CN113655539A - Method for well logging constraint qualitative prediction of overflow phase volcanic plane distribution - Google Patents
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Abstract
The invention relates to the technical field of petroleum exploration and development, and discloses a method for well logging constraint qualitative prediction of overflow phase volcanic plane distribution, which comprises the following steps: acquiring logging data, horizon interpretation data and seismic data, and performing well-to-seismic calibration; establishing a forward modeling according to the logging data and the horizon interpretation data; performing lithology division on the overflow phase volcanic rock, and giving corresponding speed values and density values to different lithologies; performing qualitative forward modeling according to the forward modeling to obtain a forward modeling result; determining the corresponding relation between the lithology and the horizon of the overflow phase volcanic rock through well seismic calibration, and performing seismic waveform clustering calculation to obtain a waveform clustering result; and comparing the forward modeling result with the waveform clustering result to analyze the waveform characteristics of different lithologies and predict the boundary and plane distribution of the overflow phase volcanic rock. The invention introduces the logging data into one-dimensional forward modeling and waveform clustering calculation, restrains the overall overflow facies volcanic rock spreading, and improves the accuracy of boundary delineation.
Description
Technical Field
The invention relates to the technical field of petroleum exploration and development, in particular to a method for well logging constraint qualitative prediction of overflow phase volcanic plane distribution and a storage medium.
Background
The exploration results show that the volcanic rock deep layer is large in thickness, the physical properties of the volcanic rock are deeply buried, the influence of the volcanic rock is smaller than that of clastic rock, the volcanic rock is compact but possibly exists in a high-yield oil and gas reservoir, and the volcanic rock oil and gas reservoir becomes another important field of oil and gas exploration. The overflow phase volcanic rock is mainly formed in the middle and late stages of volcanic eruption stage, and is formed by rock pulp flowing along the ground surface and gradually condensing under the combined action of ejection material pushing and self gravity. The overflow phase volcanic rock is more stable than the outbreak phase volcanic rock, has larger thickness, and has more single and stable lithology in the larger thickness, so the overflow phase volcanic rock is a better oil gas storage space. At present, for the conditions of deep burial and few well drilling in volcanic rock exploration, the problems of high cost, high risk, long time consumption and the like exist in the process of identifying and predicting overflow phase volcanic rock through continuously increasing exploratory wells, and the requirements of regional evaluation and reserve calculation are difficult to meet.
For the research on the overflow phase volcanic rock, at present, the research on seismic reflection characteristics is mainly focused, and the overflow phase volcanic rock is identified by utilizing top-bottom interface reflection, internal reflection characteristics, horizontal slices and the like. The volcanic rock mass identification method based on the reflection characteristics of the conventional seismic profile and the planar characteristics of the time slice has feasibility, and by integrating the analysis and the drilling information, three lithofacies of the volcanic rock are established: overflow phase, volcanic channel phase, and explosive phase. The construction guide filtering, dip angle scanning, sound wave simulating reconstruction technology and characteristic parameter inversion are combined to complete the fine mapping of the volcanic entrance and the prediction of the volcanic reservoir. The combination of construction-oriented filtering and inversion is the most widely applied and relatively mature technology at present, but under the condition that only 1-2 exploratory wells exist in the early stage of exploration, the following problems exist:
1. the fault can be well described by constructing a result of the guiding filtering, but lithology cannot be reflected;
2. 1-2 wells are applied in the early stage of exploration to carry out wave impedance inversion to restrain integral volcanic spreading, so that uncertainty exists in volcanic boundary characterization;
3. the seismic reflection characteristics of the overflow phase volcanic rock and the compact mudstone are very similar, and the prediction result has multiple resolutions.
Disclosure of Invention
The invention aims to provide a method for predicting the planar distribution of overflow phase volcanic rocks in a well logging constrained and qualitative manner, so as to solve the problems of the boundary identification and the distribution prediction of the overflow phase volcanic rocks.
In order to achieve the above object, the present invention provides a method for well logging constraint qualitative prediction of overflow phase volcanic plane distribution, comprising:
acquiring logging data, horizon interpretation data and seismic data, and performing well-to-seismic calibration by using the acquired logging data and the seismic data;
establishing a forward model according to the logging data and the horizon interpretation data;
according to the logging data, lithology division is carried out on the overflow phase volcanic rock, and corresponding speed values and density values are given to different lithologies;
carrying out qualitative forward modeling according to the forward modeling model aiming at the speed values and the density values endowed by different lithologies to obtain a forward modeling result;
determining the corresponding relation between the lithology and the horizon of the overflow phase volcanic rock through the well seismic calibration, performing seismic waveform clustering calculation on the top and bottom horizons of the determined lithology between layers to obtain a waveform clustering result, and obtaining a seismic phase diagram according to the waveform clustering result;
and comparing the forward modeling result with the waveform clustering result to analyze the waveform characteristics of different lithologies, and predicting the boundary and the plane distribution of the overflow phase volcanic rock according to the seismic phase diagram.
Further, still include: and extracting the maximum amplitude seismic attribute between the top and bottom layer positions of the determined lithology, and comparing and analyzing the maximum amplitude seismic attribute with the predicted boundary and plane spread of the overflow phase volcanic, thereby qualitatively predicting the boundary and plane spread of the overflow phase volcanic.
Further, the well log data includes a gamma log, a resistivity log, a density log, and a sonic log.
Further, the acquiring logging data, horizon interpretation data and seismic data, and performing well-to-seismic calibration by using the acquired logging data and seismic data includes:
and obtaining logging data, horizon interpretation data and seismic data, and carrying out well-to-seismic calibration according to the seismic data and a density logging curve and an acoustic wave logging curve of the logging data.
Further, the lithology division of the overflow phase volcanic rock according to the logging data and corresponding speed values and density values given to different lithologies comprises:
and performing lithology division on the overflow phase volcanic rock according to a gamma logging curve and a resistivity logging curve of the logging data, and endowing corresponding speed values and density values to different lithologies.
Further, the qualitative forward modeling is performed according to the forward modeling model by using the speed values and the density values given for different lithologies to obtain a forward modeling result, and the method comprises the following steps:
carrying out qualitative forward modeling by applying a convolution formula according to the forward modeling model aiming at the speed values and the density values endowed by different lithologies to obtain a forward modeling result;
the convolution formula is: trace (t) rfdct (t) wave (t);
wherein rflct (t) is a reflection coefficient obtained by multiplying a velocity value and a density value given by different lithologies, wave (t) is wavelet data extracted by the seismic data, and trace (t) is a forward seismic trace, i.e. a forward result.
Further, the comparing the forward modeling result with the waveform clustering result and analyzing the waveform characteristics of different lithologies includes:
comparing the forward result with the waveform clustering result, and judging whether the error between the forward result and the waveform clustering result is within a preset error range;
if the error between the forward modeling result and the waveform clustering result exceeds the preset error range, adjusting the forward modeling model and performing qualitative forward modeling again;
and if the error between the forward result and the waveform clustering result is within the preset error range, outputting the forward result, and analyzing waveform characteristics of different lithologies according to the forward result.
Further, the predetermined error range is 5% -10%.
Further, before establishing a forward model according to the logging data and the horizon interpretation data, the method further includes:
carrying out standardization processing on the logging data; and carrying out interpolation and smoothing processing on the horizon interpretation data.
The invention also provides a storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method for well logging constraint qualitative prediction of overflow phase volcanic plane spread described above.
According to the technical scheme, the logging data are introduced into the one-dimensional forward modeling and waveform clustering calculation, and the overflow phase volcanic rock has the characteristic of being stratified, and the lithology of the overflow phase volcanic rock corresponds to the logging gamma curve, so that the seismic one-dimensional forward modeling is restrained by the logging gamma curve, the well seismic calibration is accurately carried out by the logging acoustic time difference and density curve, the waveform clustering calculation of the seismic data is carried out by the logging curve supervision, and the predicted plane layout is endowed with geological meaning. And finally, verifying the predicted plane spread by two auxiliary aspects of seismic profile facies and seismic attribute analysis, thereby qualitatively predicting the boundary and spread of the overflow facies volcanic rock and improving the accuracy of prediction. The technical scheme of the invention can be applied under the condition of 1-2 exploratory wells, can well restrict the overall distribution of the overflow phase volcanic rock, and improves the accuracy of the boundary depiction of the overflow phase volcanic rock
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a method for constrained qualitative prediction of overflow phase volcanic plane distribution by well logging according to an embodiment of the present invention;
FIG. 2 is a diagram of the actual effect of one-dimensional forward modeling of well curve participation provided by the embodiment of the invention; wherein, the left part of fig. 2 is a one-dimensional forward modeling model, and the right part of fig. 2 is a forward modeling result;
FIG. 3 is a classification chart of the result of the seismic waveform clustering calculation provided by the embodiment of the invention;
FIG. 4 is a seismic facies diagram provided by an embodiment of the present invention;
FIG. 5 is a graph of the maximum amplitude seismic attribute for interlayer extraction provided by an embodiment of the invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart of a method for well logging constraint qualitative prediction of overflow phase volcanic plane distribution according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides a method for well logging constraint qualitative prediction of overflow phase volcanic plane distribution, where the method includes:
s101, obtaining logging data, horizon interpretation data and seismic data, and carrying out well-to-seismic calibration by using the obtained logging data and the seismic data.
Specifically, the well log data includes a gamma log, a resistivity log, a density log, and a sonic log. And carrying out accurate well-seismic calibration through a density logging curve and an acoustic wave logging curve of logging data.
S102, establishing a forward model according to the logging data and the horizon interpretation data.
The well log data and the horizon interpretation data are first preprocessed before the forward model is built. Specifically, the well logging data is standardized to unify the well logging data standards, and the horizon interpretation data is interpolated and smoothed. In this embodiment, a one-dimensional forward modeling model is established according to the logging data and the horizon interpretation data, and the left part of fig. 2 is the one-dimensional forward modeling model.
S103, performing lithology division on the overflow phase volcanic rock according to the logging data, and giving corresponding speed values and density values to different lithologies.
Specifically, the lithology of the overflow phase volcanic rock is divided according to the gamma logging curve and the resistivity logging curve of the logging data, as shown in the left part of fig. 2, the lithology from top to bottom is respectively: diabase, andesite, sand mudstone, tuff, andesite, basalt, and endowing different lithologies with corresponding speed values and density values.
And S104, performing qualitative forward modeling according to the forward modeling model aiming at the speed values and the density values endowed by different lithologies to obtain a forward modeling result.
Specifically, qualitative forward modeling is carried out by applying a convolution formula according to the forward modeling model aiming at the speed values and density values endowed by different lithologies (diabase, andesite, sand shale, tuff and basalt).
The convolution formula is: trace (t) rfdct (t) wave (t);
wherein rflct (t) is reflection coefficient, wave (t) is wavelet data, trace (t) is forward seismic trace.
The qualitative forward process is as follows: multiplying the speed values and the density values endowed by different lithologies to obtain a product serving as a reflection coefficient rflct (t); extracting wavelet data wave (t) according to the seismic data; convolution calculation is carried out on the reflection coefficient rflct (t) and the wavelet data wave (t) to obtain a forward seismic trace (t), namely a forward result is obtained, and the forward result is shown in the right part of figure 2.
S105, determining the corresponding relation between the lithology and the horizon of the overflow phase volcanic rock through well seismic calibration, performing seismic waveform clustering calculation on the top and bottom horizons of the determined lithology between layers to obtain a waveform clustering result, and obtaining a seismic facies diagram according to the waveform clustering result.
Fig. 3 is a classification diagram of waveform clustering results, and the meaning of the waveforms in fig. 3 is defined by the waveform structure shown in the right part (forward result) of fig. 2. The waveform of the forward result in fig. 2 is the maximum negative amplitude, corresponding to andesite. Comparing the waveform characteristics of the forward result in fig. 2 with the waveform characteristics of the waveform clustering result in fig. 3, wherein the waveform of the forward result in fig. 2 corresponds to the waveforms of the deep red region and the red region in fig. 3, and the deep red region and the red region are defined as andesite. The seismic facies map (seismic facies plane distribution map) shown in FIG. 4 is obtained according to the different lithology in FIG. 3 corresponding to the different color regions.
S106, comparing the forward modeling result with the waveform clustering result to analyze waveform characteristics of different lithologies, and predicting the boundary and the plane distribution of the overflow phase volcanic rock according to the seismic phase diagram.
And comparing the forward result obtained in the step S104 with the waveform clustering result obtained in the step S105, and judging whether the error between the forward result and the waveform clustering result is within a preset error range. If the error between the forward modeling result and the waveform clustering result exceeds a preset error range, adjusting the forward modeling, and performing qualitative forward modeling again; and if the error between the forward result and the waveform clustering result is within a preset error range, outputting the forward result, and analyzing the waveform characteristics of different lithologies according to the forward result. The predetermined error range of the forward modeling result and the waveform clustering result is 5% -10%, in this embodiment, the predetermined error range is preferably 5%, and if the error between the waveform characteristics of the forward modeling result in fig. 2 and the waveform characteristics of the waveform clustering result shown in fig. 3 is greater than 5%, the forward modeling model is adjusted to perform qualitative forward modeling again; if the error is less than or equal to 5%, the forward result shown in fig. 2 is output. And analyzing the waveform characteristics of different lithologies according to the output forward modeling result, and predicting the boundary and the plane distribution of the overflow phase volcanic rock by combining the seismic phase diagram. For example: the target layer is top andesite, the positive result in fig. 2 is the maximum negative amplitude, the waveform of andesite in fig. 2 corresponds to the waveforms of deep red region and red region in fig. 3, the boundary and the plane distribution of overflow facies andesite are drawn in the seismic facies diagram according to the color corresponding to the waveform of andesite, and the deep red and red regions in fig. 4 represent the boundary and the plane distribution of overflow facies andesite (DB11 dotted line region).
S107, extracting the maximum amplitude seismic attributes between the top and bottom layer positions of the determined lithology, and comparing and analyzing the maximum amplitude seismic attributes with the predicted boundary and plane spread of the overflow phase volcanic, so as to qualitatively predict the boundary and plane spread of the overflow phase volcanic.
Maximum amplitude seismic attributes are extracted by determining the top and bottom horizons of lithology, as shown in FIG. 5. The boundary of the overflow phase volcanic rock is drawn on the seismic phase diagram shown in fig. 4 according to the boundary of dark red and red with other colors (dotted line), the boundary of the overflow phase volcanic rock in fig. 4 is projected on fig. 5 (such as DB11 area in the diagram), the similarity degree of the predicted boundary in fig. 4 and the boundary in fig. 5 (observing the color and brightness in the boundary area of DB 11) is analyzed in comparison, and the more similar the predicted boundary in fig. 4 and the boundary in fig. 5 shows that the accuracy of the predicted boundary is higher.
Embodiments of the present invention also provide a storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method for well logging constraint qualitative prediction of overflow phase volcanic plane distribution described above.
Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be implemented by a program, which is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
While the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications are within the scope of the embodiments of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, the embodiments of the present invention will not be described separately for the various possible combinations.
Claims (10)
1. A method for well logging constraint qualitative prediction of overflow phase volcanic plane distribution is characterized by comprising the following steps:
acquiring logging data, horizon interpretation data and seismic data, and performing well-to-seismic calibration by using the acquired logging data and the seismic data;
establishing a forward model according to the logging data and the horizon interpretation data;
according to the logging data, lithology division is carried out on the overflow phase volcanic rock, and corresponding speed values and density values are given to different lithologies;
carrying out qualitative forward modeling according to the forward modeling model aiming at the speed values and the density values endowed by different lithologies to obtain a forward modeling result;
determining the corresponding relation between the lithology and the horizon of the overflow phase volcanic rock through the well seismic calibration, performing seismic waveform clustering calculation on the top and bottom horizons of the determined lithology between layers to obtain a waveform clustering result, and obtaining a seismic phase diagram according to the waveform clustering result;
and comparing the forward modeling result with the waveform clustering result to analyze the waveform characteristics of different lithologies, and predicting the boundary and the plane distribution of the overflow phase volcanic rock according to the seismic phase diagram.
2. The method for constrained, qualitative prediction of overflow facies volcanic planform spread according to claim 1, further comprising:
and extracting the maximum amplitude seismic attribute between the top and bottom layer positions of the determined lithology, and comparing and analyzing the maximum amplitude seismic attribute with the predicted boundary and plane spread of the overflow phase volcanic, thereby qualitatively predicting the boundary and plane spread of the overflow phase volcanic.
3. The method of claim 1, wherein the well log data comprises gamma, resistivity, density and sonic logs.
4. The method for well logging constraint qualitative prediction of overflow facies volcanic plane spread according to claim 3, wherein the obtaining of well logging data, horizon interpretation data and seismic data and the use of the obtained well logging data and seismic data for well-to-seismic calibration comprises:
and obtaining logging data, horizon interpretation data and seismic data, and carrying out well-to-seismic calibration according to the seismic data and a density logging curve and an acoustic wave logging curve of the logging data.
5. The method for well logging constraint qualitative prediction of planar distribution of overflow phase volcanic rock according to claim 3, wherein said dividing lithology of overflow phase volcanic rock according to said well logging data and assigning corresponding speed value and density value to different lithologies comprises:
and performing lithology division on the overflow phase volcanic rock according to a gamma logging curve and a resistivity logging curve of the logging data, and endowing corresponding speed values and density values to different lithologies.
6. The method for well logging constraint qualitative prediction of the planar distribution of overflow facies volcanic rocks according to claim 1, wherein the qualitative forward modeling is performed according to the forward modeling model on the velocity values and the density values assigned to different lithologies to obtain a forward result, and the method comprises the following steps:
carrying out qualitative forward modeling by applying a convolution formula according to the forward modeling model aiming at the speed values and the density values endowed by different lithologies to obtain a forward modeling result;
the convolution formula is: trace (t) rfdct (t) wave (t);
wherein rflct (t) is a reflection coefficient obtained by multiplying a velocity value and a density value given by different lithologies, wave (t) is wavelet data extracted by the seismic data, and trace (t) is a forward seismic trace, i.e. a forward result.
7. The method for well logging constraint qualitative prediction of overflow facies volcanic rock plane spreading as claimed in claim 1, wherein the comparing the forward modeling result with the waveform clustering result to analyze waveform characteristics of different lithologies comprises:
comparing the forward result with the waveform clustering result, and judging whether the error between the forward result and the waveform clustering result is within a preset error range;
if the error between the forward modeling result and the waveform clustering result exceeds the preset error range, adjusting the forward modeling model and performing qualitative forward modeling again;
and if the error between the forward result and the waveform clustering result is within the preset error range, outputting the forward result, and analyzing waveform characteristics of different lithologies according to the forward result.
8. The method for well logging constrained qualitative prediction of overflow phase volcanic planform spread according to claim 7, wherein the predetermined error range is 5% -10%.
9. The method for well logging constraint qualitative prediction of overflow facies volcanic planform distribution according to claim 1, wherein before establishing a forward modeling according to the well logging data and the horizon interpretation data, the method further comprises:
carrying out standardization processing on the logging data;
and carrying out interpolation and smoothing processing on the horizon interpretation data.
10. A storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of well logging constrained qualitative prediction of overflow phase volcanic plane spread of any of claims 1 to 9.
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