CN111582528A - Inter-well connectivity discrimination method based on fracture prediction and dynamic response - Google Patents
Inter-well connectivity discrimination method based on fracture prediction and dynamic response Download PDFInfo
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Abstract
The invention discloses a method for judging the connectivity among wells based on fracture prediction and dynamic response, which comprises the following steps: determining a target well fracture structure of the reservoir according to a fracture prediction method; acquiring target inter-well communication information of a reservoir body according to a dynamic response method; and determining the communication between the target wells by combining the discontinuous crack structure and the communication information of the target wells. The method can combine the static and dynamic discrimination methods, simply, conveniently and efficiently identify the cracks and the connectivity between the target wells, and has good accuracy.
Description
Technical Field
The invention relates to a method for judging the connectivity between wells based on fracture prediction and dynamic response, and belongs to the field of carbonate reservoir development.
Background
At present, reservoir interwell connectivity is typically studied from both static and dynamic aspects. The static connectivity among wells refers to the connectivity result obtained by applying a geological and geophysical exploration method, and is determined by the geological characteristics of an oil reservoir and the characteristics of the oil reservoir. Static connectivity between wells is usually determined by describing formation parameters by methods such as formation comparison and well logging. However, due to the complex characteristics of fracture-cavity reservoir bodies, the connectivity of the fracture-cavity bodies cannot be effectively known by the traditional geological and geophysical exploration research method. The method for researching the dynamic connectivity between wells commonly used at home and abroad mainly comprises tracer testing, pressure testing, well testing methods such as interference well testing, pulse well testing, unstable well testing and the like, and various connectivity identification methods such as establishing multiple linear regression by utilizing production dynamic data based on the idea of system analysis.
The pressure data is the most direct data for determining the connectivity between wells. The analysis of the pressure system is an important basis for judging the communication among wells. The method for judging the connectivity among wells according to the approximate equality of the original converted pressure at each position of the stratum, the similarity of the pressure descending trend and the yield descending trend of the stratum of each well during the exploitation period and the consistency information of the crude oil density, the components and the like of each well is the method which is most applied on site.
The well testing method is an effective method for researching the connectivity of the oil reservoir, and comprises interference well testing, pulse well testing (such as Wanxinde and the like, application of pulse well testing in oil field development, special oil and gas reservoirs, 2006, Nie crystal, analysis of the connectivity of low-permeability oil reservoir among wells based on pulse well testing, university of Yangtze river (own edition). 2013(10), Dianzyunfang and the like, research of the connectivity of low-permeability oil reservoir by using the pulse well testing method, petroleum science and report 2003) and unstable well testing (such as Linjiann and the like, qualitative analysis of injection-production balance and connectivity among wells by using unstable pressure well testing data, oil and gas well testing, 1997(02), Liao Wei and the like, and application of well testing to judge the connectivity among wells, and oil exploration and development, 2002 (04)). However, the interference well testing and the impulse well testing require changing the working system of the well, and the test period is long, so that the production plan of the oil field is inevitably influenced, and the cost of the high-precision pressure gauge is high.
The scholars use the method of 'interference-like well testing' to analyze the well group with a lot of dynamic data (oil pressure, nozzle change, output, water content, crude oil density, etc.) of oil well, and uses the interference information between wells in the development process, such as new well production, working system change, well measure, etc., to observe whether the interference information can be received in the adjacent wells, if the interference phenomenon exists, the wells are connected. Such as poplar (the karst fracture cave type carbonate reservoir interwell connectivity research in Tahe oilfield 4. Xinjiang geology 2004(02)), Zhang Xiao Heng, Qiminghu, etc. (the interwell connectivity of Tahe oilfield is researched by using similar interference well testing. inner Mongolia petrochemical industry 2009)
The multivariate linear regression analysis and the capacitance model can quantitatively reflect the relative communication relation among wells. For example, Albertoni adopts multivariate linear regression to solve the inter-well communication problem and obtains better effect. The model regresses weight coefficients that quantitatively characterize the degree of well-to-well connectivity. Youeff et al (A Capacity Model to Infer Interwellconnectivity from Production and Injection Rate Properties. SPE 95322.2005) utilize multivariate linear regression analysis to create a compression Model based on the Injection and Production data, taking into account the pressure data, as well as the time lag, attenuation characteristics of the Injection signal. Yousef et al (a Analysis and Interpretation of the interaction communication from Production and Injection Rate using a capacity model. spe 99998.2006) have a diagnostic tool that establishes permeability distribution and geologic features based on a capacitance model, and using a Connectivity coefficient and a time constant allows for more accurate inference of formation conditions than using a single parameter. However, these two methods have no clear geological significance.
Because the carbonate fracture-cavity oil reservoir has various reservoir types, complex fracture-cavity combination relationship, poor continuity and strong heterogeneity, and a single method is difficult to accurately judge and predict the inter-well fracture distribution and the inter-well connectivity, a static and dynamic combined comprehensive judgment method is necessary to be established.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a method for discriminating the connectivity between wells based on fracture prediction and dynamic response, which can combine the static and dynamic discrimination methods to simply and efficiently identify the cracks and connectivity between target wells, and has good accuracy.
In order to achieve the purpose, the invention adopts the following technical scheme: a method for judging the connectivity among wells based on fracture prediction and dynamic response comprises the following steps: determining a target well fracture structure of the reservoir according to a fracture prediction method; acquiring target inter-well communication information of a reservoir body according to a dynamic response method; and determining the communication between the target wells by combining the discontinuous crack structure and the communication information of the target wells.
In a particular embodiment, the fracture configuration includes a primary fracture and a secondary fracture.
In a specific embodiment, the dominant fracture is determined in combination with coherence properties and amplitude slicing.
In one particular embodiment, secondary rupture is determined in combination with ant body and body curvature properties.
In one embodiment, the injection-production interwell connectivity information is obtained using an injection-production response method and a tracer method.
In one embodiment, the production interval communication information is obtained using a disturbance-like method.
In a specific embodiment, the injection-production response method obtains communication information between injection-production wells by analyzing water content, liquid production quantity or oil production quantity fluctuation characteristics of production dynamic data; the tracer method is to inject tracer slug into injection well, monitor the output of tracer in peripheral production well, determine the curve of tracer output concentration changing with time, analyze or history fit the curve, obtain the communication information between injection and production wells.
In one embodiment, the interference-like method comprises the step of acquiring communication information between oil production wells by combining the interference-like method, the similarity of production characteristics, the consistency of water breakthrough time and the pressure trend reduction method.
In a specific embodiment, weighting values of the inter-well communication information acquired by a new well type interference, production characteristic similarity or water breakthrough time consistency method are respectively 1, and weighting values of the inter-well communication information acquired by a pressure trend reduction method are 2; and if the accumulated communication weight between the oil production wells is more than 2, determining the communication between the oil production wells.
In a particular embodiment, the production characteristic similarity method includes determining production dynamic curve similarity using gray correlation analysis or a dynamic time warping method.
In one embodiment, determining connectivity information for a production well using a grey relevance analysis method comprises the steps of: (1) acquiring liquid production data of an oil production well; (2) calculating grey correlation coefficients among the oil production wells; (3) determining grey correlation degrees among oil production wells; (4) and determining whether the oil production wells are communicated or not according to the grey correlation degree between the oil production wells.
In one embodiment, determining a gray correlation between production wells comprises the steps of:
selecting a plurality of oil production wells, and determining a production data corresponding sequence of each oil production well by adopting the following formula:
Xi＝{xi(1)，xi(2)，......xi(n)} (1)
Xj＝{xj(1)，xj(2)，......xj(n)} (2)
in the formula, XiProduction data for the ith production well, xi(n) is the production data of the nth position of the ith oil production well, XjProduction data for the jth producing well, xj(n) is the production data of the nth position of the jth oil production well, wherein the production data of each position of each oil production well is a dimensionless value;
secondly, the formula for calculating the difference value of the production data of the corresponding positions of the multiple oil production wells is as follows:
Δij(k)＝|xi(k)-xj(k)|，(k＝1，2，...n) (3)
Xij＝{Δij(1)，Δij(2)，...Δij(n)} (4)
in the formula,. DELTA.ij(k) Is the corresponding difference value of the production data at the kth position of the ith and jth oil production wells, xi(k) Production data, x, for the location at the kth site of the ith production wellj(k) Production data for the kth position of the ith production well, XijThe production data difference values of the corresponding positions of the ith and jth oil production wells are collected;
the formula for determining the minimum absolute difference of the production data at the maximum and minimum positions of each oil production well is as follows:
in the formula,. DELTA.minIs the minimum absolute difference, x0(k) Selecting a well with the largest production data fluctuation as a reference well when matching the gray correlation for the production sequence of the reference well, wherein the communication possibility of the reference well is the largest, the similarity calculation is carried out on other wells and the reference well, and the inner layer is
For minimum of the corresponding position distance between the current sequence to be compared and the reference sequence, of the outer layer
Is the global minimum of the corresponding position distances between all sequences to be compared and the reference sequence.
Determining the maximum absolute difference of the production data at the maximum and minimum positions of each oil production well by adopting the following formula:
in the formula,. DELTA.maxOf the inner layer for maximum absolute difference
For maximum of the corresponding position distance between the current sequence to be compared and the reference sequence, of the outer layer
Is the global maximum of the corresponding position distances between all sequences to be compared and the reference sequence.
Calculating the correlation coefficient of each item of production data of the multiple oil production wells by adopting the following formula:
where ρ is 0.5, and a smaller ρ indicates higher accuracy, ξij(k) Correlation coefficient of each item of production data of the multi-port oil production well;
sixthly, the formula for calculating the grey correlation coefficient of each item of production data of the multi-port oil production well is as follows:
in the formula, rijAnd the grey correlation coefficient is the production data of the multiple oil production wells.
In a specific embodiment, the dynamic time warping method for determining the similarity of the production dynamic curve comprises the step of carrying out quantitative judgment on the similarity of the production dynamic curve by using a dynamic time warping algorithm on the basis of determining the gray correlation coefficient.
In one embodiment, the calculation flow of the dynamic time warping method is as follows: firstly, selecting a plurality of oil production wells, and respectively determining the sequences as follows:
Q＝q1，q2，...，qi，...，qn(9)
C＝c1，c2，...，ci，...，cm(10)
in the formula, Q in the sequence Q1......qnIs a production data sequence for production well Q; c in sequence C1......cmIs a production data sequence for production well C.
Then, the following steps are executed:
judging whether n is equal to m, if so, directly comparing corresponding positions, and otherwise, entering the next step;
② A matrix of n × m is constructed, the matrix element d (q)i，cj) Denotes qiAnd cjDetermining the similarity between each point of the sequence Q and each point of the sequence C, wherein the smaller the distance is, the higher the similarity is;
thirdly, calculating the total accumulated distance of the n multiplied by m matrix according to a state transfer equation, and recording the accumulated times t;
fourthly, the formula adopted for calculating the dynamic time regular value is as follows:
Dis(i，j)＝d(qi，cj)+min{Dis(i-1，j-1)，Dis(i，j-1)，Dis(i-1，j)} (11)
in the formula, Dis (n, m) is the total accumulated distance of the matrix, t is the accumulated times, and Dis (i, j), Dis (i-1, j-1), Dis (i, j-1) and Dis (i-1, j) are the total accumulated distance of the i multiplied by j matrix, (i-1) multiplied by (j-1) the total accumulated distance of the matrix, i multiplied by (j-1) the total accumulated distance of the matrix, and (i-1) multiplied by j the total accumulated distance of the matrix, respectively.
In one embodiment, the distance between the elements of the matrix is determined using the formula:
d(qi，cj)＝(qi-cj) (13)
in the formula, d (q)i，cj) Is qiAnd cjThe distance between them.
Due to the adoption of the technical scheme, the invention has the following advantages: the method can effectively determine the main fracture among target wells by combining coherent attributes and amplitude slices, improve ant body tracking by utilizing the body curvature attributes which are not influenced by reflection amplitude and waveform, better determine the secondary fracture among the target wells, dynamically acquire the communication information among the target wells by utilizing a dynamic response method, and finally combine a dynamic response result with fracture prediction to determine the communication condition among the target wells.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solution in the embodiments of the present invention, the following briefly introduces the drawings required in the description of the embodiments:
FIG. 1 is a schematic flow diagram illustrating an embodiment of a method for determining connectivity between wells based on fracture prediction and dynamic response;
FIG. 2 is a schematic diagram of the structure of the dynamic time warping method of the present invention;
FIG. 3 is a drawing of the present invention
A schematic structural diagram of a face principal fracture distribution;
FIG. 4 is a drawing of the present invention
A schematic of the structure of the secondary face fracture distribution;
FIG. 5 is a schematic of the breakthrough time of the tracer of the invention;
FIG. 6 is a schematic of the peak concentration of the tracer of the invention;
fig. 7 is a schematic of a voidage replacement response diagram of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
As shown in fig. 1, the method for judging the connectivity between wells based on fracture prediction and dynamic response provided by the present invention includes the following steps:
(1) determining a target well fracture structure of the reservoir according to a fracture prediction method;
(2) acquiring target inter-well communication information of a reservoir body according to a dynamic response method;
(3) and determining the communication between the target wells by combining the discontinuous crack structure and the communication information of the target wells.
In particular, reservoirs include carbonate fractured-vuggy reservoirs and fractured reservoir geological conditions. If cracks exist in reservoir rock, the physical properties of compact rock are different, a physical interface is formed, the seismic wave reflection characteristics of the reservoir are changed, and therefore the cracks are displayed on a seismic section. The production dynamic data is the comprehensive reflection of the oil layer and the fluid flow thereof in the well control range, and for the strongly heterogeneous carbonate fracture-cavity type oil reservoir, a research method of dynamic and static combination is an effective method for recognizing and researching the special reservoir.
Further, the target wells include water injection wells, oil production wells, and production wells.
First, determining a reservoir well fracture configuration from a fracture prediction method includes the following:
combining coherent attribute and amplitude slice to determine main fracture between target wells
Whether coherent, ant body tracking, or AFE (automatic fault detection) and AFE ant body tracking (ant body tracking fault detection), the corresponding coherent algorithm is affected by the seismic data reflection amplitude and waveform. Although the coherent slice can clearly identify the deep stem fracture pattern, the interference of a river, beads and the like on the medium-shallow layer leads to unclear identification and structural pattern of the medium-shallow level fracture of the coherent slice.
Further, on the basis of well seismic calibration, the eigenvalue coherence attribute is optimized, eigenvalue slices are extracted, and the target inter-well main fracture is determined by combining the amplitude slices.
Determining target inter-well secondary fracture by combining ant body and curvature property
The body curvature property which is not influenced by reflection amplitude and waveform is introduced to improve ant body tracking, and the target inter-well secondary fracture can be better determined. The body curvature body can only calculate the deformation degree of the same-phase axis, is insensitive to the reflection amplitude and the bead curvature, and is sensitive to deflection and transverse wrinkle deformation, so that the secondary fracture identification effect can be improved by improving the ant body extraction process. Curvature ant bodies (combination of ant body and body curvature properties) enable clear determination of target inter-well secondary fractures and fissure zone development. Wherein, the crack belt development condition comprises the position determination of fold and deflection deformation.
Secondly, acquiring the target inter-well communication information of the reservoir according to the dynamic response method comprises the following steps:
first, a dynamic response method is determined. The dynamic response method comprises two parts, wherein one part is used for acquiring the communication information between injection wells and production wells by using an injection-production response method and a tracer method, and the other part is used for acquiring the communication information between production wells by using an interference-like method.
Further, the inter-well communication information includes characteristics of inter-well communication relation and communication degree.
Firstly, an injection-production response method obtains communication information between injection-production wells
The injection and production response method is characterized in that when the injection quantity of a water injection well changes, the liquid yield of a related oil production well fluctuates, and communication information among the injection and production wells is obtained by analyzing the water content, the liquid yield or the oil yield fluctuation characteristics of production dynamic data.
Specifically, after water injection, which is equivalent to adding excitation, the production data curves of adjacent production wells have severe fluctuation, and the larger the fluctuation is under the same water injection condition, the larger the dynamic communication degree between the adjacent production wells is. If the water content, the oil yield or the production trend of the adjacent oil production wells are obviously changed, the dynamic communication between the adjacent injection and production wells is indicated, otherwise, the communication between the adjacent injection and production wells is not established. The obvious change is particularly shown in the increase of the pressure of the adjacent oil production well, the rise of the working fluid level, the rise of the liquid production amount and the continuous change of the water content. The advantages are that: the injection-production response method can determine the tiny change of the production data curve by means of a computer algorithm, avoids the multiple solution caused by manual determination, and enables the calculation result to be more accurate.
The idea of analyzing the water content, the liquid production amount or the oil production fluctuation characteristic of the production dynamic data and acquiring the communication information between injection and production wells by the injection and production response method is as follows:
on the basis of reading and analyzing multi-source data, judging a communicated well set which preliminarily forms an effective displacement path according to the well spacing of the injection and production wells, determining different water injection sections according to water injection data, selecting a corresponding dynamic data judgment time interval of the injection and production wells, obtaining water content, liquid production and oil production data through a sliding time window, and analyzing fluctuation change characteristics. And acquiring communication information between injection wells and production wells by analyzing fluctuation change characteristics, well spacing data of the injection wells and the production wells (including under strong fluctuation and weak fluctuation conditions) and a working system. The advantages are that: the algorithm comprehensively considers a plurality of methods based on multi-source data, effectively judges the characteristics and the communication degree strength (communication information of the injection and production wells) of the communication relation between the injection and production wells in dynamic and static combination and multiple angles, determines the flow direction of the injected fluid, the swept condition of the fluid and the water injection effect condition, and has better practical application significance.
Further, the multi-source data includes reservoir geology and oilfield production static and dynamic data.
Further, the working system comprises oil nozzle replacement, well washing and well stopping.
② tracer test method for obtaining communication information between injection wells and production wells
The interwell tracing test method is characterized in that tracer slugs are injected into an injection well, the production condition of the tracer is monitored in surrounding production wells, a curve of the output concentration of the tracer along with time change is obtained, then, the curve is analyzed or subjected to history fitting, and the physical property and communication information of reservoirs among injection wells and production wells can be obtained. Wherein, the size of the crack (or fracture) between injection and production wells is represented by the production concentration of the tracer and the length of the breakthrough time. The higher the tracer production concentration and the shorter the breakthrough time, the larger the scale of the fracture (or break) between injection and production wells, and vice versa.
Obtaining communication information between oil production wells by using similar interference method
The interference-like method comprises the step of determining communication information among oil extraction wells in parallel by adopting new well interference-like, production characteristic similarity, water breakthrough time consistency and pressure trend descent methods. Furthermore, the results of each method are weighted, and considering that the reliability of the results analyzed by the pressure trend reduction method is good, the result weight is set to be 2, and the result weights judged by the other methods are set to be 1. And if the cumulative communication weight among the oil production wells is greater than 2, confirming communication among the oil production wells.
Interference method for new well
In the process of oil field development, the working system of putting a new well into production and closing the well is changed, and the dynamic change of the development of adjacent oil production wells can be influenced by the abnormal production wells. The method comprises the steps of firstly determining 'production abnormal wells' by taking adjacent oil production wells as basic units, then calculating whether various development curve change characteristics of the adjacent oil production wells change in a reverse rhythm mode, and determining whether inter-well dynamic interference exists to obtain communication information among the oil production wells.
II production of feature similarity method
The fracture-cavity unit is provided with the same fluid power system, and production wells communicated with each other have similar production dynamic characteristics, and a certain similarity is often shown on a production dynamic curve.
The method for analyzing the similarity of the production dynamic curves is characterized in that for well points which are close to each other and have severe fluctuation characteristics of water content, liquid production amount or oil production dynamic data, the similarity of the curves among a plurality of time sequences is measured, and the inter-well communication relation is determined by judging whether the production dynamic curves are related to the same rhythm or the reverse rhythm, so that whether the production dynamic curves belong to the same fracture-cave unit is judged.
Furthermore, production dynamic curves among the oil production wells are related in the same rhythm, and communication among the oil production wells is determined. The production dynamic curves between the oil production wells are related in an inverse rhythm, and the oil production wells are determined to be not communicated.
Further, curve similarity is determined by a grey correlation analysis method or a dynamic time warping method.
i grey correlation analysis method
Because of the condition of multiple injection and multiple production, the yield, bottom pressure and water content of the production well are influenced by a plurality of surrounding water injection wells, and the communication relation between the injection and production wells is a gray system determined by the complexity and time lag of the stratum. The degree of similarity or dissimilarity is the "grey correlation degree", and the correlation degree between the factors is measured by calculating the correlation coefficient. And judging the difference of the geometric shapes between the production dynamic curves by using the difference value between the production dynamic curves as a measurement scale of the correlation degree. Studying the connectivity between wells using the gray prediction method includes determining the connectivity between production wells using a gray relevancy analysis method. And comparing multiple graphs through grey correlation analysis to measure the similarity among multiple time sequences, and judging whether the production dynamic curve is related to the same rhythm and the reverse rhythm or not to determine communication information among the oil extraction wells.
Furthermore, the grey correlation degree between the oil production wells is large, and communication between the oil production wells is determined. And the grey correlation degree between the oil production wells is small, and the oil production wells are determined not to be communicated.
Further, the step of determining the communication information of the oil production well by using the grey correlation degree analysis method comprises the following steps: acquiring liquid production data of an oil production well; secondly, calculating a grey correlation coefficient between oil production wells; determining the grey correlation degree between the oil production wells; and (IV) determining whether the oil production wells are communicated or not according to the grey correlation degree.
Determining the grey correlation degree between the oil production wells comprises the following steps:
selecting a plurality of oil production wells, and determining a production data corresponding sequence of each oil production well by adopting the following formula:
Xi＝{xi(1)，xi(2)，......xi(n)} (1)
Xj＝{xj(1)，xj(2)，......xj(n)} (2)
in the formula, XiProduction data for the ith production well, xi(n) is the production data of the nth position of the ith oil production well, XjProduction data for the jth producing well, xj(n) the production data of the nth position of the jth oil production well, wherein the production data of each position of each oil production wellIs a dimensionless value;
secondly, the formula for calculating the difference value of the production data of the corresponding positions of the multiple oil production wells is as follows:
Δij(k)＝|xi(k)-xj(k)|，(k＝1，2，...n) (3)
Xij＝{Δij(1)，Δij(2)，...Δij(n)} (4)
in the formula,. DELTA.ij(k) Is the corresponding difference value of the production data at the kth position of the ith and jth oil production wells, xi(k) Production data, x, for the location at the kth site of the ith production wellj(k) Production data for the kth position of the ith production well, XijThe production data difference values of the corresponding positions of the ith and jth oil production wells are collected;
the formula for determining the minimum absolute difference of the production data at the maximum and minimum positions of each oil production well is as follows:
in the formula,. DELTA.minIs the minimum absolute difference, x0(k) Selecting a well with the largest production data fluctuation as a reference well when matching the gray correlation for the production sequence of the reference well, wherein the communication possibility of the reference well is the largest, the similarity calculation is carried out on other wells and the reference well, and the inner layer is
For minimum of the corresponding position distance between the current sequence to be compared and the reference sequence, of the outer layer
Is the global minimum of the corresponding position distances between all sequences to be compared and the reference sequence.
Determining the maximum absolute difference of the production data at the maximum and minimum positions of each oil production well by adopting the following formula:
in the formula,. DELTA.maxOf the inner layer for maximum absolute difference
For maximum of the corresponding position distance between the current sequence to be compared and the reference sequence, of the outer layer
Is the global maximum of the corresponding position distances between all sequences to be compared and the reference sequence.
Calculating the correlation coefficient of each item of production data of the multiple oil production wells by adopting the following formula:
where ρ is 0.5, and a smaller ρ indicates higher accuracy, ξij(k) Correlation coefficient of each item of production data of the multi-port oil production well;
sixthly, the formula for calculating the grey correlation coefficient of each item of production data of the multi-port oil production well is as follows:
in the formula, rijAnd the grey correlation coefficient is the production data of the multiple oil production wells.
ii dynamic time warping method
As shown in fig. 2, the determining of the similarity of the production dynamic curve by the dynamic time warping method includes performing quantitative determination on the similarity of the production dynamic curve by using a dynamic time warping algorithm based on dynamic programming on the basis of determining the gray correlation coefficient. C is a reference sequence, and C is a reference sequence,
is a sequence to be compared; l is the end point of the reference sequence,
is the end point of the sequence to be compared; s is a point in the sequence C,
is composed of
A point of (a);
further, the calculation flow of the dynamic time warping method is as follows:
firstly, selecting a plurality of oil production wells, and determining the sequences of the oil production wells as follows:
Q＝q1，q2，...，qi，...，qn(9)
C＝c1，c2，...，ci，...，cm(10)
in the formula, Q in the sequence Q1......qnIs a production data sequence for production well Q; c in sequence C1......cmIs a production data sequence for production well C.
Then, the following steps are executed:
judging whether n is equal to m, if so, directly comparing corresponding positions, and otherwise, entering the next step;
② A matrix of n × m is constructed, the matrix element d (q)i，cj) Denotes qiAnd cjDetermining the similarity between each point of the sequence Q and each point of the sequence C, wherein the smaller the distance is, the higher the similarity is;
thirdly, calculating the total accumulated distance of the n multiplied by m matrix according to a state transfer equation, and recording the accumulated times t;
fourthly, the formula adopted for calculating the dynamic time regular value is as follows:
Dis(i，j)＝d(qi，cj)+min{Dis(i-1，j-1)，Dis(i，j-1)，Dis(i-1，j)} (11)
in the formula, Dis (n, m) is the total accumulated distance of the matrix, t is the accumulated times, and Dis (i, j), Dis (i-1, j-1), Dis (i, j-1) and Dis (i-1, j) are the total accumulated distance of the i multiplied by j matrix, (i-1) multiplied by (j-1) the total accumulated distance of the matrix, i multiplied by (j-1) the total accumulated distance of the matrix, and (i-1) multiplied by j the total accumulated distance of the matrix, respectively.
The formula used to determine the distances between the matrix elements is:
d(qi，cj)＝(qi-cj) (13)
in the formula, d (q)i，cj) Is qiAnd cjThe distance between them.
iii pressure trend analysis method
The pressure data is the most direct data for judging the communication between the target wells. The analysis of the pressure system is an important basis for judging the communication between target wells. The karst fracture-cave carbonate reservoir body theoretically has relatively consistent pressure drop or pressure change trend in the same unit, and the pressure drop trend is consistent according to time. Different fracture-cavity cells should have different formation pressure variation characteristics.
iv method for keeping consistent water-breakthrough time
When the well is opened, the production well is in a self-spraying state, the produced liquid is basically oil, the water content is 0 or extremely low, the water content is generally considered to be water breakthrough when the water content reaches 2%, and the time is the water breakthrough time. In the karst fracture-cave carbonate reservoir body, the water breakthrough time interval of two communicated production wells is generally not more than 2 years, and the communication can be judged to a certain extent by analyzing the water breakthrough time relationship between the two production wells.
And thirdly, determining the target inter-well connectivity by combining the reservoir body inter-well fracture structure and the inter-well connectivity information.
And further determining the target inter-well connectivity by combining target inter-well connectivity information obtained by dynamic response discrimination according to the determined main fractures and secondary fractures among the target wells of the reservoir body. By combining static communication and dynamic communication, the communication relation between the target wells is clearer and more definite.
An example is listed below:
certain reservoirThe fracture pattern of the original seismic section of the collective unit is fuzzy, the fault direction is not clear, and the continuity is poor. After reinterpretation, the fracture pattern is clear, and the fault distribution direction is clear. Coherent, all-directional ant and AFE ant tracking identifies major north-east and north-west breakages: (
A plane dominant fracture distribution, as shown in fig. 3), but the cutting and combination relationships between fractures are not very clear. The curvature ant body tracing not only clearly identifies the main fracture cutting and combination relation in the northeast and northwest directions, but also clearly identifies the secondary fracture and crack belt derived from the main fracture (the following formula)
The plane secondary fracture distribution, as shown in fig. 4), the overall construction configuration is clear. And providing a reliable fracture prediction result for the next connectivity analysis.
Selecting a TK663-TK635H well group, selecting a TK663 well for injecting a tracer, selecting a TK635H well for monitoring, breaking through the tracer after 3 days (shown in table 1), and reaching a maximum peak concentration after 13 days (shown in figures 5 and 6). From the injection-production response diagram (shown in figure 7), the TK663-TK635H well dynamic response is obvious, which indicates that a large communication channel exists between the TK663-TK635H wells. Meanwhile, as seen from a secondary fracture profile (shown in FIG. 4), a secondary fracture exists between the two TK663-TK635H wells, so that the two TK663-TK635H wells are proved to have good connectivity through the combination of static communication and dynamic communication.
TABLE 1 results of tracer test for well groups
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (12)
1. A method for judging the connectivity among wells based on fracture prediction and dynamic response is characterized by comprising the following steps:
determining a target well fracture structure of the reservoir according to a fracture prediction method;
acquiring target inter-well communication information of a reservoir body according to a dynamic response method;
and determining the communication between the target wells by combining the discontinuous crack structure and the communication information of the target wells.
2. The method as claimed in claim 1, wherein the fracture structure comprises a main fracture and a secondary fracture, the main fracture is determined by combining coherence properties and amplitude slices, and the secondary fracture is determined by combining ant body and body curvature properties.
3. The method for judging the connectivity between wells based on the fracture prediction and the dynamic response as claimed in claim 1, wherein the injection-production well connectivity information is obtained by an injection-production response method and a tracer method.
4. The method for discriminating the communication between wells based on the fracture prediction and the dynamic response as claimed in claim 3, wherein the communication information between the oil production wells is obtained by using a quasi-interference method.
5. The method for judging the connectivity between wells based on the fracture prediction and the dynamic response is characterized in that an injection-production response method obtains the connectivity information between injection-production wells by analyzing the water content, the liquid production amount or the oil production fluctuation characteristics of production dynamic data; the tracer method is to inject tracer slug into injection well, monitor the output of tracer in peripheral production well, determine the curve of tracer output concentration changing with time, analyze or history fit the curve, obtain the communication information between injection and production wells.
6. The method of claim 4 wherein the interference-like approach includes using new well interference, production characteristic similarity, water breakthrough time consistency and pressure trend descent methods in combination to obtain production well communication information.
7. The method for judging the connectivity among wells based on the fracture prediction and the dynamic response is characterized in that the weight of the connectivity information among wells obtained by the new well interference, the production characteristic similarity or the water-break time consistency method is respectively given as 1, and the weight of the connectivity information among wells obtained by the pressure trend reduction method is given as 2; and if the accumulated communication weight between the oil production wells is more than 2, determining the communication between the oil production wells.
8. The method for discriminating well-to-well connectivity based on fracture prediction and dynamic response of claim 7, wherein the method for similarity of production characteristics comprises determining the similarity of production dynamic curves by using gray correlation analysis or dynamic time warping.
9. The method for discriminating the connectivity between wells based on the fracture prediction and the dynamic response as claimed in claim 8, wherein the step of determining the connectivity information of the oil production well by using the grey correlation degree analysis method comprises the following steps: (1) acquiring liquid production data of an oil production well; (2) calculating grey correlation coefficients among the oil production wells; (3) determining grey correlation degrees among oil production wells; (4) and determining whether the oil production wells are communicated or not according to the grey correlation degree between the oil production wells.
10. The method for discriminating well-to-well connectivity based on fracture prediction and dynamic response of claim 9, wherein the step of determining the gray correlation degree between the oil production wells comprises the steps of:
selecting a plurality of oil production wells, and determining a production data corresponding sequence of each oil production well by adopting the following formula:
Xi＝{xi(1)，xi(2)，......xi(n)} (1)
Xj＝{xj(1)，xj(2)，......xj(n)} (2)
in the formula, XiProduction data for the ith production well, xi(n) is the production data of the nth position of the ith oil production well, XjProduction data for the jth producing well, xj(n) is the production data of the nth position of the jth oil production well, wherein the production data of each position of each oil production well is a dimensionless value;
secondly, the formula for calculating the difference value of the production data of the corresponding positions of the multiple oil production wells is as follows:
Δij(k)＝|xi(k)-xj(k)|，(k＝1，2，...n) (3)
Xij＝{Δij(1)，Δij(2)，...Δij(n)} (4)
in the formula,. DELTA.ij(k) Is the corresponding difference value of the production data at the kth position of the ith and jth oil production wells, xi(k) Production data, x, for the location at the kth site of the ith production wellj(k) Production data for the kth position of the ith production well, XijThe production data difference values of the corresponding positions of the ith and jth oil production wells are collected;
the formula for determining the minimum absolute difference of the production data at the maximum and minimum positions of each oil production well is as follows:
in the formula,. DELTA.minIs the minimum absolute difference, x0(k) Selecting a well with the largest production data fluctuation as a reference well when matching the gray correlation for the production sequence of the reference well, wherein the communication possibility of the reference well is the largest, the similarity calculation is carried out on other wells and the reference well, and the inner layer is
For minimum of the corresponding position distance between the current sequence to be compared and the reference sequence, of the outer layer
Is the global minimum of the corresponding position distances between all sequences to be compared and the reference sequence.
Determining the maximum absolute difference of the production data at the maximum and minimum positions of each oil production well by adopting the following formula:
in the formula,. DELTA.maxOf the inner layer for maximum absolute difference
For maximum of the corresponding position distance between the current sequence to be compared and the reference sequence, of the outer layer
Is the global maximum of the corresponding position distances between all sequences to be compared and the reference sequence.
Calculating the correlation coefficient of each item of production data of the multiple oil production wells by adopting the following formula:
where ρ is 0.5, and a smaller ρ indicates higher accuracy, ξij(k) Correlation coefficient of each item of production data of the multi-port oil production well;
sixthly, the formula for calculating the grey correlation coefficient of each item of production data of the multi-port oil production well is as follows:
in the formula, rijAnd the grey correlation coefficient is the production data of the multiple oil production wells.
11. The method of claim 10, wherein the dynamic time warping method for determining the similarity of the production dynamic curve comprises performing a quantitative determination of the similarity of the production dynamic curve by using a dynamic time warping algorithm based on the determination of the grey correlation coefficient of the production well.
12. The method for discriminating the connectivity between wells based on the fracture prediction and the dynamic response as claimed in claim 11, wherein the dynamic time warping method comprises the following steps: firstly, selecting a plurality of oil production wells, and respectively determining the sequences as follows:
Q＝q1，q2，...，qi，...，qn(9)
C＝c1，c2，...，ci，...，cm(10)
in the formula, Q in the sequence Q1......qnIs a production data sequence for production well Q; c in sequence C1......cmIs a production data sequence for production well C.
Then, the following steps are executed:
judging whether n is equal to m, if so, directly comparing corresponding positions, and otherwise, entering the next step;
② A matrix of n × m is constructed, the matrix element d (q)i，cj) Denotes qiAnd cjDetermining the similarity between each point of the sequence Q and each point of the sequence C, wherein the smaller the distance is, the higher the similarity is;
thirdly, calculating the total accumulated distance of the n multiplied by m matrix according to a state transfer equation, and recording the accumulated times t;
fourthly, the formula adopted for calculating the dynamic time regular value is as follows:
Dis(i，j)＝d(qi，cj)+min{Dis(i-1，j-1)，Dis(i，j-1)，Dis(i-1，j)} (11)
in the formula, Dis (n, m) is the total accumulated distance of the matrix, t is the accumulated times, and Dis (i, j), Dis (i-1, j-1), Dis (i, j-1) and Dis (i-1, j) are the total accumulated distance of the i multiplied by j matrix, (i-1) multiplied by (j-1) the total accumulated distance of the matrix, i multiplied by (j-1) the total accumulated distance of the matrix, and (i-1) multiplied by j the total accumulated distance of the matrix;
the formula used to determine the distances between the matrix elements is:
d(qi，cj)＝(qi-cj)2(13)
in the formula, d (q)i，cj) Is qiAnd cjThe distance between them.
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