CN107120111B - Oil reservoir inter-well communication degree evaluation method and system based on multi-fractal - Google Patents

Oil reservoir inter-well communication degree evaluation method and system based on multi-fractal Download PDF

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CN107120111B
CN107120111B CN201710289327.9A CN201710289327A CN107120111B CN 107120111 B CN107120111 B CN 107120111B CN 201710289327 A CN201710289327 A CN 201710289327A CN 107120111 B CN107120111 B CN 107120111B
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张冬梅
金佳琪
汪海
姜鑫维
刘远兴
杨宏湘
程迪
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Abstract

The invention relates to an oil reservoir inter-well communication degree evaluation method and system based on multi-fractal, wherein the method comprises the following steps of S1, reading and preprocessing production dynamic data of an injection and production well to obtain a time sequence of the production dynamic data; s2, calculating the time sequence of the production dynamic data with the collection time in the preset period after water injection by adopting a multi-fractal algorithm to obtain the multi-fractal spectrum parameters of the production dynamic data with the collection time in the preset period after water injection; s3, performing waveform feature calculation on the time sequence of the production dynamic data with the collection time within a period of time span in a preset period after water injection to qualitatively evaluate the dynamic connectivity among wells; and S4, on the basis of qualitative evaluation of the inter-well dynamic communication degree, taking the multi-fractal spectrum parameters as injection-production split coefficients, and quantitatively evaluating the inter-well dynamic communication degree through the injection-production split coefficients. The method can be used for depicting the communication degree between oil reservoir wells in a fine granularity.

Description

Oil reservoir inter-well communication degree evaluation method and system based on multi-fractal
Technical Field
The invention relates to the field of inter-well connectivity evaluation in oil reservoir development, in particular to an oil reservoir inter-well connectivity evaluation method and system based on multi-fractal.
Background
Reservoir interwell connectivity can be divided into static connectivity and dynamic connectivity. Generally, static connectivity refers to the connectivity result obtained by applying geological and geophysical exploration methods, and is determined by reservoir geological characteristics and reservoir characteristics. Due to the characteristic that reservoir layers of the fracture-cavity oil reservoirs are complex, stratum connectivity obtained by traditional geological and geophysical exploration (such as well logging, well testing, geological modeling and other methods) belongs to a static category, and connectivity of fracture-cavity bodies cannot be effectively known. And the dynamic connectivity among the oil reservoir wells refers to the communication degree of reservoir fluid among the wells after the oil reservoir is developed. The quantitative inversion of the interwell connectivity by using production data is an important method, and the basic principle is that an oil reservoir is regarded as an injection and production system with a water injection well for sending stimulation and a surrounding oil well for receiving stimulation, and the water injection well, the oil well and an interwell reservoir form a complex dynamic balance system. After water is injected into the oil layer, crude oil or formation water must be displaced to the oil well through the communicated reservoir, so as to maintain the production of the oil well. The fluctuation of the liquid production of the oil well caused by the change of the water injection amount of the water injection well is the characteristic reflection of the communication in the oil-water well layer, the fluctuation amplitude of the liquid production of the oil well is related to the communication degree of the oil-water well, the better the communication is, the more obvious the liquid production fluctuation is, so the communication degree between injection and production can be quantitatively represented by a mathematical method, and the model mainly comprises a model based on a multivariate linear regression model, a system analysis model, an elastic compression model, a capacitance model, a neural network and the like.
Through the statistical analysis of the characteristics of the production dynamic data, the production dynamic data has certain nonlinear characteristics, and the characteristics are as follows:
(1) the distribution of the oil reservoir production dynamic data has a sharp peak state of a fat tail, and the variance is uncertain or infinite and does not conform to normal distribution.
(2) In the process of changing production data, water content, oil production and the like do not follow random variables of random walk, a unique optimization solution cannot be obtained through calculation, and a plurality of possible solutions exist.
(3) The production dynamic data has long memory (long-term memory, namely long-range correlation and state persistence) and trend influenced by historical information, has global ordering (determination) and local disorder (randomness), and has feedback effect.
In conclusion, the changes of the oil reservoir production data are not independent, do not follow random walk, and do not follow normal distribution, and the properties are just the characteristics of a nonlinear power system, while the traditional multiple linear regression model, system analysis model, elastic compression model, capacitance model and the like can not describe the characteristics in a fine-grained manner.
Disclosure of Invention
The invention aims to solve the technical problem of providing an oil reservoir inter-well communication degree evaluation method and system based on multi-fractal, which can depict the oil reservoir inter-well communication degree in a fine-grained manner.
The technical scheme for solving the technical problems is as follows: an evaluation method of the communication degree between oil deposit wells based on multi-fractal comprises the following steps,
s1, reading and preprocessing the production dynamic data of the injection and production well to obtain a time sequence of the production dynamic data;
s2, calculating the time sequence of the production dynamic data with the collection time in the preset period after water injection by adopting a multi-fractal algorithm to obtain the multi-fractal spectrum parameters of the production dynamic data with the collection time in the preset period after water injection;
s3, performing waveform feature calculation on the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection to obtain the waveform amplitude of the production dynamic data with the collection time within the period of time span in the preset period after water injection, and qualitatively evaluating the inter-well dynamic connectivity through the waveform amplitude;
s4, on the basis of qualitative evaluation of inter-well dynamic communication degree, taking the multi-fractal spectrum parameters of production dynamic data with the collection time in a preset period after water injection as injection-production split coefficients, and quantitatively evaluating the inter-well dynamic communication degree through the injection-production split coefficients.
The invention has the beneficial effects that: the method of the invention inverts the connectivity among wells based on the production dynamic data, and more effectively reflects the actual communication situation among wells; meanwhile, the multi-fractal method is a method for researching overall characteristics from local system, and mainly calculates probability distribution by means of a statistical physics method.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, as S1, specifically,
s11, reading the production dynamic data of the injection and production well;
s12, screening out production dynamic data corresponding to initial water injection amount dates in the starting time of the water injection sections;
and S13, bringing the screened production dynamic data into a corresponding data preprocessing algorithm for calculation to obtain time sequences of the production dynamic data respectively corresponding to and matched with initial water injection amount dates in the starting time of a plurality of water injection sections.
Further, the dynamic production data comprises water injection amount, oil production amount, water content and water injection section starting time.
Further, in S12, if there is a data loss in the selected water injection amount, oil production amount, water content, and water content, the method further includes performing data compensation on the selected water injection amount, oil production amount, water content, and water content.
The beneficial effect of adopting the further scheme is that: the accuracy of subsequent calculation can be effectively improved by the data completion processing.
And further, carrying out interpolation processing on the data to fill up the missing data by adopting denoising to fill up the missing data or adopting EEMD time-frequency decomposition and combining with a support vector machine to model and predict each sub-signal.
The beneficial effect of adopting the further scheme is that: if a small amount of data is lost due to shut-in, well repair and the like, denoising and filling up the lost data; for longer data loss, EEMD time-frequency decomposition is adopted, and the interpolation processing of data is realized by modeling and predicting each sub-signal by combining a support vector machine; different filling processing methods are adopted for the data with different missing degrees, so that the filled data are more real and effective.
Further, as S2, specifically,
s21, dividing the time sequence of the production dynamic data with the collection time in the preset period after water injection into N (N is 1/delta) non-overlapping intervals by using a time scale delta;
s22, calculating the probability measure P of the sample in each intervali(δ)；
S23, according to the probability measure P in all the intervalsi(delta) and weight factor q to obtain distribution function chi of multi-fractal algorithmq(δ)；
S24, right partition function χq(delta) and time scale delta are logarithmized to obtain ln chiq(delta) -ln delta double logarithmic curves, and estimating ln χ by least square fitting algorithmqThe slope τ (q) of the log-log curves (δ) to ln δ;
s25, Legendre transformation is carried out on the slope tau (q) and the weight factor q to obtain a singular index α and a multi-fractal spectrum function f (α);
s26, drawing graphs from f (α) - α and carrying out integral treatment on the graphs to obtain a multi-fractal spectrum parameter of the production dynamic data of which the acquisition time is in a preset period after water injection
Figure BDA0001281535370000041
The beneficial effect of adopting the further scheme is that: the multi-fractal is a set formed by infinite scale indexes defined on a fractal structure, and the difference of the fractal structure is described by a spectrum functionThe local conditions of the fractal structure, or special structural behaviors and characteristics caused by different levels of the fractal structure in the evolution process; by calculating a probability measure PiPartition function χq(delta) and quality index tau (q), using least square regression fitting to obtain α and multi-fractal spectrum function f (α), in the graphs from f (α) to α, delta α (i.e. α)max-αmin) Indicating the degree of uniformity of the data, the larger Δ α indicates the more irregular curve, f (α) indicates the fractal dimension of the section, i.e., the degree of irregularity of the data in the section, for each range corresponding to the singular index α, and f (α) to α are graphically integrated
Figure BDA0001281535370000042
The results of (a) characterize interwell connectivity as the complexity of the fluctuations.
Further, the time series of the production dynamic data, the collection time of which is in the preset period after water injection in the step S21, includes the time series of oil production and the time series of water content; in the step S26, the multi-fractal spectrum parameters of the production dynamic data with the collection time in the preset period after water injection comprise the multi-fractal spectrum parameters of oil production and the multi-fractal spectrum parameters of water content.
Further, in S3, the specific steps of calculating the waveform characteristics of the time series of the production dynamic data whose collection time is within a time span of the preset period after water injection are,
s31, selecting a time sequence of the production dynamic data with the collection time within a period of time span in a preset period after water injection;
and S32, calculating the fluctuation amplitude of the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection in the local window by taking the time window as a unit, and recording the maximum fluctuation amplitude of the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection by adopting a sliding time window, wherein the maximum fluctuation amplitude of the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection is the waveform amplitude of the production dynamic data with the collection time within a period of time span in the preset period after water injection.
Further, the production dynamic data of which the collection time is within a time span in the preset period after water injection in S31 is the water content, and the fluctuation amplitude of the production dynamic data of which the collection time is within a time span in the preset period after water injection in S32 is the fluctuation amplitude of the water content.
The beneficial effect of adopting the further scheme is that: theoretically, production data of communicated wells have certain change characteristics after water injection, and the larger the fluctuation characteristic change is, the stronger the dynamic communication degree among the wells is under the same water injection condition; the waveform characteristics of the production data of the oil well after water injection reflect the communication degree among wells to a certain extent, the fluctuation change of the production data is comprehensively influenced by factors such as water injection driving, pumping, stroke, frequency of stroke and the like under normal conditions, the energy change of a production well generated in the production process is reflected, and the maximum fluctuation often shows irregularity under abnormal conditions such as frequent shut-down of the well, oil nozzle replacement, well flushing (referred to as working system change) or incomplete production data loss (poor data quality); in various production data, the fluctuation condition of the water content after water injection is most obviously influenced by water injection, so that the inter-well communication relation can be judged to a certain extent by calculating the fluctuation size of the water content before and after water injection.
Based on the oil reservoir inter-well communication degree evaluation method based on the multi-fractal, the invention also provides an oil reservoir inter-well communication degree evaluation system based on the multi-fractal.
An oil reservoir inter-well communication degree evaluation system based on multi-fractal comprises a pretreatment module, a multi-fractal spectrum parameter generation module, a waveform characteristic calculation module and an inter-well dynamic communication degree quantitative evaluation module,
the preprocessing module is used for reading and preprocessing the production dynamic data of the injection and production well to obtain a time sequence of the production dynamic data;
the multi-fractal spectrum parameter generation module is used for calculating the production dynamic data with the collection time in the preset period after water injection by adopting a multi-fractal algorithm to obtain the multi-fractal spectrum parameter of the production dynamic data with the collection time in the preset period after water injection;
the waveform feature calculation module is used for performing waveform feature calculation on the production dynamic data with the collection time within a period of time span in a preset period after water injection to obtain a waveform amplitude of the production dynamic data with the collection time within the period of time span in the preset period after water injection, and qualitatively evaluating the inter-well dynamic connectivity through the waveform amplitude;
and the inter-well dynamic communication degree quantitative evaluation module is used for quantitatively evaluating the inter-well dynamic communication degree through the injection and production split number by taking the multi-fractal spectrum parameter of the production dynamic data with the acquisition time in the preset period after water injection as the injection and production split coefficient on the basis of the qualitative evaluation of the inter-well dynamic communication degree.
The invention has the beneficial effects that: the system of the invention inverts the connectivity among wells based on the production dynamic data, and more effectively reflects the actual communication condition among wells; meanwhile, the multi-fractal method is a method for researching overall characteristics from local parts of the system, and mainly calculates the probability distribution condition by means of a statistical physics method.
Drawings
FIG. 1 is an overall flow chart of an evaluation method of the oil reservoir inter-well connectivity degree based on multi-fractal in the invention;
FIG. 2 is a detailed flowchart of S1 in the method for evaluating the connectivity between wells of an oil reservoir based on multi-fractal disclosed by the invention;
FIG. 3 is a detailed flowchart of S2 in the method for evaluating the connectivity between wells of an oil reservoir based on multi-fractal disclosed by the invention;
FIG. 4 is a detailed flowchart of S3 in the method for evaluating the degree of connectivity between wells of an oil reservoir based on multi-fractal according to the present invention;
FIG. 5 is a geological profile of a region of a TK634 well group;
FIG. 6 is a graph showing the relationship between the cleavage of TK634 well groups obtained by the multi-fractal method;
FIG. 7 is a geological profile of the TK617CH well group region;
FIG. 8 is a graph showing the relationship between the TK617CH well group split obtained by the multi-fractal method;
FIG. 9 is a structural block diagram of an evaluation system for reservoir well-to-well connectivity based on multi-fractal.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, a method for evaluating the connectivity between wells of a reservoir based on multi-fractal comprises the following steps,
s1, reading and preprocessing the production dynamic data of the injection and production well to obtain a time sequence of the production dynamic data;
s2, calculating the time sequence of the production dynamic data with the collection time in the preset period after water injection by adopting a multi-fractal algorithm to obtain the multi-fractal spectrum parameters of the production dynamic data with the collection time in the preset period after water injection;
s3, performing waveform feature calculation on the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection to obtain the waveform amplitude of the production dynamic data with the collection time within the period of time span in the preset period after water injection, and qualitatively evaluating the inter-well dynamic connectivity through the waveform amplitude;
s4, on the basis of qualitative evaluation of inter-well dynamic communication degree, taking the multi-fractal spectrum parameters of production dynamic data with the collection time in a preset period after water injection as injection-production split coefficients, and quantitatively evaluating the inter-well dynamic communication degree through the injection-production split coefficients.
Next, S1 to S4 will be explained in detail.
As shown in fig. 2, S1 specifically includes:
s11, reading the production dynamic data of the injection and production well; wherein the dynamic production data comprises water injection amount, oil production amount, water content and water injection section starting time;
s12, screening dynamic production data which are correspondingly matched with initial water injection quantity dates in the starting time of a plurality of water injection sections, wherein if the screened water injection quantity, oil production quantity, water content and water content have data loss, the screened water injection quantity, oil production quantity, water content and water content are subjected to data complementing treatment, the data complementing treatment method comprises the steps of removing noise and complementing missing data if a small amount of data is lost due to shut-in and well workover, and the like ①, and ②, for longer data loss, carrying out EEMD time frequency decomposition and combining with a support vector machine to realize data interpolation treatment on each sub-signal modeling prediction to complement the data;
and S13, bringing the screened production dynamic data into a corresponding data preprocessing algorithm for calculation to obtain time sequences of the production dynamic data respectively corresponding to and matched with initial water injection amount dates in the starting time of a plurality of water injection sections.
At S2, the predetermined period is two months, that is, the study data object of this embodiment is a time series of water cut and oil production for the injection and production well 60 days (i.e., two months) after each water injection (the water injection is generally effective within two months according to the tracer test case); the multi-fractal is a set formed by infinite scale indexes defined on a fractal structure, and different local conditions on the fractal structure or special structural behaviors and characteristics caused by different levels of the fractal structure in the evolution process are described through a spectrum function. The calculation of the multi-fractal spectrum parameters adopts a box counting method, and the calculation flow is shown in figure 3:
s21, covering the time sequence of the production dynamic data whose collection time is in the preset period after water filling with a small one-dimensional box with a time scale δ (δ < 1), i.e. dividing the time sequence of the production dynamic data whose collection time is in the preset period after water filling into N (N is 1/δ) non-overlapping intervals with the time scale δ; the time sequence of the production dynamic data with the collection time in the preset period after water injection comprises the time sequence of oil production and the time sequence of water content;
s22, calculating each regionProbability measure P of inter-samplesi(δ); wherein, note Ii(delta) is the sum of the water content or oil production values of all samples in the ith interval on the time scale delta, and the probability measure P for the sample in the ith intervali(δ) is:
Figure BDA0001281535370000091
if the time series of water content or the time series of oil production has a multi-fractal characteristic, the following power law relationship is satisfied in the scale-free interval:
Pi(δ)∝δα(2)
wherein α is the mark P corresponding to the ith intervaliSingular index of size (delta), reflecting PiThe singularity degree of each interval is different along with delta change;
the number of intervals with the same α time scale of delta is recorded as Nα(delta) as delta decreases, Nα(δ) is increasing, and if the time series of water content or the time series of oil production has a multi-scale relationship, the following power law relationship is satisfied in the non-scale interval:
Nα(δ)∝δ-f(α)(δ→0) (3)
wherein the fractal multiple spectrum function f (α) represents each PiThe speed at which the number of elements in the group with the same α subset increases as δ decreases;
s23, according to the probability measure P in all the intervalsi(delta) and weight factor q to obtain distribution function chi of multi-fractal algorithmq(δ); wherein, a distribution function chi of the multi-fractal algorithm is definedq(δ) q-order moment normalized to water or oil production:
Figure BDA0001281535370000101
χq(delta) is a reflection of normalized water or oil production Pi(delta) statistic of the nonuniformity, q is a weighting factor, and the purpose of hierarchically researching the fine structure is achieved through weighting processing;
s24, right partition function χq(delta) and time scale delta are logarithmized to obtain ln chiq(delta) -ln delta double logarithmic curves, and estimating ln χ by least square fitting algorithmqThe slope τ (q) of the log-log curves (δ) to ln δ; specifically, χ is known from fractal self-similarityq(δ) and δ satisfy the following power-law relationship in the non-scale interval:
χq(δ)∝δτ(q)(5)
logarithm is obtained on the two sides of the above formula to obtain ln χqCurves (delta) to ln delta, where the slope τ (q) of the curve is mass index and τ (q) can pass through ln χqPerforming least square fitting on linear points in the log-log curves of (delta) -ln delta to estimate;
s25, performing Legendre transformation on the slope tau (q) and the weighting factor q to obtain a singular index α and a multi-fractal spectrum function f (α), specifically, obtaining α (q) and f (α) from q and tau (q) by Legendre transformation (Legendre transformation) on the premise that a q-tau (q) curve is known:
Figure BDA0001281535370000102
s26, drawing graphs from f (α) - α and carrying out integral treatment on the graphs to obtain a multi-fractal spectrum parameter of the production dynamic data of which the acquisition time is in a preset period after water injection
Figure BDA0001281535370000103
The multi-fractal spectrum parameters of the production dynamic data, the collection time of which is in the preset period after water injection, comprise the multi-fractal spectrum parameters of oil production and the multi-fractal spectrum parameters of water content.
In summary, by calculating the probability measure PiPartition function χq(delta) and quality index tau (q), using least square regression fitting to obtain α and multi-fractal spectrum function f (α), in the graphs from f (α) to α, delta α (i.e. α)max-αmin) Indicating the degree of uniformity of the data, the larger the Δ α, the more irregular the curve, and for each singularity index αCorresponding range, f (α) representing the fractal dimension of the interval, i.e. the uneven degree of the data in the interval, and f (α) - α graphical integration
Figure BDA0001281535370000111
The results of (a) characterize the degree of interwell connectivity as the complexity of the fluctuations.
In S3, calculating the maximum fluctuation of production data such as the water content in a period of time span in a preset period after water injection, and selecting the maximum value of the difference between the wave crest and the wave trough as the local maximum fluctuation; suppose that the time span is selected as T and the time window size is T (T)<T), solving the local maximum fluctuation value of the water content in T-T +1 time windows, wherein the local maximum fluctuation of the ith window is recorded as deltai,δmax＝max{δ1，δ2，...，δT-t+1The maximum fluctuation value in a certain time span after water injection; deltaiThe specific judgment is shown in fig. 4:
in S3, the specific steps of calculating the waveform characteristics of the time series of the production dynamic data whose collection time is within a time span of the preset period after water injection are,
s31, selecting a time sequence of the production dynamic data with the collection time within a period of time span in a preset period after water injection; acquiring production dynamic data with the acquisition time within a period of time span in a preset period after water injection, wherein the production dynamic data is the water content; if the oil well to be judged has production system interference (such as changing the size of a choke, washing the well, stopping the well and other operations) in the water content fluctuation period, determining whether to screen the well according to specific conditions;
s32, calculating the fluctuation amplitude of the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection in the local window by taking the time window as a unit, and recording the maximum fluctuation amplitude of the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection by adopting a sliding time window, wherein the maximum fluctuation amplitude of the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection is the waveform amplitude of the production dynamic data with the collection time within a period of time span in the preset period after water injection; in addition, the fluctuation amplitude of the production dynamic data with the collection time within a period of time span in the preset period after water injection is the fluctuation amplitude of the water content.
In S3, theoretically, the dynamic data of the production of the injection and production well after water injection has certain change characteristics, and the larger the fluctuation characteristic change is, the stronger the dynamic communication degree among wells is under the same water injection condition; the waveform characteristics of the production data of the production well after water injection reflect the inter-well communication degree to a certain extent; under normal conditions, the fluctuation change of the well is comprehensively influenced by factors such as water injection driving, pumping, stroke frequency and the like, and the energy change of a production well generated in the production process is reflected; under abnormal conditions, such as frequent shut-in of the well, change of oil nozzles, well flushing (referred to as working regime change) or incomplete production data loss (poor data quality), the maximum fluctuation often shows irregularity; in various production data, the fluctuation of the water content after water injection is most obviously influenced by water injection, so that the inter-well communication relation can be qualitatively judged to a certain extent by calculating the fluctuation of the water content before and after water injection.
S4, carrying out quantitative evaluation on the communication degree by adopting a multi-fractal method, and specifically comprising the following steps: on the basis of qualitative calculation of the inter-well communication degree in S3, the multi-fractal spectrum parameters of oil production and the multi-fractal spectrum parameters of water content obtained in S2 are integrated to serve as injection and production splitting numbers, and splitting conditions of water of each production well after water injection are reflected; the greater the number of cleavages, the greater the degree of dynamic communication between wells in general.
The method aims to establish a mapping relation between oil reservoir production dynamic data and the inter-well communication degree and quantitatively judge the strength relation of the inter-well communication degree by calculating the multi-fractal spectrum parameters. The basic principle is that the change of the injection amount of a water injection well often causes the fluctuation of the liquid production amount of surrounding oil wells, the larger the fluctuation range is, the better the communication degree is, and the communication degree between injection and production is quantified and represented by a mathematical method aiming at production dynamic data. The dynamic changes of production dynamic data of the fracture-cavity oil reservoir, such as water content, oil production and the like, do not follow random walk, nor do the dynamic changes follow normal distribution, and the whole distribution presents the characteristic of a nonlinear power system of a peak fertilizer tail, so that the traditional statistical method and a time sequence model cannot effectively depict a complex injection and production mechanism. The multi-fractal method is a method for researching overall characteristics from system local, calculates the probability distribution condition by means of a statistical physics method, adopts multi-fractal spectrum parameters to describe the change characteristics of production data after various working systems of continuous water injection, well shut-in and well flushing are changed, excavates the correlation between water content and oil production fluctuation and water injection, and quantitatively analyzes the communication degree between oil deposit injection and production wells. The algorithm is based on production dynamic data, utilizes a statistical physics method to calculate probability distribution to carry out quantitative analysis on the communication strength between injection and production, and calculates the water injection effect condition through a multi-fractal algorithm on the basis of recognizing the flow direction of the injected fluid. The method solves the problem that the conventional inter-well communication judgment method influences normal production construction operation, reduces the cost for judging the strength of injection-production communication, and simplifies the workload for judging the conventional injection-production communication relation.
The concrete application of the method is described by taking a tower river oil field fracture-cavity type oil reservoir as an example:
the tower river oil field fracture-cave oil reservoir is a special oil reservoir mainly comprising a karst cave and a fracture cave. The holes, holes and slits will form a plurality of reservoir types according to different modes and scales, and have strong heterogeneity characteristics. Mainly comprises a sandstone reservoir of a triassic system and a carbolite system and a carbonate reservoir of an Ordovician system. The oil field reserves mainly come from Ordovician carbonate reservoirs, the exploratory reserves of the Ordovician reservoirs account for 81.7 percent of the total exploratory reserves of the oil field, and the main oil producing layer of the oil field is an Ordovician fracture-type carbonate stratum at present. The study area contained 296 wells and the historical data had more than 15 years of data (from 2001 to 2015). The algorithm operating environment is as follows: windows 7 system, 4G run memory, 2.94GHz Pentium (R) Dual-Core CPU; operating a tool: VS 2010; programming language: C/C + +. The experimental test object selects four, six and seven units of the Tahe oil field.
The experimental test object selects 2 injection-production well groups of four, six and seven units of the Tahe oil field, namely TK634 (23/4 in 2009) and TK617CH (27/11 in 2007). And based on production data information, selecting production data 60 days after water injection of the 2 injection and production well groups, and performing multi-fractal spectrum parameter calculation on the data to obtain a result of the inter-well communication degree.
Tracer verification is a traditional method for judging the inter-well communication relationship, and the inter-well communication relationship is determined by putting a tracer, sampling in surrounding wells, analyzing samples and the like. The method takes an actual injection and production well tracer experiment as a reference to verify the judgment of the connectivity of a calculation result. The multi-fractal results and tracer results were compared as follows:
(1) TK634 well group (2009, 23 months 4)
The TK634 well is a development well deployed on the north 6 construction of the pasture in the tahey oilfield 6 region, and the TK634 well is located at the junction of the TK7-607 cell and the S67 cell, and the T606 cell area in the northeast region as shown in fig. 5. The well is put into operation in 5-24 days in 2002, no water is produced during the operation, the period of water-free oil production is long, 15.5626 ten thousand tons of accumulated produced liquid and 12.4326 ten thousand tons of produced oil are produced so far. At present, 31.2 tons of daily produced water liquid, 2.3 tons of daily produced oil and 92.5 percent of water are contained.
The water flooding splitting relation pair of the TK634 well group obtained by the multi-fractal method is shown in the following table 1:
TABLE 1
Figure BDA0001281535370000141
The TK634 well component split relation obtained by the multi-fractal method is shown in figure 6;
by table 1 and fig. 6, the tracer water distribution is consistent with the connected parameter characteristics of the multi-fractal method. Through inspection, the water distribution condition of the tracer agent of the well group is consistent with the proportion sequence of the connected parameters obtained by the multi-fractal, and the proportion of the water distribution condition of the tracer agent of the well group is very close to that of the connected parameters obtained by the multi-fractal, which indicates that the well connectivity result is relatively consistent with the tracer agent test result.
(2) TK617CH well group (11 months and 27 days in 2007)
The TK617CH well is located in the northeast of the Tahe oil field 6 region, and is located at the No. 2 structure high point of the pasture, and the production layer is O1-2 y. The regional fracture-cavity unit is shown in fig. 7, the natural energy of part of oil wells in the unit is large, the waterless exploitation period is long, the initial yield is high, the oil wells in the unit have emptying phenomena of different degrees in the drilling process, and the reservoir in the unit is an oil reservoir with karst caves and developed seams.
The water injection splitting relationship of the TK617CH well group obtained by the multi-fractal method is shown in the following table 2:
TABLE 2
Figure BDA0001281535370000142
FIG. 8 shows the TK617CH well group split relationship obtained by the multi-fractal method as shown in FIG. 8;
from table 2 and fig. 8, it can be seen that the tracer water distribution is more consistent with the connected parameter characteristics of the multi-fractal method. Through inspection, the water distribution condition of the tracer agent of the well group is consistent with the proportion sequence of the connected parameters obtained by the multi-fractal, and the proportion of the water distribution condition of the tracer agent of the well group is very close to that of the connected parameters obtained by the multi-fractal, which indicates that the well connectivity result is relatively consistent with the tracer agent test result.
In general, from the results and analysis of the comparison chart, the experimental result of the multi-fractal research on the degree of communication between wells is consistent with the result of the tracer, the actual condition of the oil field is basically met, the result calculated by the novel method is accurate and reliable, and the method has certain practical value and plays a certain role in promoting the development of the oil reservoir.
Based on the oil reservoir inter-well communication degree evaluation method based on the multi-fractal, the invention also provides an oil reservoir inter-well communication degree evaluation system based on the multi-fractal.
As shown in FIG. 9, an evaluation system for the connectivity between wells of an oil reservoir based on multi-fractal comprises a preprocessing module, a multi-fractal spectrum parameter generation module, a waveform characteristic calculation module and a quantitative evaluation module for the dynamic connectivity between wells,
the preprocessing module is used for reading and preprocessing the production dynamic data of the injection and production well to obtain a time sequence of the production dynamic data;
the multi-fractal spectrum parameter generation module is used for calculating the production dynamic data with the collection time in the preset period after water injection by adopting a multi-fractal algorithm to obtain the multi-fractal spectrum parameter of the production dynamic data with the collection time in the preset period after water injection;
and the waveform feature calculation module is used for performing waveform feature calculation on the production dynamic data with the acquisition time within a period of time span in the preset period after water injection to obtain a waveform amplitude of the production dynamic data with the acquisition time within the period of time span in the preset period after water injection, and qualitatively evaluating the inter-well dynamic connectivity through the waveform amplitude.
The system of the invention inverts the connectivity among wells based on the production dynamic data, and more effectively reflects the actual communication condition among wells; meanwhile, the multi-fractal method is a method for researching overall characteristics from local parts of the system, and mainly calculates the probability distribution condition by means of a statistical physics method.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. An oil reservoir inter-well communication degree evaluation method based on multi-fractal is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1, reading and preprocessing the production dynamic data of the injection and production well to obtain a time sequence of the production dynamic data;
s2, calculating the time sequence of the production dynamic data with the collection time in the preset period after water injection by adopting a multi-fractal algorithm to obtain the multi-fractal spectrum parameters of the production dynamic data with the collection time in the preset period after water injection;
s3, performing waveform feature calculation on the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection to obtain the waveform amplitude of the production dynamic data with the collection time within a period of time span in the preset period after water injection, and qualitatively evaluating the inter-well dynamic connectivity through the waveform amplitude; acquiring production dynamic data with the acquisition time within a period of time span in a preset period after water injection, wherein the production dynamic data is the water content;
s4, on the basis of qualitative evaluation of inter-well dynamic communication degree, taking the multi-fractal spectrum parameters of production dynamic data with the collection time in a preset period after water injection as injection-production split coefficients, and quantitatively evaluating the inter-well dynamic communication degree through the injection-production split coefficients.
2. The method for evaluating the communication degree between wells of an oil reservoir based on multi-fractal, which is characterized by comprising the following steps: the specific example of S1 is,
s11, reading the production dynamic data of the injection and production well;
s12, screening out production dynamic data corresponding to initial water injection amount dates in the starting time of the water injection sections;
and S13, bringing the screened production dynamic data into a corresponding data preprocessing algorithm for calculation to obtain time sequences of the production dynamic data respectively corresponding to and matched with initial water injection amount dates in the starting time of a plurality of water injection sections.
3. The method for evaluating the communication degree between wells of an oil reservoir based on multi-fractal according to claim 2, characterized in that: the dynamic production data comprises water injection amount, oil production amount, water content and water injection section starting time.
4. The method for evaluating the communication degree between oil reservoir wells based on multi-fractal, which is characterized by comprising the following steps: in S12, if there is a data loss in the water injection amount, oil production amount, water content, and water content that have been screened, the method further includes performing data compensation on the water injection amount, oil production amount, water content, and water content that have been screened.
5. The method for evaluating the degree of communication between wells of an oil reservoir based on multi-fractal, which is characterized by comprising the following steps: and (3) carrying out denoising and filling missing data or EEMD time-frequency decomposition and combining with a support vector machine to model and predict each sub-signal to realize interpolation processing and filling missing data.
6. The method for evaluating the connectivity degree between oil deposit wells based on multi-fractal according to any one of claims 3 to 5, wherein: the specific example of S2 is,
s21, dividing the time sequence of the production dynamic data with the collection time in the preset period after water injection into N (N is 1/delta) non-overlapping intervals by using a time scale delta;
s22, calculating the probability measure P of the sample in each intervali(δ)；
S23, according to the probability measure P in all the intervalsi(delta) and weight factor q to obtain distribution function chi of multi-fractal algorithmq(δ)；
S24, right partition function χq(delta) and time scale delta are logarithmized to obtain ln chiq(delta) -ln delta double logarithmic curves, and estimating ln χ by least square fitting algorithmqThe slope τ (q) of the log-log curves (δ) to ln δ;
s25, Legendre transformation is carried out on the slope tau (q) and the weight factor q to obtain a singular index α and a multi-fractal spectrum function f (α);
s26, drawing graphs from f (α) - α and carrying out integral treatment on the graphs to obtain a multi-fractal spectrum parameter of the production dynamic data of which the acquisition time is in a preset period after water injection
Figure FDA0002383790430000021
7. The method for evaluating the degree of communication between wells of an oil reservoir based on multi-fractal according to claim 6, wherein: the time sequence of the production dynamic data with the collection time in the preset period after water injection in the S21 comprises the time sequence of oil production and the time sequence of water content; in the step S26, the multi-fractal spectrum parameters of the production dynamic data with the collection time in the preset period after water injection comprise the multi-fractal spectrum parameters of oil production and the multi-fractal spectrum parameters of water content.
8. The method for evaluating the connectivity degree between oil deposit wells based on multi-fractal according to any one of claims 3 to 5, wherein: in S3, the specific steps of calculating the waveform characteristics of the time series of the production dynamic data whose collection time is within a time span of the preset period after water injection are,
s31, selecting a time sequence of the production dynamic data with the collection time within a period of time span in a preset period after water injection;
and S32, calculating the fluctuation amplitude of the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection in the local window by taking the time window as a unit, and recording the maximum fluctuation amplitude of the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection by adopting a sliding time window, wherein the maximum fluctuation amplitude of the time sequence of the production dynamic data with the collection time within a period of time span in the preset period after water injection is the waveform amplitude of the production dynamic data with the collection time within a period of time span in the preset period after water injection.
9. The method for evaluating the communication degree between wells of an oil reservoir based on multi-fractal, which is characterized by comprising the following steps: the production dynamic data with the collection time within a period of time span in the preset period after water injection in the S31 is the water content, and the fluctuation amplitude of the production dynamic data with the collection time within a period of time span in the preset period after water injection in the S32 is the fluctuation amplitude of the water content.
10. An oil reservoir inter-well communication degree evaluation system based on multi-fractal is characterized in that: comprises a preprocessing module, a multi-fractal spectrum parameter generating module, a waveform characteristic calculating module and a quantitative evaluation module for the dynamic communication degree between wells,
the preprocessing module is used for reading and preprocessing the production dynamic data of the injection and production well to obtain a time sequence of the production dynamic data;
the multi-fractal spectrum parameter generation module is used for calculating the production dynamic data with the collection time in the preset period after water injection by adopting a multi-fractal algorithm to obtain the multi-fractal spectrum parameter of the production dynamic data with the collection time in the preset period after water injection;
the waveform feature calculation module is used for performing waveform feature calculation on the production dynamic data with the collection time within a period of time span in a preset period after water injection to obtain a waveform amplitude of the production dynamic data with the collection time within the period of time span in the preset period after water injection, and qualitatively evaluating the inter-well dynamic connectivity through the waveform amplitude; acquiring production dynamic data with the acquisition time within a period of time span in a preset period after water injection, wherein the production dynamic data is the water content;
and the inter-well dynamic communication degree quantitative evaluation module is used for quantitatively evaluating the inter-well dynamic communication degree through the injection and production split number by taking the multi-fractal spectrum parameter of the production dynamic data with the acquisition time in the preset period after water injection as the injection and production split coefficient on the basis of the qualitative evaluation of the inter-well dynamic communication degree.
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