CN111580158A - Prediction method for internal permeability resistance band of sandstone reservoir - Google Patents
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Abstract
The invention discloses a method for predicting a permeability resistance band in a sandstone reservoir, which comprises the following steps: evaluating the quality of the seismic data; denoising the seismic data; calculating the local structure entropy of the three-dimensional data volume; calibrating a prediction result; predicting the spreading of the permeation resistant strip; and (3) well position deployment and optimization in actual production of the oil field. The sectional view and the plan view are obtained by adopting the calculation of the local structure entropy algorithm, the difficulty that the scale of the internal permeability resistance band of the sandstone reservoir is usually smaller than the scale range of the resolution capability of seismic data and the internal permeability resistance band of the sandstone reservoir is difficult to accurately identify by the conventional seismic inversion or reservoir prediction means is overcome, and the problem that the internal permeability resistance band of the sandstone reservoir cannot be predicted is solved.
Description
Technical Field
The invention relates to the technical field of oil field development, in particular to a prediction method of a permeability resistance band in a sandstone reservoir.
Background
The problems of interlayer interference, single-layer outburst, injection and production imbalance and the like in the oilfield development process are related to the heterogeneity of the oil and gas reservoir, so that the heterogeneity of the oil and gas reservoir is one of important factors for restricting the oilfield development effect. Impermeable or low permeable zones within a hydrocarbon reservoir that can affect the flow, migration, or accumulation of fluids are called barrier zones, which are one of the important factors contributing to reservoir heterogeneity. Sandstone reservoirs are one of the most common oil and gas reservoir types, and more than 80 percent of oil and gas reservoirs in China are sandstone reservoirs. The lithology and physical property changes of an oil and gas reservoir and the internal anti-seepage strip of the sandstone reservoir formed by small faults due to the deposition effect, the diagenesis effect, the tectonic movement and the like generate different degrees of shielding effect on fluid seepage, and the anti-seepage strip is an important reason for ineffective injection and production, low reserve production degree and partial residual oil enrichment in oil field development.
In seismic signal processing, a Local structural Entropy algorithm (LSE for short) reflects the non-linearity of signals between channels (refer to the application of Local structural Entropy in seismic data discontinuity detection in article 1 of 35 volume 2007 of journal of coal field geology and exploration). The method is applied to the research of the correlation problem of seismic data, but the method is not applied to the prediction of the distribution of the internal permeability resistance bands of the sandstone.
The internal permeability-resistant strip of the sandstone reservoir has important significance in guiding the design and construction of a water injection well and a production well, and is important data for oil and gas development. However, because the dimension of the internal permeability resistance band of the sandstone reservoir is usually smaller than the size range of the resolution capability of seismic data, the internal permeability resistance band of the sandstone reservoir is difficult to accurately identify by the conventional seismic inversion or reservoir prediction means, and the internal permeability resistance band of the sandstone reservoir cannot be accurately predicted at present.
Disclosure of Invention
The invention provides a prediction method of an internal permeability resistance band of a sandstone reservoir, which is used for solving the problem that the internal permeability resistance band of the sandstone reservoir cannot be predicted.
The invention provides a method for predicting an internal permeability resistance band of a sandstone reservoir, which comprises the following steps:
and (3) seismic data quality evaluation: analyzing the signal-to-noise ratio and the resolution ratio of the seismic data space range corresponding to the target sandstone reservoir, and evaluating the quality of the seismic data;
denoising the seismic data: optimizing the seismic data quality by means of denoising or filtering until the signal-to-noise ratio and the seismic resolution in the seismic data quality are improved;
calculating the local structure entropy of the three-dimensional data volume;
calibrating a prediction result;
and (3) predicting the spreading of the permeation resistant strip: according to the top and bottom explanation horizon of the target oil and gas reservoir, calculating the sum of the local structure entropy values between the top and bottom interfaces by using the local structure entropy volume value, and displaying the sum as a plane diagram, wherein the plane diagram can be used for representing the plane distribution condition of the internal permeability resistance strip of the reservoir;
the well position deployment and optimization in the actual production of the oil field: based on the planar spreading knowledge of the permeability-resistant strips in the sandstone reservoir, the deployment and optimization of the well positions in the actual production of the oil field are reasonably guided, the well positions of the production wells need to avoid the permeability-resistant strips, and the permeability-resistant strips are avoided between the water injection wells and the oil production wells.
Preferably, the calculation of the local structural entropy of the three-dimensional data volume comprises the following steps:
preprocessing of sampling points: preprocessing the sampling points of the original three-dimensional seismic data volume, converting the seismic data of each sampling point into a seismic data difference obtained by subtracting the seismic channel mean value from the data, wherein the calculation formula of the seismic data difference is as follows:
in the formula (d)xytAnd
respectively, raw seismic data and converted seismic data, NtTotal number of seismic sampling points for each seismic trace, EtThe mean value of each sampling point of each seismic channel is represented, x and y are the spatial coordinate positions of seismic data, and t represents the time variable of the seismic data;
calculation of covariance matrix for sample points to be calculated, 2 × L centered on it was chosen1Number of lines, 2 × L2The three-dimensional analysis time window is formed by the number of channels and the N time sampling points and divided into four L1×L2× N quadrants, connecting the seismic traces in sequence end to end in each quadrant, and combining into a vector to obtain four corresponding vectors { a }i1,2,3,4, so as to obtain a covariance matrix S corresponding to the calculated sample point, which is expressed as:
calculating the local structural entropy of the sample point seismic data: calculating a local structure entropy value corresponding to the sample point by using the constructed covariance matrix, wherein the calculation formula is as follows:
wherein, | | · |, is Hilbert-Schmidt operator, tr means trace of calculation matrix,
local structural entropy of the global seismic data: sliding the three-dimensional analysis time window in the three-dimensional data body to obtain a local structure entropy value of each sample point of the three-dimensional data body, so as to obtain a local structure entropy value of the whole seismic data;
preferably, the calibration of the prediction result comprises the following steps:
arranging the number and thickness of the mudstone sections on all drilled horizontal wells of the target oil and gas reservoir;
removing mudstone section information encountered by drilling due to non-geological factors;
screening a mudstone section thickness lower limit for calibrating the local structure entropy of the whole seismic data volume according to the reservoir thickness which can be distinguished by seismic data;
calibrating an abnormal value with a larger local structure entropy value and a screened horizontal well mudstone section by using the horizontal well profile of the local structure entropy value of the overall seismic data to obtain the single-well coincidence rate; if the horizontal well meeting the mudstone section is not drilled in the full-horizontal well section, if the local structure entropy passes through the well profile and has no larger abnormal value, the coincidence rate is 100%, otherwise, the coincidence rate is 0%.
The invention has the beneficial effects that:
according to the method, the local structure entropy value of the seismic data is calculated, and the abnormal value with the larger local structure entropy value is calibrated with the mudstone section drilled on the horizontal well, so that the problem that the size range of the internal permeability resistance band of the sandstone reservoir is usually smaller than the size range of the resolution capability of seismic data, the internal permeability resistance band of the sandstone reservoir is difficult to accurately identify through a conventional seismic inversion or reservoir prediction means is solved, and the problem that the internal permeability resistance band of the sandstone reservoir cannot be predicted is solved.
Drawings
FIG. 1(a) is a seismic profile of an oilfield prior to denoising as provided by the present invention;
FIG. 1(b) is a seismic section of a field after denoising as provided by the present invention;
FIG. 2(a) is a seismic section of an oil field provided by the present invention;
FIG. 2(b) is a partial structural entropy profile of an oilfield provided by the present invention;
FIG. 3 is a statistical table of the number and thickness of mudstone segments on a horizontal well in a certain oilfield provided by the present invention;
FIG. 4(a) is a cross-sectional view of a mudstone section of a horizontal well A10H trajectory drilled reservoir provided by the present invention;
FIG. 4(b) is a statistical table of the number and thickness of mudstone segments on a horizontal well A10H provided by the present invention;
FIG. 4(c) is a lithology profile on horizontal well A10H provided by the present invention;
FIG. 5 is a statistical table of horizontal well mudstone segments for calibration obtained after screening according to the present invention;
FIG. 6(a) is a well-crossing trajectory profile of local structural entropy values for horizontal well A14H provided by the present invention;
FIG. 6(b) is a statistical table of mudstone zone data for horizontal well A14H provided by the present invention;
FIG. 6(c) is a well-crossing trajectory profile of the local structural entropy value of the horizontal well A15H provided by the present invention;
FIG. 6(d) is a cross-well trajectory profile of the local structural entropy value of the horizontal well A13H provided by the present invention;
FIG. 6(e) is a statistical table of mudstone zone data for horizontal well A13H provided by the present invention;
figure 7 is a plan projection of a predicted internal permeability barrier for sandstone in accordance with the present invention.
Detailed Description
Example 1
The invention provides a prediction method of an internal permeability resistance band of a sandstone reservoir, which comprises the following steps:
step S1: seismic data quality assessment
The method comprises the following steps of analyzing the signal-to-noise ratio and the resolution of a seismic data space range corresponding to a target sandstone reservoir, and evaluating the seismic data quality, wherein the parameters of the seismic data quality comprise the signal-to-noise ratio and the resolution, and the method comprises the following steps:
step S11: setting a target sandstone reservoir;
step S12: acquiring the seismic data quality of the sandstone reservoir;
step S13: analyzing the signal-to-noise ratio and seismic resolution of seismic data;
step S14: and evaluating the seismic data quality of the sandstone reservoir.
Step S2: seismic data de-noising processing
Because the two indexes of the signal-to-noise ratio and the resolution of the seismic data quality have larger influence on the application effect of the prediction method of the internal anti-seepage stripe of the sandstone reservoir, the seismic data quality is optimized by adopting a denoising or filtering method on the premise of stabilizing the seismic waveform and amplitude change until the signal-to-noise ratio and the seismic resolution of the seismic data are improved;
referring to fig. 1(a) and 1(b), the seismic profile of an actual oil field is a response in which sandstone layers 1 and mudstone layers 2 are alternately distributed in the longitudinal direction; FIG. 1(a) shows that before denoising, the thickness of a sandstone layer 1 and a mudstone layer 2 of a seismic section is uneven and appears in time, the waveform is influenced by noise, and the signal-to-noise ratio is low; as shown in fig. 1(b), after the noise interference is filtered, the sandstone layer and the shale layer of the seismic section of the seismic data are smooth and clear, and meanwhile, the seismic event discontinuity information of the geologic body boundary is retained.
Step S3: computation of local structural entropy of three-dimensional data volumes
Calculating a local structure entropy value of a three-dimensional data body, and detecting the distribution of permeability resistance strips in a sandstone reservoir, wherein the method comprises the following specific steps:
step S31: pretreatment of sampling points
Preprocessing the sampling points of the original three-dimensional seismic data volume, converting the seismic data of each sampling point into a seismic data difference obtained by subtracting the seismic channel mean value from the data, wherein the calculation formula of the seismic data difference is as follows:
in the formula (d)xytAnd
respectively, raw seismic data and converted seismic data, NtTotal number of seismic sampling points for each seismic trace, EtRepresenting the mean value of each sampling point of each seismic channel, and x and y are seismic numbersThe spatial coordinate position of the data, t, represents the time variant of the seismic data.
Step S32: computation of covariance matrix
Forming a three-dimensional analysis time window by using a three-dimensional data sub-volume generated by using the sample point as the center in the three-dimensional data volume, and selecting 2 × L using the sample point as the center for the sample point to be calculated1Number of lines, 2 × L2The three-dimensional analysis time window is formed by the number of channels and the N time sampling points and divided into four L1×L2× N quadrants, connecting the seismic traces in sequence end to end in each quadrant, and combining into a vector to obtain four corresponding vectors { a }i1,2,3,4, so as to obtain a covariance matrix S corresponding to the calculated sample point, which is expressed as:
step S33: calculation of local structural entropy of sample point seismic data
Calculating a local structure entropy value corresponding to the sample point by using the constructed covariance matrix, wherein the calculation formula is as follows:
in the formula, | | · |, which is Hilbert-Schmidt operator, tr means trace of the calculation matrix.
Step S34: local structural entropy value of global seismic data
And sliding the three-dimensional analysis time window in the three-dimensional data body to obtain the local structure entropy value of each sample point of the three-dimensional data body, thereby obtaining the local structure entropy value of the whole seismic data.
Taking a certain oil field in Heizhou of south China sea as an engineering entity, and performing an entity engineering test on the oil field in 2018 and 5 months to obtain a seismic section shown in a figure 2(a) and a local structure entropy section shown in a figure 2 (b); as shown in fig. 2(a), the seismic profile can clearly identify the permeability resistance band 3 and the well track 4 in addition to the sandstone layer 1 and the mudstone layer 2.
As can be seen from the comparison between fig. 2(a) and fig. 2(b), the local structure entropy has a high detection accuracy for the positions of the seismic event discontinuity, the wiggling or the amplitude variation, which are usually the distribution of the permeability resistance bands in the reservoir.
Step S4: calibration of prediction results
The method comprises the following steps of calibrating a prediction result of a local structure entropy value by using information of a drilled horizontal well of a target oil and gas reservoir, and judging the rationality of attribute extraction parameters and the reliability of the prediction result, wherein the method comprises the following specific steps:
step S41: the number and thickness of the mudstone segments on all the drilled horizontal wells of the target hydrocarbon reservoir are collated, see fig. 3.
Step S42: and (4) removing the information of the mudstone sections drilled by non-geological factors, wherein the non-geological factors comprise a well track drilling reservoir stratum and the like.
Referring to fig. 4(a) to 4(c), for a typical case that drilling out the reservoir along the horizontal well trajectory results in drilling the mudstone section, the depth of the well trajectory is continuously deepened during the drilling process of the a10H well, and after drilling 98 meters of mudstone, the well trajectory 4 is picked up and returns to the interior of the sandstone layer 1, so that the 98 meters of mudstone layer 2 drilled out can be determined to be removed due to drilling out the reservoir along the well trajectory 4 at the calibration time.
Step S43: and screening a mudstone section thickness lower limit for calibrating the local structure entropy value of the whole seismic data body according to the reservoir thickness which can be distinguished by seismic data, wherein the thickness lower limit is generally approximately equal to the distinguishing thickness of the seismic data, and the thickness lower limit of the mudstone section of the test oil field is about 20 meters.
The result of the mudstone segment available for calibration after the conditions are screened in the steps S42 and S43 is shown in fig. 5.
Step S44: calibrating the abnormal value of the horizontal well profile with the local structure entropy value of the overall seismic data and the horizontal well mudstone section after screening to obtain the single-well coincidence rate; if the horizontal well meeting the mudstone section is not drilled in the all-horizontal well section, if the local structure entropy passes through the well profile and has no abnormal value, the coincidence rate is 100%, otherwise, the coincidence rate is 0%.
As can be seen from fig. 6(a), the statistical mudstone segment data of the horizontal well a14H and the cross-well trajectory profile of the local structural entropy attribute, as can be seen from fig. 6(b), the abnormal response of the horizontal well a14H is completely matched with the mudstone segment information drilled and encountered on the well trajectory, and the calibration is qualified.
As can be seen from fig. 6(c) and 6(d), the full horizontal section of the horizontal well a15H is not drilled with mudstone, and the cross-well trajectory profile of the local structure entropy has no abnormal attribute value, so that the information of the two is completely consistent, and the calibration is qualified.
As can be seen from fig. 6(e), the calibration result of the well is shown, and it can be seen that only 1 mudstone segment in the horizontal well a13H is consistent with the abnormal value of the cross-well trajectory profile of the local structure entropy value, so that the single well coincidence rate is 25%.
Step S5: spreading prediction of permeation barrier strips
According to the top and bottom explanation horizon of the target oil and gas reservoir, the sum of the local structure entropy values between the top and bottom interfaces is calculated by using the local structure entropy volume value, and the sum is displayed as a plane diagram, and the plane diagram can be used for representing the plane distribution condition of the internal permeability resistance strip of the reservoir, as shown in fig. 7.
Step S6: well position deployment and optimization in actual production of oil field
Based on the planar spreading knowledge of the permeability-resistant strips in the sandstone reservoir, the deployment and optimization of the well position in the actual production of the oil field are reasonably guided, the well position of the production well needs to avoid the permeability-resistant strips, the existence of the permeability-resistant strips is preferably avoided between the water injection well and the oil production well, and if the permeability-resistant strips develop in the range defined by the permeability-resistant strips, residual oil possibly exists, and the production well can be deployed.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (3)
1. A prediction method of an internal permeability resistance band of a sandstone reservoir is characterized by comprising the following steps:
and (3) seismic data quality evaluation: analyzing the signal-to-noise ratio and the resolution ratio of the seismic data space range corresponding to the target sandstone reservoir, and evaluating the quality of the seismic data;
denoising the seismic data: optimizing the seismic data quality by means of denoising or filtering until the signal-to-noise ratio and the seismic resolution in the seismic data quality are improved;
calculating the local structure entropy of the three-dimensional data volume;
calibrating a prediction result;
and (3) predicting the spreading of the permeation resistant strip: according to the top and bottom explanation horizon of the target oil and gas reservoir, calculating the sum of the local structure entropy values between the top and bottom interfaces by using the local structure entropy volume value, and displaying the sum as a plane diagram, wherein the plane diagram can be used for representing the plane distribution condition of the internal permeability resistance strip of the reservoir;
the well position deployment and optimization in the actual production of the oil field: based on the planar spreading knowledge of the permeability-resistant strips in the sandstone reservoir, the deployment and optimization of the well positions in the actual production of the oil field are reasonably guided, the well positions of the production wells need to avoid the permeability-resistant strips, and the permeability-resistant strips are avoided between the water injection wells and the oil production wells.
2. The method for predicting the internal permeability resistance band of the sandstone reservoir of claim 1, wherein the calculation of the local structural entropy of the three-dimensional data volume comprises the following steps:
preprocessing of sampling points: preprocessing the sampling points of the original three-dimensional seismic data volume, converting the seismic data of each sampling point into a seismic data difference obtained by subtracting the seismic channel mean value from the data, wherein the calculation formula of the seismic data difference is as follows:
in the formula (d)xytAnd
respectively, raw seismic data and converted seismic data, NtTotal number of seismic sampling points for each seismic trace, EtThe mean value of each sampling point of each seismic channel is represented, x and y are the spatial coordinate positions of seismic data, and t represents the time variable of the seismic data;
calculation of covariance matrix for sample points to be calculated, 2 × L centered on it was chosen1Number of lines, 2 × L2The three-dimensional analysis time window is formed by the number of channels and the N time sampling points and divided into four L1×L2× N quadrants, connecting the seismic traces in sequence end to end in each quadrant, and combining into a vector to obtain four corresponding vectors { a }i1,2,3,4, so as to obtain a covariance matrix S corresponding to the calculated sample point, which is expressed as:
calculating the local structural entropy of the sample point seismic data: calculating a local structure entropy value corresponding to the sample point by using the constructed covariance matrix, wherein the calculation formula is as follows:
wherein, | | · |, is Hilbert-Schmidt operator, tr means trace of calculation matrix,
local structural entropy of the global seismic data: and sliding the three-dimensional analysis time window in the three-dimensional data body to obtain the local structure entropy value of each sample point of the three-dimensional data body, thereby obtaining the local structure entropy value of the whole seismic data.
3. The method for predicting the internal permeability resistance band of the sandstone reservoir of claim 1, wherein the calibration of the prediction result comprises the following steps:
arranging the number and thickness of the mudstone sections on all drilled horizontal wells of the target oil and gas reservoir;
removing mudstone section information encountered by drilling due to non-geological factors;
screening a mudstone section thickness lower limit for calibrating the local structure entropy of the whole seismic data volume according to the reservoir thickness which can be distinguished by seismic data;
calibrating an abnormal value with a larger local structure entropy value and a screened horizontal well mudstone section by using the horizontal well profile of the local structure entropy value of the overall seismic data to obtain the single-well coincidence rate; if the horizontal well meeting the mudstone section is not drilled in the full-horizontal well section, if the local structure entropy passes through the well profile and has no larger abnormal value, the coincidence rate is 100%, otherwise, the coincidence rate is 0%.
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