CN1250967C - Dynamic chromatographic monitoring method for layered yield contribution of multi-layer mixed extracting crude oil - Google Patents
Dynamic chromatographic monitoring method for layered yield contribution of multi-layer mixed extracting crude oil Download PDFInfo
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Abstract
The present invention discloses a monitoring method for a dynamic chromatogram of the layered yield contribution of multi-layer mixed crude oil production. The method comprises the following steps: (1) taking and mixing crude oil from each layer of a mixing production well according to a setting proportion to form an analog-hybrid oil sample, and respectively carrying out chromatogram quantitative analysis to obtain a series of characteristic fingerprint parameters; (2) utilizing the parameters obtained in step (1) to build a mathematical model by an intelligence learning algorithm of a nonlinear artificial neural network; (3) collecting crude oil to be measured at the wellhead of the mixing production well to carry out chromatogram quantitative analysis, and then obtaining corresponding characteristic fingerprint data; (4) introducing the serial characteristic fingerprint data obtained in step (3) into the mathematical model built in step (2) to calculate and obtain the yield contribution of the crude oil from each layer of the mixing production well. The method has the advantages of easy operation, short time for obtaining a monitoring result, low cost and high accuracy, has important significance for improving the calculation accuracy of the layered yield contribution of the multi-layer mixing production well, and provides an effective method for the analog calculation of the layered yield contribution of the mixing production well with more than three layers.
Description
Technical field
The present invention relates to a kind of in oil-field development is produced the monitoring method to oil reservoir, be specifically related to the method that single-zone productivity contribution in the multilayer commingled oil well is monitored.
Background technology
In oil-field development was produced, in order to optimize crude production, most of oil wells all were to mix layer exploitation more than three layers, because the influence of nonuniformity such as the thickness of oil bearing reservoir own, factor of porosity, permeability, the oil offtake of each layering is different.Especially in middle and later periods of waterflooding reservoir, in order to implement " steady oil control water ", to improve recovery ratio, will strengthen dynamic monitoring, the enforcement layer-management of oil reservoir, grasp the productivity contribution of multilayer commingled oil well layering immediately, guarantee the effectively correct of comprehensive control measures.
At present, monitoring methods such as oil field field by using water detection flowmeter method, tubing string method of testing, annulus logging method, radioactivity tracing, the most variations of adopting machinery metering, less relevance fluid properties of these monitoring methods, all there are shortcomings such as borehole operation, complex process, cost height, cycle length in they.Therefore, searching is a kind of fast, economical, simple and effective chemical monitoring method is just extremely important.Company of Chevron Corporation has developed and has used the layering crude oil energy output technology that crude oil gas chromatography hydrocarbon fingerprint calculates two-layer commingled oil well, and has carried out practical application in oil fields, ground such as California; U.S. MFE method of testing, can accurately obtain the oil offtake and the characteristics such as water production rate, layering crude oil and water sample of layering oil reservoir, but there are shortcomings such as borehole operation, stopping production test, complex process, cost height (90,000 yuan of Daqing oil field individual layer operation costs), cycle length in this method.In recent years, units such as China Jianghan Petroleum College, University of Petroleum have also carried out this technical research, and better effect is obtained in some oil fields at home.But mainly there is following problem: the one, the theory of not resolving multi-zone produced oil single-zone productivity contribution more than three layers and chemistry, mathematical model, what have follows linear theory, the employing partial least square method algorithm computation that has is mixed when adopting single-zone productivity contribution for three layers negative value etc. can occur, can not adapt to the needs of production that the oil field majority is a commingled oil well more than three layers; The 2nd, lack a large amount of intensive simulating lab test proportionings and on-the-spot test test contrast work.
The innovation and creation content
The purpose of this invention is to provide a kind of can dynamic monitoring multilayer commingled oil well in the chromatogram monitoring method of single-zone productivity contribution.
The dynamic chromatogram monitoring method of multi-zone produced oil single-zone productivity contribution provided by the invention comprises following steps:
1) gets each layering crude oil of commingled oil well, mix forming simulation and mix oil sample according to preset proportion, carry out chromatographic quantitative analysis respectively, obtain serial characteristic fingerprint parameter;
2) parameter of utilizing step 1) to obtain adopts nonlinear artificial neural network intelligence learning algorithm to set up mathematical model;
3) the mixed well well head crude oil of adopting of gathering required mensuration carries out chromatographic quantitative analysis, obtains the individual features finger print data;
4) the serial characteristic fingerprint data importing step 2 that the step 3) is obtained) mathematical model of Jian Liing calculates this and mixes the productivity contribution of adopting each layering crude oil of well.
In the dynamic chromatogram monitoring method of above-mentioned multi-zone produced oil single-zone productivity contribution, the computation process of described nonlinear artificial neural network intelligence learning algorithm is made up of forward-propagating and backpropagation, in the forward-propagating process, input information is successively handled through hidden layer from input layer, and to the output layer propagation, the neuronic state of each layer only influences the neuronic state of one deck down; If the output in that output layer can not obtain expecting then changes backpropagation over to, error signal is successively returned along original connecting path, revise the neuronic weights of each layer by error signal, make error reduce, reach accuracy requirement until error;
The local error function formula is as follows:
In the dynamic chromatogram monitoring method of above-mentioned multi-zone produced oil single-zone productivity contribution, step 2) sets up mathematical model, be meant in the analog computation processing procedure, import all simulations one by one and mix the selected masterplate characteristic fingerprint parameter of oil sample stratographic analysis, through a series of Sigmoid function and matrix operation, weighting, on average, output to second hidden layer, a series of computings through same principle, output to first hidden layer, pass through a series of computings of same principle again, output to output layer, output layer promptly is mixed each layer number percent contrast of adopting the percentage contribution rate and the actual proportioning of each layering of crude oil, error is successively feedbacked by original path, in the process of feedback,, adjust the weight vector matrix of each node successively by the size of error; Repeat top step once more according to the weight vector matrix after adjusting, so move in circles, till the error between output and the actual proportioning satisfies desired precision; At this moment, store the weight matrix and the correlation parameter of each unit of each layer, just set up and mixed the standard mathematical model of adopting the crude oil single-zone productivity contribution.
Described Sigmoid function is that asymmetric Sigmoid function is
The functional value scope is (0,1).
In the dynamic chromatogram monitoring method of above-mentioned multi-zone produced oil single-zone productivity contribution, between described output and the actual proportioning error finally satisfy absolute error less than 5%, relative deviation is less than 10%.
In the dynamic chromatogram monitoring method of above-mentioned multi-zone produced oil single-zone productivity contribution, described step 1) and 2) set up mathematical model, step 3) and 4 to mix the parameter of adopting main shaft) calculate with the auxiliary shaft image data, obtain the auxiliary shaft single-zone productivity contribution.
Adopt method of the present invention, easy and simple to handle, the time that draws monitoring result is short, the low but accuracy height of expense, significant to improving multilayer commingled oil well single-zone productivity contribution computational accuracy, adopt the analog computation of crude oil single-zone productivity contribution effective means is provided for mixing more than three layers.
Description of drawings
Fig. 1 calculates synoptic diagram for the error back propagation of the multilayer feedforward neural network that the present invention adopts;
Fig. 2 is a commingled oil well well location floor map in Sa Ertu test site among the embodiment;
Fig. 3 is a Sa Ertu test site commingled oil well productive zone position synoptic diagram among the embodiment;
Fig. 4 is a main shaft B1-50-J562 layering crude oil chromatogram ration analysis spectrogram among the embodiment;
Fig. 5 is the repeated star-plot of test site main shaft B1-50-J562 layering oil among the embodiment;
Fig. 6 is a test site main shaft B1-50-J562 layering crude oil template characteristic fingerprint otherness star-plot among the embodiment;
Fig. 7 is auxiliary shaft B1-42-563 well and a main shaft SI crude oil feature hydrocarbon fingerprint parameter star-plot among the embodiment;
Fig. 8 is auxiliary shaft B1-42-563 well and a main shaft PII2-5 crude oil feature hydrocarbon fingerprint parameter star-plot among the embodiment;
Fig. 9-A is asymmetric Sigmoid function synoptic diagram among the present invention;
Fig. 9-B is symmetrical Sigmoid function synoptic diagram.
Embodiment
The present invention has mainly proposed the nonlinear theory of multi-zone produced oil single-zone productivity contribution, and according to this theory, simulate proportioning test, set up chemical model and mathematical model.The present invention is the model with regard to setting up also, selects Daqing oil field main force produce oil old liberated area Sa Ertu field test district etc., carries out the contrast of lab simulation proportioning test and field monitoring and verifies.
Below describe the present invention in detail from several respects.
One, the nonlinear theory of multi-zone produced oil single-zone productivity contribution and mathematical simulation calculation
1, nonlinear theory
In the oil reservoir that is communicated with, the crude oil composition characteristic should be consistent or similar in the oil reservoir, the capillary gas chromatography hydrocarbon fingerprint detection technology of crude oil is analyzed the hydro carbons fingerprint composition of crude oil on the molecular level level, has reflected that hydrocarbon compound is formed and content in the crude oil.Each layering crude oil exists detectable otherness and proportioning on composition (as isoparaffin and the naphthenic hydrocarbon etc.) concentration of chromatogram hydrocarbon fingerprint, the chromatogram characteristic fingerprint parameter that characterizes each layering oil otherness is an amount that does not change with layering crude oil quantity (volume or weight).
In the multilayer crude oil mixed mining process of oil field, flow into the shaft bottom from each layering crude oil along oil bearing reservoir, mixed in the flow process of well head by the shaft bottom in pit shaft, mixed process is the dynamic process that a physical chemistry is formed concentration change.Mix the degree that mixing takes place crude oil of adopting, be subjected to the common influence of conditions down-hole such as the self-characteristic such as physicochemical property of each layering viscosity of crude, density, hydrocarbon fingerprint molecular compound and incorporation time (flowing time of layering crude oil from the shaft bottom to the well head), temperature, pressure, water, mix that to adopt in the process each layering crude oil general near mixing fully or fully, otherwise, just lost the chromatographic fingerprint method calculate crude oil mix adopt than the basis.
In theory, same a kind of compound of single solvent variable concentrations mixes fully by different proportionings, should meet linear relationship between the melting concn of this compound and the single concentration, owing to measured condition effect such as precision, it is error that certain dispersion is also arranged.Crude oil is by stable hydrocarbon, aromatic hydrocarbons, nonhydrocarbon, the very complicated potpourri that bituminous matter etc. are formed, when different crude oils takes place to mix fully, for the hydrocarbon component composition, be exactly the polynary mixing of many solutes in multi-solvent, because the interaction between crude oil each component molecular compound self-characteristic and the compound, the quantitative proportioning in the influence of conditions down-hole and laboratory, the influence of chromatographiccondition and analysis precision etc., not all hydrocarbon fingerprint compound all satisfies linear relationship, along with the increase that mixes the number of plies, influencing each other between the oil component compound just increases, cause the nonlinear relationship degree to increase, causing in layering crude oil and the multi-zone produced oil has not been simple linear relationship between the same hydrocarbon fingerprint concentration, but a kind of curvilinear relation of complexity.
2, mix the analog ligand ratio and the mathematical simulation calculation of adopting crude oil layering contribution
The present invention simulates the production run of commingled oil well in the laboratory with layering crude oil proportioning test process, sets up suitable chemical model and mathematical model by experiment, realizes the mathematical simulation calculation of multi-zone produced oil single-zone productivity contribution.
Laboratory crude oil analog ligand than process in, owing to be subjected to the restriction of laboratory existence conditions, can not simulate the production run of commingled oil well fully, as down-hole pressure, water etc. (because the mixed process that crude oil takes place in the down-hole is a dynamic process), can accomplish that each layering crude oil proportioning is mixed fully under certain condition.When each layering crude oil of down-hole does not take place fully or when mixing fully, calculate to mix in the laboratory simulation proportioning and adopt crude oil and mix and adopt than producing than mistake or wrong conclusion.Therefore, utilize chromatogram hydrocarbon fingerprint technique calculate to mix and adopt the crude oil single-zone productivity contribution, be subjected to the common restriction of each layering crude oil in down-hole mixability and laboratory simulation condition, both are approaching more, the method calculate oil well actual mix adopt than accuracy just high more.
Solved the two-layer problem of adopting of mixing in the past, big more options ratio of peak parameter, but its not strict linear superposition relation, though calculate mixed adopting than there being defective by making linear " standard plate ", but two-layer proportioning influence factor is less relatively, choose the good ratio of peak characteristic fingerprint parameter of correlativity, can be similar to and satisfy linear relationship, is easy to set up chemistry and mathematical model: solve two-layer mathematical simulation calculation of mixing the problem of adopting, generally with the linear method that fits, the mathematical model problem solves substantially.
For three layers and the mixed problem of adopting of above multilayer, owing to polynary confounding factor and the increase of nonlinear relationship degree, it is just difficult to set up chemistry and mathematical model, and the analog computation error is just bigger.There is bibliographical information to set up chemical model by adopting internal standard method, absolute quantitation etc., eliminated fractional error, but the characteristic fingerprint parameter of selecting also not exclusively satisfies linear relationship, if the approximate linear mathematical model of using, will certainly increase model error and error calculated, simultaneously, the selection of characteristic fingerprint parameter is also relatively more difficult, even select the characteristic fingerprint parameter meticulously, also be difficult to reach high-precision requirement with the similar linearity method that fits; The domestic literature report adopts three layers of proportioning of offset minimum binary non-linear regression method calculating can make maximum error less than 5%, but negative contribution rate occurs sometimes.Therefore, need to improve mathematical model.
The present invention adopts nonlinear artificial neural network intelligence learning algorithm to set up mathematical model, and this Model Calculation process is seen Fig. 1.This computation process is made up of forward-propagating and backpropagation, and in the forward-propagating process, input information is successively handled through hidden layer from input layer, and propagates to output layer, and the neuronic state of each layer only influences the neuronic state of one deck down.If the output in that output layer can not obtain expecting then changes backpropagation over to, error signal is successively returned along original connecting path, revise the neuronic weights of each layer by error signal, make error reduce, reach accuracy requirement until error.
The local error function formula is as follows:
In above-mentioned mathematical simulation calculation processing procedure, import all mixing match sample chromatograms one by one and analyze selected masterplate characteristic fingerprint parameter value (absolute magnitude) etc., through a series of Sigmoid function and matrix operation, weighting, on average, output to second hidden layer, a series of computings through same principle, output to first hidden layer, pass through a series of computings of same principle again, output to output layer, output layer promptly is mixed each layer number percent contrast of adopting the percentage contribution rate and the actual proportioning of each layering of crude oil, error is successively feedbacked by original path, in the process of feedback,, adjust the weight vector matrix of each node successively by the size of error.Repeat top step once more according to the weight vector matrix after adjusting, so move in circles, till the error between output and the actual proportioning satisfies desired precision.At this moment, store the weight matrix and the correlation parameter of each unit of each layer, just set up and mixed the standard masterplate of adopting the crude oil single-zone productivity contribution.
When calculating, relative error between each layering crude oil contribution rate and the actual proportioning and precision control, the people is for being divided into 7 range of control:＜1% do not control, 1～5%, 5～10%, 10～25%, 25～50%, 50～75%, 75～100%, import the artificial desired relative error of each range of control respectively when the training masterplate.Output and actual proportioning between error finally satisfy absolute error less than 5%, relative deviation is less than 10%.
The Sigmoid function is neuronic nonlinear interaction function, and asymmetric Sigmoid function is
The functional value scope is (0,1), referring to Fig. 9-A; Symmetry Sigmoid function
Functional value is (1,1), referring to Fig. 9-B.Because mixed output percentage range of adopting well each minute oil reservoir is in [0,100%], so the present invention adopts asymmetric Sigmoid function.
When using this standard masterplate, only need import mixing the individual features fingerprint parameter of adopting the stratographic analysis of well well head crude oil, can calculate this and mix the productivity contribution of adopting each layering crude oil of well.
Two, embodiment: the test site mixes adopts crude oil single-zone productivity contribution chromatogram monitoring and comparative experimental research
This time is test site explanation the specific embodiment of the present invention with the produce oil old liberated area Sa Ertu of the Daqing oil field main force.
1, test site geologic aspects
Sa Ertu test site, Daqing oil field main force produce oil old liberated area, referring to Fig. 2, main producing horizon is Sa Ertu and Putaohua reservoir, contains a plurality of substratums separately, each payzone crude oil water containing is all more than 90%.Test and Selection four-hole oil well is main subjects (Fig. 2, Fig. 3), and its oil bearing reservoir sandstone development, connection: the B1-50-J562 well is a main shaft, and it has SI1-I4+5, SII8-9, four of PII2-5, PII8-10 to produce oil reservoir; Auxiliary shaft 1 is the B1-42-563 well, and it has and is communicated with the main shaft sandstone and the characteristics of thickness attenuation, has two of SI1-3, PII2-5 to produce oil reservoir; Auxiliary shaft 2 is the B1-D5-143 well, and it has two of SI1-3, PII8-10 to produce oil reservoir; Auxiliary shaft 3 is the B1-5-B143 well, and it has SI1-3-I4+5, SII8, three of PII2-4 to produce oil reservoir.
2, sample collecting
With U.S. MFE technology (U.S. MFE company multithread test device testing method, form by multithread test device, hydraulic locking joint, safety sub, P-T packer, pressure gauge etc., be used for test, gather resident fluid etc.) obtain main shaft B1-50-J562, auxiliary shaft B1-42-563 layering oil sample, and calculate each layering in mixed daily oil production of adopting under the production status according to the MFE technical test data.
3, laboratory crude oil simulation proportioning test
3.1 gas chromatographic analysis
(1) analytical conditions for gas chromatography
U.S. HP6890 Plus gas chromatograph, 7683 type automatic sampler and chem workstations, long 60m, internal diameter 0.25mm capillary column, carrier gas is a nitrogen; Detecting device is FID, 320 ℃ of detector temperatures, and combustion gas is a hydrogen, combustion-supporting gas is air; 300 ℃ of injector temperatures; 40 ℃ of post initial temperatures, constant temperature 1min rises to 300 ℃ with 4 ℃/min, constant temperature 60min etc.; Peak area internal standard method absolute quantitation (μ g/g).
(2) repeatability of former oil chromatography hydrocarbon fingerprint
The stability of chromatographic detection system operation is most important for analytical test result's reliability, thereby have influence on the accuracy of layering crude oil productivity simulation proportioning, so one of most advanced world chromatographic apparatus U.S. HP6890 Plus gas chromatograph and chem workstation are selected in this test for use, guarantee to analyze data accuracy.For selected characteristic fingerprint hydrocarbon parameter repeatedly the replicate analysis relative deviation be not more than 5%.
3.2 four layers are mixed and adopt crude oil simulation proportioning test and monitoring
(1) layering crude oil chromatographic fingerprint is analyzed and the characteristic fingerprint selection
Get four layering oil samples of SI1-I4+5, SII8-9, PII2-5, PII8-10 of main shaft B1-50-J562, quantitative test under identical GC conditions (analysis result is referring to Fig. 4), select the template characteristic fingerprint peaks according to repeatability (the replication relative deviation of Fig. 5 is generally 1.00%～4.00%), otherness (each layering crude oil masterplate characteristic fingerprint parameter relative deviation is generally 7.00%～20.00% among Fig. 6) principle, visible each layering oil of B1-50-J562 well can be used for mixing to be adopted than calculating.
(2) four layers are mixed the foundation of adopting crude oil layering production capacity monitoring standard masterplate
With four layering crude oil of B1-50-J562 well, (than in three matching methods that ratio is approaching are arranged according to the listed ratio of table 1 by an assembly, as 10: 10: 10: 70,20: 20: 20: 40 etc.) simulation mixed oil sample, carry out chromatogram ration analysis, set up to mix and adopt crude oil single-zone productivity contribution characteristic fingerprint parameter, see Table 1, utilize this parameter to carry out mathematical simulation calculation according to nonlinear artificial neural network intelligence learning algorithm, form mathematical model (also claiming the standard masterplate), absolute error with this standard masterplate regression Calculation result and actual proportioning is generally less than 5%, relative deviation sees Table 2 less than 5%.
Four layers of crude oil simulation of table 1 compound sample chromatographic fingerprint quantitative test masterplate characteristic fingerprint data mu g/g
Table 2 mixes for four layers and adopts crude oil single-zone productivity contribution standard masterplate regression Calculation comparing result
| Join colon | Layer position | Masterplate calculates | Actual proportioning | Absolute error | Relative deviation |
| ％ | ％ | ％ | ％ | ||
| 1 | SI1-I4+5 | 40.40 | 40.94 | 0.54 | 0.66 |
| SII8-9 | 22.55 | 24.28 | 1.73 | 3.06 | |
| PII2-5 | 20.80 | 18.40 | 2.40 | 3.43 | |
| PII8-10 | 16.95 | 16.38 | 0.57 | 2.14 | |
| 2 | SI1-I4+5 | 20.71 | 21.41 | 0.70 | 3.66 |
| SII8-9 | 43.28 | 44.67 | 1.39 | 2.25 | |
| PII2-5 | 17.87 | 16.30 | 1.57 | 2.03 | |
| PII8-10 | 18.13 | 17.62 | 0.51 | 3.53 | |
| 3 | SI1-I4+5 | 13.89 | 15.33 | 1.44 | 2.08 |
| SII8-9 | 26.71 | 28.22 | 1.51 | 1.49 | |
| PII2-5 | 41.95 | 47.94 | 5.99 | 1.25 | |
| PII8-10 | 17.43 | 20.56 | 3.13 | 3.36 | |
| 4 | SI1-I4+5 | 18.80 | 18.57 | 0.23 | 4.35 |
| SII8-9 | 21.27 | 21.93 | 0.66 | 2.23 | |
| PII2-5 | 12.72 | 13.73 | 1.01 | 1.14 | |
| PII8-10 | 47.19 | 45.77 | 1.42 | 4.90 |
| 5 | SI1-I4+5 | 13.34 | 12.43 | 0.91 | 3.31 |
| SII8-9 | 9.62 | 9.65 | 0.03 | 3.27 | |
| PII2-5 | 12.68 | 11.97 | 0.71 | 0.69 | |
| PII8-10 | 64.35 | 65.96 | 1.61 | 3.02 | |
| 6 | SI1-I4+5 | 13.20 | 12.41 | 0.79 | 0.62 |
| SII8-9 | 13.93 | 14.82 | 0.89 | 4.16 | |
| PII2-5 | 64.19 | 63.86 | 0.33 | 4.22 | |
| PII8-10 | 8.66 | 8.91 | 0.25 | 1.42 |
(3) four layers are mixed the verification of adopting crude oil layering production capacity monitoring standard masterplate
For the single-zone productivity contribution standard masterplate of being set up, analog ligand is tested than miscella, the masterplate finger-mark check is analyzed data and is seen Table 3, input calculation check result contrast sees Table 4, the absolute error of standard masterplate analog computation result and actual proportioning less than 3%, relative deviation is less than 5%, this shows that simulated experiment proportioning effect and standard masterplate are good.
Table 3 mixes for four layers adopts crude oil layering contribution standard masterplate verification finger print data μ g/g
Table 4 mixes for four layers adopts the contrast of crude oil layering contribution standard masterplate check results
| Join colon | Layer position | The standard masterplate calculates % | Actual proportioning % | Absolute error % | |
| 1 1 | SI1-I4+5 | 14.70 | 13.92 | 0.78 | 0.45 |
| SII8-9 | 30.01 | 32.10 | 2.09 | 2.96 | |
| PII2-5 | 37.03 | 36.20 | 0.83 | 1.13 | |
| PII8-10 | 18.24 | 17.78 | 0.46 | 1.28 | |
| 2 2 | SI1-I4+5 | 11.96 | 12.28 | 0.32 | 0.85 |
| SII8-9 | 13.53 | 14.57 | 1.04 | 4.94 | |
| PII2-5 | 65.15 | 62.51 | 2.64 | 3.14 | |
| PII8-10 | 9.35 | 9.65 | 0.30 | 4.35 |
(4) four layers of mixed crude oil single-zone productivity contribution monitoring result of adopting
Get from main shaft B1-50-J562 well head and to mix oil recovery sample and chromatographic quantitative analysis, masterplate fingerprint analysis data see Table 5, its input standard masterplate is calculated back four layers mix and adopt crude oil single-zone productivity contribution monitoring result and see Table 6.
Table 5 B1-50-J562 well mixes for four layers and adopts crude oil masterplate finger print data μ g/g
| Peak number |
| 17# | 19# | 21# | 22# | 23# | 24# | 25# | 26# | 27# | 31# | 34# | |
| Concentration | 485.18 | 319.58 | 261.98 | 505.66 | 229.42 | 406.98 | 591.86 | 573.51 | 481.44 | 1371.53 | 325.28 |
Table 6 mixes for four layers and adopts crude oil single-zone productivity contribution chromatogram monitoring result
| Pound sign | Layer position | The analog computation of standard masterplate is % as a result |
| B1-50-J562 | SI1-I4+5 | 25.87 |
| SII8-9 | 30.08 | |
| PII2-5 | 25.65 | |
| PII8-10 | 18.40 |
3.3 monitoring result contrast
(1) two-layer mixing adopted crude oil layering production capacity monitoring result relatively
After auxiliary shaft B1-42-563 well MFE measuring technology draws oil sample and asks the product construction to finish, got from well head in 16 days at interval at twice and mix oil recovery sample and chromatographic quantitative analysis, utilize two-layer the mixing of this well preparation to adopt the analog computation of crude oil layering production capacity monitoring standard masterplate, chromatogram hydrocarbon fingerprint The dynamic monitor result (table 7), as seen twice monitoring result unanimity, this well Putaohua reservoir (PII2-5 layer) are that the main force produces oil reservoir.
Two-layer the mixing of table 7 adopted crude oil single-zone productivity contribution chromatogram The dynamic monitor result
| Pound sign | Layer position | On July 2nd, 2002 is sampling monitoring % as a result for the first time | On July 18th, 2002 is sampling monitoring % as a result for the second time | Absolute deviation % |
| B1-42-563 | SI1-3 | 10.12 | 9.74 | 0.28 |
| PII2-5 | 89.87 | 90.25 | 0.38 |
Auxiliary shaft B1-42-563 well MFE technical monitoring result is SI1-3:0.585t/d, PII2-5:6.35t/d, single well productivity 6.935t/d.Be 0.47% with the relative deviation of actual production capacity 7t/d, absolute error is 0.065t.
The B1-42-563 well mixes to be adopted crude oil single-zone productivity contribution chromatography and contrast (table 8) with MFE method monitoring result: relative deviation is not more than 7.2%, absolute deviation is not more than 1.33%, has good consistance.
Two-layer the mixing of table 8 adopted crude oil layering production capacity monitoring result relatively
| Pound sign | Layer position | MFE monitoring result % | Chromatogram monitoring is % as a result | Absolute deviation % | Relative deviation % |
| B1-42-563 | SI1-3 | 8.43 | 9.74 | 1.31 | 7.2 |
| PII2-5 | 91.57 | 90.25 | 1.32 | 0.73 |
(2) four layers of mixed crude oil layering production capacity monitoring result of adopting compare
Main shaft B1-50-J562 well chromatogram hydrocarbon fingerprint monitoring and MFE monitoring result be (table 9) relatively: the maximum relative deviation of each layer relative productivity is 6.60%, maximum absolute deviation is 3.12%, two kind of technical monitoring unanimity as a result, and the Sa Ertu oil reservoir is that the main force produces oil reservoir.Crude oil 3.778t/d is produced in main shaft B1-50-J562 well MFE monitoring, produces crude oil 4t/d result relatively with reality, and absolute error is 0.222t, and relative deviation is 3.28%.
From above comparative result, U.S. MFE method of testing mixes with chromatography monitoring of the present invention and adopts consistent, the accuracy height of crude oil layering production capacity result.Yet, comparing the MFE method of testing, the present invention but has the borehole operation of not needing and the characteristics of the test that stops production, and method is easy, and the time that draws monitoring result is short, and expense is low.
Table 9 mixes for four layers adopts crude oil layering production capacity monitoring result relatively
| Layer position | Chromatography monitoring result % | MFE monitoring result % | Absolute deviation % | Relative deviation % |
| SI1-I4+5 | 25.87 | 28.69 | 2.82 | 5.17 |
| SII8-9 | 30.08 | 32.66 | 2.58 | 4.11 |
| PII2-5 | 25.65 | 22.53 | 3.12 | 6.48 |
| PII8-10 | 18.40 | 16.12 | 2.28 | 6.60 |
4, main shaft monitoring auxiliary shaft B1-42-563 well mixes and adopts crude oil layering production capacity and result's contrast
4.1 the otherness of main shaft and auxiliary shaft B1-42-563 well oil bearing reservoir
Sa Ertu test site auxiliary shaft B1-42-563 well is communicated with main shaft B1-50-J562 well sandstone reservoir, but the sandstone variation of its corresponding reservoir, thickness SI are 5.8m by the 7.1m attenuation, PII2-5 is 3.8m by the 5.5m attenuation, sandstone thickness attenuation amplitude is respectively 18%, 31%, thereby this experiment will be determined whether hydrocarbon fingerprint geo-chemical feature that auxiliary shaft is communicated with oil reservoir crude oil changes and judge the monitoring effect of main shaft to auxiliary shaft.
4.2 two mouthfuls of well layering crude oil feature hydrocarbon fingerprint parameters relatively
From auxiliary shaft B1-42-563 well and main shaft B1-50-J562 well SI crude oil feature hydrocarbon fingerprint parameter star-plot 7, the hydrocarbon fingerprint parameter is approaching, and general relative deviation illustrates that less than 5% two mouthfuls of well SI1-3 and SI1-I4+5 crude oil have similarity.From auxiliary shaft B1-42-563 well and main shaft B1-50-J562 well PII2-5 crude oil feature hydrocarbon fingerprint parameter star-plot 8, the hydrocarbon fingerprint parameter is approaching, and general relative deviation illustrates that less than 5% two mouthfuls of well PII2-5 crude oil have similarity.
In view of this, can utilize main shaft layering crude oil to set up the standard masterplate, monitor two-layer mixing and adopt auxiliary shaft B1-42-563 well layering crude oil productivity contribution.
4.3 utilize the two-layer standard masterplate monitoring auxiliary shaft B1-42-563 well layering production capacity of main shaft and result's contrast
Get two crude oil samples of main shaft B1-50-J562 well SI1-I4+ 5 and PII2-5, press different proportion simulation preparation miscella, after the chromatographic fingerprint quantitative test, select and the identical masterplate fingerprint parameter of auxiliary shaft B1-42-563 standard masterplate, form the two-layer monitoring standard masterplate of main shaft B1-50-J562 well after the analog computation, the relative deviation of regression Calculation and the verification of standard masterplate is all less than 5%; After getting auxiliary shaft B1-42-563 well head again and mixing the oil recovery sample and carry out chromatographic quantitative analysis, utilize the two-layer standard masterplate of main shaft analog computation auxiliary shaft B1-42-563 single-zone productivity contribution to be SI1-3:9.34%, PII2-5:90.65%, ask with MFE and to produce as a result absolute deviation less than 0.92% (table 10), with the standard masterplate result of calculation (table 7) of utilizing this well layering crude oil to set up relatively absolute deviation less than 0.4%.
Two-layer the mixing of table 10 main shaft monitoring adopted auxiliary shaft B1-42-563 layering contribution result and contrast
| Auxiliary shaft | Layer position | MFE monitoring result % | Main shaft monitoring auxiliary shaft is % as a result | Absolute deviation % |
| B1-42-563 | SI1-3 | 8.43 | 9.34 | 0.91 |
| PII2-5 | 91.57 | 90.65 | 0.92 |
2.5 main shaft is monitored other mixed well single-zone productivity contribution of adopting
(1) main shaft monitoring auxiliary shaft B1-5-B143 mixes for three layers and adopts layering crude oil production capacity
Auxiliary shaft B1-5-B143 has SI1-I4+5, SII8-9, three of PII2-5 produce oil reservoir, and be communicated with main shaft B1-50-J562 sandstone, get the SI1-I4+ 5 of main shaft, SII8-9, three oil samples of PII2-5, set up main shaft monitoring auxiliary shaft B1-5-B143 and mix the standard masterplate of adopting single-zone productivity contribution for three layers, after getting auxiliary shaft B1-5-B143 well head and mixing the oil recovery sample and carry out stratographic analysis, three layers of mixed layering production capacity monitoring standard masterplate of adopting with main shaft, three layers of analog computation auxiliary shaft B1-5-B143 mix and adopt layering production capacity result and account for 54.17% for SI1-I4+5, SII8-9 accounts for 37.69%, PII2-5 accounts for 8.12%, this well Sa Ertu oil reservoir (SI1-I4+5, SII8-9) produce oil reservoir for the main force.
(2) two-layer the mixing of main shaft monitoring auxiliary shaft B1-D5-143 adopted layering crude oil production capacity
Auxiliary shaft B1-D5-143 has two of SI1-3, PII8-10 to produce oil reservoir, and B1-50-J562 is communicated with main shaft, adopt above-mentioned same method, utilize the main shaft oil sample to set up two-layer monitoring standard masterplate, monitoring auxiliary shaft B1-D5-143 is two-layer to be mixed and adopts that single-zone productivity contribution result: SI1-3 accounts for 4.46%, PII8-10 accounts for 95.57%, and this well Putaohua reservoir (PII8-10) is main force's pay sand.
The present invention has following characteristics:
(1) broken through multi-zone produced oil single-zone productivity contribution chromatogram hydrocarbon fingerprint concentration linear relationship theory, establish and verification experimental verification multilayer miscella chromatogram hydrocarbon fingerprint concentration and layering oil chromatography hydrocarbon fingerprint nonlinear concentration relational theory, set up the nonlinear mathematics simulation method of multi-zone produced oil productivity contribution, significant to improving multilayer commingled oil well single-zone productivity contribution computational accuracy, adopt the analog computation of crude oil single-zone productivity contribution effective means is provided for mixed more than three layers.
(2) utilize first nonlinear theory and the nonlinear mathematics simulation method of multi-zone produced oil single-zone productivity contribution at home and abroad, realize that not only three layers have also been broken through four layers of mixed analog computation of adopting the crude oil single-zone productivity contribution, contrast in Sa Ertu field test district and MFE method field monitoring result that absolute deviation is not more than 3.3%, relative deviation is not more than 6.6%, obtain very good effect; And inquired into mixability under laboratory simulation conditions of mixture ratios and the crude oil well and may adopt the impact that contribution rate is calculated to mixed.
(3) utilize first former oil chromatography hydrocarbon fingerprint technique, grand celebration Sa I and Sa II crude oil, the II2-5 of Portugal and the II8-10 of Portugal substratum oil region are separated, and be used for the mixed crude oil single-zone productivity contribution analog computation of adopting.
(4) test is thought, auxiliary shaft is communicated with oil reservoir sandstone variation with main shaft, when the attenuation amplitude reaches 31%, the mixed relative deviation of adopting crude oil layering production capacity calculating standard masterplate characteristic fingerprint parameter is generally less than 5%, the available mixed main shaft standard masterplate monitoring auxiliary shaft single-zone productivity contribution of adopting.
(5) exploitation of multi-zone produced oil single-zone productivity contribution dynamic monitoring chromatographic technique, can set up the monitoring standard masterplate by enough main shaft layering crude oil, be communicated with the two-layer single-zone productivity contribution dynamic monitoring to four layers of commingled oil well to many mouthfuls, need not stop production, only need regularly from wellhead sampling, lab analysis, analog computation, can realize the dynamic on-line monitoring of on-the-spot well head, have that applicability is strong, small investment, cycle are short, accurately, fast, the characteristics such as large tracts of land monitoring, wide application prospect and huge economic benefit are arranged, adopt the dynamic monitoring of crude oil single-zone productivity contribution for the oil field is mixed and opened up new method.
Claims (5)
1, the dynamic chromatogram monitoring method of a kind of multi-zone produced oil single-zone productivity contribution is characterized in that, comprises following steps:
1) gets each layering crude oil of commingled oil well, mix forming simulation and mix oil sample according to preset proportion, respectively each layering crude oil sample is carried out chromatographic quantitative analysis, obtain serial characteristic fingerprint parameter;
2) parameter of utilizing step 1) to obtain adopts nonlinear artificial neural network intelligence learning algorithm to set up mathematical model;
3) the mixed well well head crude oil of adopting of gathering required mensuration carries out chromatographic quantitative analysis, obtains the individual features finger print data;
4) the serial characteristic fingerprint data importing step 2 that the step 3) is obtained) mathematical model of Jian Liing calculates this and mixes the productivity contribution of adopting each layering crude oil of well.
2, the dynamic chromatogram monitoring method of multi-zone produced oil single-zone productivity contribution according to claim 1, it is characterized in that, the computation process of described nonlinear artificial neural network intelligence learning algorithm is made up of forward-propagating and backpropagation, in the forward-propagating process, input information is successively handled through hidden layer from input layer, and to the output layer propagation, the neuronic state of each layer only influences the neuronic state of one deck down; If the output in that output layer can not obtain expecting then changes backpropagation over to, error signal is successively returned along original connecting path, revise the neuronic weights of each layer by error signal, make error reduce, reach accuracy requirement until error;
The local error function formula is as follows:
3, the dynamic chromatogram monitoring method of multi-zone produced oil single-zone productivity contribution according to claim 2, it is characterized in that, step 2) sets up mathematical model, be meant in the analog computation processing procedure, import all simulations one by one and mix the selected masterplate characteristic fingerprint parameter of oil sample stratographic analysis, through a series of Sigmoid function and matrix operation, weighting, on average, output to second hidden layer, a series of computings through same principle, output to first hidden layer, pass through a series of computings of same principle again, output to output layer, output layer promptly is to mix the percentage contribution rate of adopting each layering of crude oil, each layer number percent contrast with actual proportioning successively feedbacks error by original path, in the process of feedback, by the size of error, adjust the weight vector matrix of each node successively; Repeat top step once more according to the weight vector matrix after adjusting, so move in circles, till the error between output and the actual proportioning satisfies desired precision; At this moment, store the weight matrix and the correlation parameter of each unit of each layer, just set up and mixed the standard mathematical model of adopting the crude oil single-zone productivity contribution;
Described Sigmoid function is asymmetric Sigmoid function
The functional value scope is (0,1).
4, the dynamic chromatogram monitoring method of multi-zone produced oil single-zone productivity contribution according to claim 3 is characterized in that, between described output and the actual proportioning error finally satisfy absolute error less than 5%, relative deviation is less than 10%.
5, according to the dynamic chromatogram monitoring method of the arbitrary described multi-zone produced oil single-zone productivity contribution of claim 1 to 4, it is characterized in that, described step 1) and 2) set up mathematical model, step 3) and 4 to mix the parameter of adopting main shaft) calculate with the auxiliary shaft image data, obtain the auxiliary shaft single-zone productivity contribution.
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| CN104110257A (en) * | 2013-05-24 | 2014-10-22 | 中国石油化工股份有限公司 | Single-layer contribution rate quantitative evaluation method |
| CN103556990B (en) * | 2013-10-30 | 2016-03-16 | 大庆市永晨石油科技有限公司 | A kind of producing well production capacity is followed the tracks of and evaluation method |
| CN104101673B (en) * | 2014-07-22 | 2015-10-07 | 中国石油大学(华东) | A kind of assay method of commingle crude oil productivity contribution rate |
| CN104265259A (en) * | 2014-08-07 | 2015-01-07 | 员增荣 | Capacity tracking and evaluating method |
| CN107330475A (en) * | 2017-07-19 | 2017-11-07 | 北京化工大学 | A kind of new model-free Bayes's classification forecast model flexible measurement method |
| CN111272924A (en) * | 2020-02-11 | 2020-06-12 | 长江大学 | Method and device for calculating single-layer yield of two-layer commingled production well and readable storage medium |
| CN111749687B (en) * | 2020-07-23 | 2023-11-21 | 中海石油国际能源服务(北京)有限公司 | Multi-layer oil reservoir principal force horizon determination method, device, equipment and storage medium |
| CN113094991B (en) * | 2021-04-09 | 2022-03-29 | 西南石油大学 | Method for calculating crude oil density by using geological pyrolysis spectrogram and machine learning |
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