CN109188520B - Thin reservoir thickness prediction method and device - Google Patents
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Abstract
The application discloses a thin reservoir thickness prediction method and a device, wherein the method comprises the following steps: carrying out synthetic record calibration on the acquired seismic data and logging data, and determining the position of a target layer; performing three-dimensional seismic horizon interpretation, and determining first time when seismic waves reach the top of a target layer and second time when the seismic waves reach the bottom of the target layer; determining a forward model of the thickness change of the thin reservoir according to the logging information, and determining the change condition of the seismic wave amplitude along with the thickness change of the thin reservoir; determining a forward model of the thickness change of the overlying strata of the target layer according to the logging information, and determining the rule that the amplitude of seismic waves changes along with the thickness change of the overlying strata; determining the stratum thickness of the overlying stratum according to seismic inversion and logging data; setting a time window and extracting the amplitude attribute of the target layer; correcting the amplitude attribute of the target layer according to the rule that the amplitude changes along with the thickness of the overlying stratum and the thickness of the stratum; and predicting the thickness of the thin reservoir according to the corrected amplitude attribute. The method and the device can improve the accuracy of thin reservoir thickness prediction.
Description
Technical Field
The application relates to the technical field of geophysical exploration of petroleum, in particular to a thin reservoir thickness prediction method and device.
Background
With the deepening of oil and gas exploration degree in China and the increasing of exploration difficulty, oil field exploration objects are mostly thin reservoirs with the thickness smaller than 1/4 seismic wave wavelength, so that the problem of how to determine the distribution and the thickness of the thin reservoirs also becomes an important research problem in the current seismic reservoir prediction. However, due to the limitation of seismic data resolution, the reflection of the thin reservoir on a seismic section is difficult to identify, and the seismic reflection characteristics of the thin reservoir are covered due to the reflection interference effect of the overlying strata, so that the thickness prediction of the thin reservoir is more difficult.
At present, the methods for predicting the thickness of the thin reservoir mainly comprise two types, one type is high-resolution inversion, such as model inversion, seismic statistics inversion, waveform characteristic indication inversion and the like, the methods start from a high-frequency model established among wells and combine seismic inversion to obtain a high-resolution lithologic section, but the method is adopted on the premise that a sufficient number of exploratory wells are arranged in a work area and the exploratory wells are distributed uniformly, so that a proper model can be established; the other type is to use seismic attributes to predict, such as amplitude attributes, frequency attributes, phase attributes and the like, the method is usually started from a forward model, a rule that various seismic attributes change along with the thickness of a reservoir is found out, then seismic data are analyzed by using the rule, and the thickness and distribution of a thin reservoir are predicted.
Taking the amplitude attribute as an example, since the thin reservoir cannot observe top and bottom reflection on the seismic profile, but can cause variation of the amplitude value, it is feasible to predict the thickness of the thin reservoir by using the lateral variation of the amplitude value, which is also a widely applied technology at present. However, factors causing the amplitude change include other factors besides the thin reservoir thickness, and the factors can interfere with the thin reservoir thickness prediction, so that the prediction result of the thin reservoir thickness is inaccurate.
Disclosure of Invention
The embodiment of the application provides a thin reservoir thickness prediction method, which is used for improving the accuracy of thin reservoir thickness prediction and comprises the following steps:
acquiring seismic data and logging data in a work area; performing synthetic record calibration on the seismic data and the logging data, and determining the position of a target layer; performing three-dimensional seismic horizon interpretation, and determining first time of seismic waves reaching the top of a target layer and second time of the seismic waves reaching the bottom of the target layer; determining a first forward model of the thickness change of the thin reservoir according to the logging information, and determining the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir; determining a second forward modeling of the thickness change of the overlying strata of the target layer according to the logging information, and determining the rule of the seismic wave amplitude along with the thickness change of the overlying strata of the target layer; determining the stratum thickness of the overlying stratum of the target layer according to the seismic inversion and the logging data; setting a time window according to the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir, the first time and the second time, and extracting the amplitude attribute of the target layer; correcting the amplitude attribute of the target layer according to the rule that the amplitude of the seismic wave of the target layer changes along with the thickness of the overlying stratum of the target layer and the thickness of the overlying stratum of the target layer; and predicting the thickness of the thin reservoir according to the corrected amplitude attribute of the target layer.
The embodiment of the present application further provides a thin reservoir thickness prediction apparatus, which is used to improve the accuracy of thin reservoir thickness prediction, and the apparatus includes:
the acquisition module is used for acquiring seismic data and logging data in a work area; the determining module is used for performing synthetic record calibration on the seismic data and the logging data acquired by the acquiring module and determining the position of a target layer; the determining module is also used for performing three-dimensional seismic horizon interpretation, and determining first time when the seismic waves reach the top of the target layer and second time when the seismic waves reach the bottom of the target layer; the determining module is further used for determining a first forward model of the thickness change of the thin reservoir according to the logging information and determining the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir; the determining module is further used for determining a second forward modeling of the thickness change of the overlying strata of the target layer according to the logging information and determining the rule of the seismic wave amplitude along with the thickness change of the overlying strata of the target layer; the determining module is further used for determining the stratum thickness of the overlying stratum of the target layer according to the seismic inversion and the logging information; the extraction module is used for setting a time window according to the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir, the first time and the second time and extracting the amplitude attribute of the target layer; the correction module is used for correcting the amplitude attribute of the target layer extracted by the extraction module according to the rule that the amplitude of the seismic wave of the target layer determined by the determination module changes along with the thickness of the overlying stratum of the target layer and the thickness of the overlying stratum of the target layer; and the prediction module is used for predicting the thickness of the thin reservoir according to the amplitude attribute of the target layer corrected by the correction module.
According to the thin reservoir thickness prediction method and device provided by the embodiment of the application, the influence of the thickness of the overlying stratum of the target layer on the amplitude attribute of the target layer is determined through the seismic data and the logging data in the work area, the amplitude attribute of the target layer is corrected according to the influence, the influence of the thickness of the overlying stratum on the amplitude attribute of the target layer is eliminated, and therefore the accuracy of predicting the thin reservoir thickness by using the amplitude attribute of the target layer is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
fig. 1 is a flow chart of a thin reservoir thickness prediction method provided by an embodiment of the present application;
FIG. 2 is a schematic diagram of a synthetic record best matched to a borehole seismic event as provided by an embodiment of the present application;
FIG. 3 is a schematic diagram of a first forward model provided by an embodiment of the present application;
FIG. 4 is a schematic illustration of a first seismic trace section provided by an embodiment of the present application;
FIG. 5 is a diagram of a second forward model provided by an embodiment of the present application;
FIG. 6 is a schematic illustration of a second seismic trace section provided by an embodiment of the present application;
FIG. 7 is a schematic view of a fitted curve of thickness of overburden and seismic wave amplitude provided in accordance with an embodiment of the present application;
fig. 8 is a block diagram of a thin reservoir thickness prediction apparatus according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application more apparent, the embodiments of the present application are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present application are provided herein to explain the present application and not to limit the present application.
The application provides a thin reservoir thickness prediction method which is applied to computer equipment such as a computer and a mobile phone with data analysis and processing functions. As shown in fig. 1, the method includes steps 101 to 106:
Seismic data is typically a three-dimensional post-stack seismic data volume that is acquired at the surface and subsequently processed. Optionally, a seismic source capable of emitting seismic waves and a receiving device for reflected wave signals of the seismic waves may be arranged on the earth surface, and when the seismic source is excited to generate seismic waves, the seismic waves propagate deep into an underground rock stratum and encounter a medium interface with different elasticity, wave reflection is generated; the reflected wave signal is received by a receiving device, and the characteristics of the reflected wave signal, such as time, frequency, amplitude and the like, are further analyzed to obtain the characteristic information of the underground rock stratum, and the characteristic information of the underground rock stratum can be used as seismic data.
The logging data comprises a sound wave time difference curve, a density curve, a gamma curve, a resistivity curve and the like, and is acquired from a deployed exploratory well in a work area.
And 102, performing synthetic record calibration on the seismic data and the logging data, and determining the position of a target layer.
Optionally, the acoustic wave time difference curve included in the logging data may be determined, the velocity attribute of the acoustic wave curve is multiplied by the density attribute of the point with the same depth on the density curve to obtain a wave impedance curve, wavelets are extracted from the seismic data, and convolution operation is performed on the wavelets and the wave impedance curve to obtain a synthetic record.
It should be noted that the acoustic moveout curve is a curve for describing the reciprocal of the seismic wave velocity at different depth points in the subsurface, and the acoustic curve is a curve for describing the seismic wave velocity at different depth points in the subsurface, so that the acoustic curve can be obtained by taking the reciprocal of the numerical value on the acoustic moveout curve.
The density curve is used for describing the density of different depth points, the density on the density curve is multiplied by the speed of the same depth point on the sound wave curve to obtain the wave impedance value of the depth point, the depth point is taken as a vertical coordinate, the wave impedance value is taken as a horizontal coordinate, and the wave impedance values of the different depth points are mapped on a coordinate axis to obtain the wave impedance curve.
Wavelets are a component of the convolution model of seismic records, and generally refer to seismic impulses consisting of 2 to 3 or more phases, and extracting wavelets from seismic data is a well-established technical means, and for this process, it is not described herein again. In addition, convolution operation is also a mature technical means, and the operation process of convolution operation on wavelets and wave impedance curves is not repeated.
Optionally, the well-side earthquake and the synthetic record in the seismic data may be optimally matched according to the principle that the wave crest corresponds to the wave crest and the wave trough corresponds to the wave trough, so as to determine the time-depth relationship and the position of the target layer.
For example, referring to fig. 2, fig. 2 shows a more ideal matching result, the peak and the trough of the synthetic record and the earthquake beside the well are relatively matched, but due to the extremely complicated condition of the underground rock stratum, in many cases, the peak and the trough of the synthetic record and the earthquake beside the well cannot be completely corresponding, and at this time, as many peaks and troughs as possible can be respectively corresponding to achieve the best matching.
And 103, performing three-dimensional seismic horizon interpretation, and determining the first time when the seismic waves reach the top of the target layer and the second time when the seismic waves reach the bottom of the target layer.
Optionally, the first time is denoted as T1And the second time is denoted as T2。
And step 104, determining a first forward modeling model of the thickness change of the thin reservoir according to the logging information, and determining the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir.
Optionally, the logging information includes characteristics of the stratum at the deployed position of the exploratory well, such as thicknesses of various stratums, so that the thickness range of the thin reservoir in the work area can be determined according to the logging information: and establishing a first forward model of the thickness change of the thin reservoir according to the thickness range of the thin reservoir, wherein in the first forward model, the thickness of the thin reservoir gradually changes, and the thicknesses of other rock stratums do not change.
Illustratively, according to the logging information, determining that the overlying stratum of the target layer is a set of argillaceous nephrite, and the argillaceous nephrite has a low impedance characteristic; the whole target layer is high-impedance dense dolomite, and a medium-low impedance pore dolomite reservoir layer develops at the top of the target layer. Based on the above-mentioned formation characteristics, a first forward model as shown in fig. 3 can be established.
Optionally, after the first forward modeling is established, the wavelet extracted from the seismic data and the first forward modeling may be subjected to convolution operation to generate the first seismic trace section. And determining the change condition of the seismic wave amplitude along with the change of the thickness of the thin reservoir according to the first seismic channel section. For example, the generated first seismic trace profile is shown in fig. 4, and it is obvious from the seismic profile that when the reservoir thickness is 0 m, the amplitude of the top of the target layer is strong, and gradually weakens as the reservoir thickness increases, so that the amplitude of the top of the target layer can reflect the change of the reservoir thickness.
And 105, determining a forward model of the thickness change of the overlying strata of the target layer according to the logging information, and determining the rule of the amplitude of the seismic waves changing along with the thickness change of the overlying strata of the target layer.
Optionally, determining a forward model of the thickness change of the overburden stratum of the target layer according to the logging information, and determining the rule that the amplitude of the seismic wave changes along with the thickness change of the overburden stratum of the target layer, including determining the thickness range of the overburden stratum of the target layer in the work area according to the logging information: and establishing a second forward modeling of the target layer overburden thickness variation according to the overburden thickness range, wherein in the second forward modeling, the overburden thickness gradually varies, and the thicknesses of other rock stratums do not vary.
Illustratively, according to the logging information, it is determined that a certain change exists in the thickness of the argillaceous nephrite of the overburden of the target stratum, the change range is 15 meters to 35 meters, and based on the change, a second forward model is established, wherein the thickness of the overburden is 15 meters to 35 meters, and the thickness of other stratums is not changed, and the second forward model is shown in fig. 5.
Optionally, after the second forward modeling is established, the wavelet extracted from the seismic data and the second forward modeling may be subjected to convolution operation to generate a second seismic trace section. And then, determining the rule that the seismic wave amplitude changes along with the thickness of the overlying strata of the target layer according to the second seismic channel section. For example, the generated second seismic trace profile is shown in fig. 6, and it is obvious from the seismic trace profile that the amplitude of the top of the target layer gradually weakens as the thickness of the argillaceous cloud rock gradually decreases, and it can be seen that the change in the thickness of the overlying strata of the target layer causes the amplitude of the seismic wave at the top of the target layer to change.
Optionally, after determining the change condition, obtaining thicknesses of overburden formations of at least two target layers and corresponding seismic wave amplitude values from the second seismic channel profile; mapping the thicknesses of the overburden strata of the at least two target layers and the corresponding seismic wave amplitudes acquired from the second seismic channel section to coordinate axes as basic points by taking the thickness of the overburden strata of the target layer as an abscissa and the seismic wave amplitude value of the target layer as an ordinate; and determining a fitting curve of the thickness of the overburden stratum of the target layer and the seismic wave amplitude by using the basic points, and determining an expression of the fitting curve as a rule that the seismic wave amplitude changes along with the thickness of the overburden stratum of the target layer.
Illustratively, taking an overburden of a target layer as argillaceous cloud rock as an example, the thickness of the argillaceous cloud rock is an abscissa, the amplitude of seismic waves propagating in the argillaceous cloud rock is an ordinate, the obtained plurality of basic points are mapped onto coordinate axes to obtain "actual data" shown in fig. 7, a "fitting curve" shown in fig. 7 is determined according to the actual data, as can be seen from the figure, the difference between the fitting curve and the actual data is small, and the accuracy of the fitting curve is high. Obviously, in this documentIn the example, the fitted curve resembles a parabola, and thus, in the mathematical form of parabola, f (h) ═ aH2+ bH + c represents the fitted curve, where H is the thickness of the overlying argillaceous nephrite, F (H) is the amplitude value at the top of the thin reservoir, and a, b, and c are coefficients. After determining the mathematical form of the fitted curve, the coefficients a, b, and c in the above mathematical form may be calculated using actual data, illustratively, a is-0.259, b is 4.816, and c is 1843, and the expression f (H) is-0.259H2+4.816H+1843。
It should be noted that the first forward model and the second forward model that are established need to conform to geological rules, otherwise, they cannot be applied to practice.
And step 106, determining the stratum thickness of the overlying stratum of the target layer according to the seismic inversion and the logging data.
Alternatively, seismic inversion may be performed using the seismic data to pick up the time at which the seismic waves reach the top surface and the time at which the seismic waves reach the bottom surface of the overburden. The seismic inversion method comprises constrained sparse impulse inversion, push-to-earth inversion or logging constrained inversion and the like, and can be selected according to the actual situation of a work area, and is not limited herein. After the time of arrival of the seismic wave at the top surface and the time of arrival of the seismic wave at the bottom surface of the overburden are obtained, the difference between the time of arrival of the seismic wave at the bottom surface and the time of arrival at the top surface of the overburden can be determined as the time thickness of the overburden. Optionally, the time when the seismic wave reaches the top surface and the time when the seismic wave reaches the bottom surface of the overburden stratum can be respectively recorded as N1、N2The time thickness N of the overburden may be expressed as N ═ N2-N1. Then, determining the propagation speed of seismic waves in the overlying strata according to the logging information; and according to the formula
And calculating the stratum thickness H of the overburden stratum, wherein V is used for representing the propagation speed of the seismic waves in the overburden stratum, and N is used for representing the time thickness of the overburden stratum, so that the actual stratum thickness of the overburden stratum is determined through the time thickness of the overburden stratum.
And step 107, setting a time window according to the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir, the first time and the second time, and extracting the amplitude attribute of the target layer.
In the embodiment of the present application, the root mean square amplitude attribute of the top of the destination layer is extracted within a set time window, which may be set to [ T [ ]1-t，T1+ T ] or may be [ T ]2-t，T2+ T ], wherein T1For the first time, T, that the seismic waves reach the top of the destination layer2And a second time when the seismic waves reach the bottom of the destination layer, wherein the first time or the second time can be selected to set the time window by referring to the first forward modeling obtained in the step 104. Illustratively, referring to fig. 4, the peak corresponding to the top of the destination layer has a larger variation with the thickness of the thin reservoir, and the valley corresponding to the bottom of the destination layer has a smaller variation with the thickness of the thin reservoir, so in the embodiment of the present application, the first time T is selected1To set the time window.
t can be set according to the principle that the time window comprises the peak reflection at the top of the target layer and by combining with the actual stratum characteristics. Optionally, in the embodiment of the present application, t may be 10 milliseconds.
And 108, correcting the amplitude attribute of the target layer according to the rule that the amplitude of the seismic wave of the target layer changes along with the thickness of the overburden stratum of the target layer and the thickness of the overburden stratum of the target layer.
Optionally, the amplitude may be changed according to the rule of the change of the amplitude with the thickness of the overburden, according to the formula Ar(i,j)＝At(i,j)-F(H(i,j)) + C corrects the destination layer amplitude property, where Ar(i,j)A correction result for the layer amplitude attribute of the ith track of the ith line representing the desired output; a. thet(i,j)The layer amplitude attribute is used for representing the ith line and the jth track; f (H)(i,j)) Expression for representing a fitted curve, H(i,j)And C is a constant, and the values of i and j are positive integers.
And step 109, predicting the thickness of the thin reservoir according to the corrected amplitude attribute of the target layer.
It should be noted that, predicting the thin reservoir thickness according to the amplitude attribute of the target layer is a mature method, and details of the specific process are not described herein.
According to the method and the device, the influence of the thickness of the overlying stratum of the target layer on the amplitude attribute of the target layer is determined through the seismic data and the logging data in the work area, the amplitude attribute of the target layer is corrected according to the influence, the influence of the thickness of the overlying stratum on the amplitude attribute of the target layer is eliminated, and therefore the accuracy of predicting the thickness of the thin reservoir layer by using the amplitude attribute of the target layer is improved.
The present application also provides an apparatus for predicting thin reservoir thickness, as shown in fig. 8, the apparatus 800 includes an obtaining module 801, a determining module 802, an extracting module 803, a correcting module 804, and a predicting module 805, wherein,
the obtaining module 801 is configured to obtain seismic data and logging data in a work area.
And the determining module 802 is configured to perform synthetic record calibration on the seismic data and the well logging data acquired by the acquiring module 801, and determine a position of a target layer.
The determining module 802 is further configured to perform three-dimensional seismic horizon interpretation, and determine a first time when the seismic waves reach the top of the destination layer and a second time when the seismic waves reach the bottom of the destination layer.
The determining module 802 is further configured to determine a first forward model of the thin reservoir thickness variation according to the logging data, and determine a variation situation of the target layer seismic wave amplitude along with the thin reservoir thickness variation.
The determining module 802 is further configured to determine a second forward model of the thickness variation of the overburden of the target layer according to the logging data, and determine a rule that the amplitude of the seismic wave varies with the thickness variation of the overburden of the target layer.
The determining module 802 is further configured to determine a thickness of a formation overlying the target layer according to the seismic inversion and the logging data.
The extracting module 803 is configured to set a time window according to the variation of the seismic wave amplitude of the target interval along with the thickness of the thin reservoir, the first time and the second time, and extract the amplitude attribute of the target interval.
And the correcting module 804 is configured to correct the amplitude attribute of the target layer extracted by the extracting module 803 according to the rule that the amplitude of the seismic wave of the target layer determined by the determining module 802 changes with the thickness of the overburden stratum of the target layer and the thickness of the overburden stratum of the target layer.
And the prediction module 805 is used for predicting the thin reservoir thickness according to the target layer amplitude attribute corrected by the correction module 804.
Optionally, the determining module 802 is configured to:
determining the thickness range of a thin reservoir in a work area according to the logging information; according to the thickness range of the thin reservoir, a first forward model of the thickness change of the thin reservoir is established, in the first forward model, the thickness of the thin reservoir gradually changes, and the thicknesses of other rock stratums do not change; performing convolution operation on wavelets extracted from the seismic data and the first forward modeling model to generate a first seismic channel section; and determining the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir according to the first seismic channel section.
Optionally, the determining module 802 is configured to:
determining the thickness range of the overlying strata of the target layer in the work area according to the logging information: according to the thickness range of the overburden stratum of the target layer, a second forward model of the thickness change of the overburden stratum of the target layer is established, in the second forward model, the thickness of the overburden stratum of the target layer gradually changes, and the thicknesses of other rock stratums do not change; performing convolution operation on the wavelets extracted from the seismic data and the second forward modeling model to generate a second seismic channel section; and determining the rule that the seismic wave amplitude of the target layer changes along with the thickness of the overlying strata of the target layer according to the second seismic channel section.
Optionally, the determining module 802 is configured to:
acquiring the thicknesses of overlying strata of at least two target layers and corresponding seismic wave amplitude values from the second seismic channel section;
mapping the thicknesses of the overburden strata of the at least two target layers and the corresponding seismic wave amplitudes acquired from the second seismic channel section to coordinate axes as basic points by taking the thickness of the overburden strata of the target layer as an abscissa and the seismic wave amplitude value of the target layer as an ordinate;
and determining a fitting curve of the thickness of the overburden stratum of the target layer and the seismic wave amplitude by using the basic points, and determining an expression of the fitting curve as a rule that the seismic wave amplitude changes along with the thickness of the overburden stratum of the target layer.
Optionally, the determining module 802 is configured to:
carrying out seismic inversion, and picking up the time of seismic waves reaching the top surface and the time of the bottom surface of an overlying stratum of a target layer; determining the difference value of the time of the seismic waves reaching the bottom surface and the time of the top surface of the overburden stratum of the target layer as the time thickness of the overburden stratum of the target layer; determining the propagation speed of seismic waves in an overlying stratum of a target layer according to the logging information; according to the formula
And calculating the stratum thickness H of the overburden of the target layer, wherein V is used for representing the propagation speed of the seismic waves in the overburden of the target layer, and N is used for representing the time thickness of the overburden of the target layer.
Optionally, the correcting module 804 is configured to:
according to formula Ar(i,j)＝At(i,j)-F(H(i,j)) + C corrects the destination layer amplitude property, where Ar(i,j)A correction result for the layer amplitude attribute of the ith track of the ith line representing the desired output; a. thet(i,j)The layer amplitude attribute is used for representing the ith line and the jth track; f (H)(i,j)) Expression for representing a fitted curve, H(i,j)And C is a constant, and the values of i and j are positive integers.
According to the method and the device, the influence of the thickness of the overlying stratum of the target layer on the amplitude attribute of the target layer is determined through the seismic data and the logging data in the work area, the amplitude attribute of the target layer is corrected according to the influence, the influence of the thickness of the overlying stratum on the amplitude attribute of the target layer is eliminated, and therefore the accuracy of predicting the thickness of the thin reservoir layer by using the amplitude attribute of the target layer is improved.
The embodiment of the present application further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method described in step 101 to step 109 when executing the computer program.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program for executing the method described in step 101 to step 109 is stored in the computer-readable storage medium.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are further described in detail for the purpose of illustrating the invention, and it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (15)
1. A method for thin reservoir thickness prediction, comprising:
acquiring seismic data and logging data in a work area;
performing synthetic record calibration on the seismic data and the logging data, and determining the position of a target layer;
performing three-dimensional seismic horizon interpretation, and determining first time of seismic waves reaching the top of a target layer and second time of the seismic waves reaching the bottom of the target layer;
determining a first forward model of the thickness change of the thin reservoir according to the logging information, and determining the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir;
determining a second forward modeling of the thickness change of the overlying strata of the target layer according to the logging information, and determining the rule of the seismic wave amplitude along with the thickness change of the overlying strata of the target layer;
determining the stratum thickness of the overlying stratum of the target layer according to the seismic inversion and the logging data;
setting a time window according to the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir, the first time and the second time, and extracting the amplitude attribute of the target layer;
correcting the amplitude attribute of the target layer according to the rule that the amplitude of the seismic wave of the target layer changes along with the thickness of the overlying stratum of the target layer and the thickness of the overlying stratum of the target layer;
and predicting the thickness of the thin reservoir according to the corrected amplitude attribute of the target layer.
2. The method of claim 1, wherein the well log data comprises sonic moveout curves and density curves;
the synthetic record calibration of the seismic data and the logging data is carried out, and the position of the target layer is determined, and the synthetic record calibration comprises the following steps:
determining a sound wave curve according to the sound wave time difference curve;
multiplying the speed attribute of the acoustic curve and the density attribute of the same depth point on the density curve to obtain a wave impedance curve;
extracting wavelets from the seismic data, and performing convolution operation on the wavelets and the wave impedance curve to obtain a synthetic record;
and optimally matching the well side earthquake and the synthetic record in the earthquake data according to the principle that the wave crest corresponds to the wave crest and the wave trough corresponds to the wave trough, and determining the time-depth relation and the position of the target layer.
3. The method of claim 1, wherein determining the first forward model of the thin reservoir thickness variation from the well log data and determining the variation of the seismic wave amplitude of the target zone with the thin reservoir thickness variation comprises:
determining the thickness range of the thin reservoir in the work area according to the logging information:
according to the thickness range of the thin reservoir, establishing a first forward model of the thickness change of the thin reservoir, wherein in the first forward model, the thickness of the thin reservoir gradually changes, and the thicknesses of other rock stratums do not change;
performing convolution operation on wavelets extracted from the seismic data and the first forward modeling model to generate a first seismic channel section;
and determining the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir according to the first seismic channel section.
4. The method of claim 1, wherein determining a second forward model of the change in thickness of the overburden at the destination from the well log data and determining the regularity of the amplitude of the seismic waves as a function of the thickness of the overburden at the destination comprises:
determining the thickness range of the overlying strata of the target layer in the work area according to the logging information:
according to the thickness range of the overburden stratum of the target layer, a second forward model of the thickness change of the overburden stratum of the target layer is established, in the second forward model, the thickness of the overburden stratum of the target layer gradually changes, and the thicknesses of other rock layers do not change;
performing convolution operation on the wavelets extracted from the seismic data and the second forward modeling model to generate a second seismic channel section;
and determining the rule that the amplitude of the seismic wave changes along with the thickness of the overlying strata of the target layer according to the second seismic channel section.
5. The method of claim 4, wherein determining the law of seismic wave amplitude as a function of thickness of overburden at the destination from the second seismic trace section comprises:
acquiring the thicknesses of overlying strata of at least two target layers and corresponding seismic wave amplitude values from the second seismic channel section;
mapping the thicknesses of the overburden strata of the at least two target layers and the corresponding seismic wave amplitudes acquired from the second seismic channel section to coordinate axes as basic points by taking the thickness of the overburden strata of the target layer as an abscissa and the seismic wave amplitude value of the target layer as an ordinate;
and determining a fitting curve of the thickness of the overburden stratum of the target layer and the seismic wave amplitude by using the basic points, and determining an expression of the fitting curve as a rule that the seismic wave amplitude changes along with the thickness of the overburden stratum of the target layer.
6. The method of claim 1, wherein determining the formation thickness of the overburden at the target formation from the seismic inversion and log data comprises:
carrying out seismic inversion, and picking up the time of seismic waves reaching the top surface and the time of the bottom surface of an overlying stratum of a target layer;
determining the difference value of the time of the seismic waves reaching the bottom surface and the time of the top surface of the overburden stratum of the target layer as the time thickness of the overburden stratum of the target layer;
determining the propagation speed of seismic waves in an overlying stratum of a target layer according to the logging information;
according to the formula
And calculating the stratum thickness H of the overburden of the target layer, wherein V is used for representing the propagation speed of the seismic waves in the overburden of the target layer, and N is used for representing the time thickness of the overburden of the target layer.
7. The method of claim 5 or 6, wherein correcting the target formation amplitude attribute based on the law of target formation seismic amplitude as a function of target formation overburden thickness and target formation overburden thickness comprises:
according to formula Ar(i,j)＝At(i,j)-F(H(i,j)) + C corrects the destination layer amplitude property, where Ar(i,j)A correction result for the layer amplitude attribute of the ith track of the ith line representing the desired output; a. thet(i,j)The layer amplitude attribute is used for representing the ith line and the jth track; f (H)(i,j)) Expression for representing a fitted curve, H(i,j)And C is a constant, and the values of i and j are positive integers.
8. A thin reservoir thickness prediction apparatus, comprising:
the acquisition module is used for acquiring seismic data and logging data in a work area;
the determining module is used for performing synthetic record calibration on the seismic data and the logging data acquired by the acquiring module and determining the position of a target layer;
the determining module is also used for performing three-dimensional seismic horizon interpretation, and determining first time when the seismic waves reach the top of the target layer and second time when the seismic waves reach the bottom of the target layer;
the determining module is further used for determining a first forward model of the thickness change of the thin reservoir according to the logging information and determining the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir;
the determining module is further used for determining a second forward modeling of the thickness change of the overlying strata of the target layer according to the logging information and determining the rule of the seismic wave amplitude along with the thickness change of the overlying strata of the target layer;
the determining module is further used for determining the stratum thickness of the overlying stratum of the target layer according to the seismic inversion and the logging information;
the extraction module is used for setting a time window according to the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir, the first time and the second time and extracting the amplitude attribute of the target layer;
the correction module is used for correcting the amplitude attribute of the target layer extracted by the extraction module according to the rule that the amplitude of the seismic wave of the target layer determined by the determination module changes along with the thickness of the overlying stratum of the target layer and the thickness of the overlying stratum of the target layer;
and the prediction module is used for predicting the thickness of the thin reservoir according to the amplitude attribute of the target layer corrected by the correction module.
9. The apparatus of claim 8, wherein the means for determining is configured to:
determining the thickness range of the thin reservoir in the work area according to the logging information:
according to the thickness range of the thin reservoir, establishing a first forward model of the thickness change of the thin reservoir, wherein in the first forward model, the thickness of the thin reservoir gradually changes, and the thicknesses of other rock stratums do not change;
performing convolution operation on wavelets extracted from the seismic data and the first forward modeling model to generate a first seismic channel section;
and determining the change condition of the seismic wave amplitude of the target layer along with the thickness change of the thin reservoir according to the first seismic channel section.
10. The apparatus of claim 8, wherein the means for determining is configured to:
determining the thickness range of the overlying strata of the target layer in the work area according to the logging information:
according to the thickness range of the overburden stratum of the target layer, a second forward model of the thickness change of the overburden stratum of the target layer is established, in the second forward model, the thickness of the overburden stratum of the target layer gradually changes, and the thicknesses of other rock layers do not change;
performing convolution operation on the wavelets extracted from the seismic data and the second forward modeling model to generate a second seismic channel section;
and determining the rule that the amplitude of the seismic wave changes along with the thickness of the overlying strata of the target layer according to the second seismic channel section.
11. The apparatus of claim 10, wherein the means for determining is configured to:
acquiring the thicknesses of overlying strata of at least two target layers and corresponding seismic wave amplitude values from the second seismic channel section;
mapping the thicknesses of the overburden strata of the at least two target layers and the corresponding seismic wave amplitudes acquired from the second seismic channel section to coordinate axes as basic points by taking the thickness of the overburden strata of the target layer as an abscissa and the seismic wave amplitude value of the target layer as an ordinate;
and determining a fitting curve of the thickness of the overburden stratum of the target layer and the seismic wave amplitude by using the basic points, and determining an expression of the fitting curve as a rule that the seismic wave amplitude changes along with the thickness of the overburden stratum of the target layer.
12. The apparatus of claim 8, wherein the means for determining is configured to:
carrying out seismic inversion, and picking up the time of seismic waves reaching the top surface and the time of the bottom surface of an overlying stratum of a target layer;
determining the difference value of the time of the seismic waves reaching the bottom surface and the time of the top surface of the overburden stratum of the target layer as the time thickness of the overburden stratum of the target layer;
determining the propagation speed of seismic waves in an overlying stratum of a target layer according to the logging information;
according to the formula
And calculating the stratum thickness H of the overburden of the target layer, wherein V is used for representing the propagation speed of the seismic waves in the overburden of the target layer, and N is used for representing the time thickness of the overburden of the target layer.
13. The apparatus of claim 11 or 12, wherein the correction module is configured to:
according to formula Ar(i,j)＝At(i,j)-F(H(i,j)) + C corrects the destination layer amplitude property, where Ar(i,j)A correction result for the layer amplitude attribute of the ith track of the ith line representing the desired output; a. thet(i,j)The layer amplitude attribute is used for representing the ith line and the jth track; f (H)(i,j)) Expression for representing a fitted curve, H(i,j)And C is a constant, and the values of i and j are positive integers.
14. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 7 when executing the computer program.
15. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 7.
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| CN112346116A (en) * | 2019-08-09 | 2021-02-09 | 中国石油天然气集团有限公司 | Reservoir stratum prediction method and device |
| CN111158048B (en) * | 2020-01-04 | 2022-10-04 | 杨林海 | Analysis method for improving reservoir prediction precision through seismic waveform envelope interpretation |
| CN113376690B (en) * | 2020-03-09 | 2023-09-26 | 中国石油天然气股份有限公司 | Reservoir parameter prediction method and system |
| CN111581890A (en) * | 2020-05-27 | 2020-08-25 | 中国石油大学(北京) | Reservoir thickness prediction method, device, equipment and storage medium |
| CN112269212A (en) * | 2020-10-20 | 2021-01-26 | 中国石油天然气集团有限公司 | Method, device, equipment and medium for determining seismic interpretation horizon of small logging layering |
| CN114428322A (en) * | 2020-10-29 | 2022-05-03 | 中国石油天然气股份有限公司 | Method and device for predicting thickness of thin reservoir based on frequency attribute |
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