the quantitative analysis of the influence of various factors of the highly deviated well to be detected on the resistivity of different detection depths, and the establishment of the resistivity forward model of the highly deviated well to be detected, and the determination of the apparent resistivity of the highly deviated well to be detected at different detection depths comprises:
quantitatively analyzing the influence of each factor of the highly deviated well to be detected on the resistivity at different detection depths to obtain a chart of the influence of each factor on the resistivity at different detection depths;
and establishing a resistivity forward model of the highly-deviated well to be detected, and determining apparent resistivities of different detection depths according to the charts of influences of various factors on the resistivities of the different detection depths.
5. The method for identifying the complex lithologic water flooded layer of the highly deviated well as claimed in claim 4, wherein the establishing of the resistivity forward model of the highly deviated well to be detected and the determining of the apparent resistivity of different detection depths according to the plate of the influence of each factor on the resistivity of different detection depths comprises:
considering the formation model parameters, setting the distribution range of the corresponding parameters, and establishing a resistivity forward model of the highly deviated well to be measured;
and forward calculating to obtain apparent resistivity of different detection depths according to the established resistivity forward model of the highly deviated well to be detected and the obtained chart of the influence of each factor on the resistivity of different detection depths.
6. The method for identifying the complex lithologic water flooded layer of the highly deviated well according to claim 1, wherein the iterative inversion based on the determined well track according to the logging data of the highly deviated well to be detected and the determined apparent resistivity of different detection depths is used to obtain the resistivity of the target interval of the highly deviated well to be detected, and the iterative inversion comprises:
preprocessing the logging data of the highly deviated well to be detected, and segmenting the preprocessed logging data;
acquiring logging data after the i-th section of preprocessing;
performing inversion by adopting a damping least square method based on the determined well track according to the logging data after the i-th section of preprocessing and the determined apparent resistivity of different detection depths to obtain the resistivity of the target interval of the highly-deviated well to be detected;
determining whether the inversion of all the sections is finished, and if so, outputting the resistivity; otherwise, acquiring the logging data after the i +1 th section of preprocessing and carrying out inversion processing.
7. The method for identifying the complicated lithologic water flooded layer of the highly deviated well as the claim 1, wherein the step of comparing the logging data of the highly deviated well to be detected and the target interval corresponding to the vertical well, the rock mineral component content, the porosity and the resistivity obtained by inverting the highly deviated well to be detected to identify the water flooded layer of the highly deviated well to be detected comprises the following steps:
when the content, the porosity and the resistivity of the rock mineral components of the target interval of the highly deviated well to be detected after inversion are smaller than the change of the content, the porosity and the resistivity of the rock mineral components corresponding to the target interval which is the same as that of the adjacent well vertical well, the complex lithology reservoir in the target interval is an oil layer;
and when the content, the porosity and the resistivity of the target interval of the highly deviated well to be detected after inversion and the change of the content, the porosity and the resistivity of the target interval of the highly deviated well before corresponding inversion are smaller than a preset threshold value, and the resistivity of the target interval of the highly deviated well to be detected after inversion is smaller than the resistivity of the same target interval of the vertical well of the adjacent well, the complex lithological reservoir in the target interval is a water flooded layer.
8. A system for identifying a complex lithologic water flooded layer of a highly deviated well is characterized by comprising:
the borehole trajectory determination module is used for acquiring well deviation data of each sampling point of the highly deviated well to be detected and determining the borehole trajectory of the highly deviated well;
the logging data measuring module is used for respectively measuring logging data of a target interval of a straight well in a highly deviated well to be measured and an adjacent well thereof, and calculating to obtain the rock mineral component content and the porosity of a complex lithologic reservoir in the highly deviated well and the straight well;
the apparent resistivity determining module is used for quantitatively analyzing the influence of various factors of the highly deviated well to be detected on the resistivities at different detection depths, establishing a resistivity forward model of the highly deviated well to be detected and determining the apparent resistivities at different detection depths;
the inversion module is used for carrying out iterative inversion to obtain the resistivity of the target interval of the highly deviated well to be detected according to the well logging data of the highly deviated well to be detected and the determined apparent resistivity of different detection depths on the basis of the determined well track;
and the identification module is used for comparing the logging data of the highly deviated well to be detected and the target interval of the corresponding vertical well, the rock mineral component content, the porosity and the resistivity obtained by inversion of the highly deviated well to be detected, and identifying the water flooded layer of the highly deviated well to be detected.
9. A processing device comprising computer program instructions, wherein the computer program instructions, when executed by the processing device, are adapted to implement the steps corresponding to the method for identifying complex lithologic water-flooded layers of highly deviated wells according to any of claims 1-7.
10. A computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions when executed by a processor are adapted to implement the corresponding steps of the complex lithologic water flooded layer identification method of highly deviated well according to any of claims 1-7.
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| CN117153289A (en) * | 2023-09-14 | 2023-12-01 | 大庆油田有限责任公司 | Reservoir flooding degree prediction method suitable for narrow and thin sand bodies |
| CN117153289B (en) * | 2023-09-14 | 2024-04-05 | 大庆油田有限责任公司 | Reservoir flooding degree prediction method suitable for narrow and thin sand bodies |
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