CN103485770A - Method and system of obtaining oil saturation based on artificial neural network - Google Patents
Method and system of obtaining oil saturation based on artificial neural network Download PDFInfo
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Abstract
The invention relates to the field of oil saturation technologies, and discloses a method and a system of obtaining oil saturation based on an artificial neural network. The method comprises the following steps of carrying out a rock electricity experiment on a core so as to obtain log data; assigning a and b to 1, and carrying out back calculation to obtain m and n; taking a logging curve which represents lithology, physical properties and oiliness as an input layer, selecting the m and the n as output layers, and establishing a BP neural network model; substituting logging data corresponding to the m and the n which are obtained through the rock electricity experiment into the model so as to obtain m' and n'; comparing the m and the n which are obtained through the rock electricity experiment with the m' and the n' which are obtained through the model so as to obtain a model with a minimum error; substituting data on the logging curve of the corresponding input layer into the model with the minimum error so as to obtain a cementation index curve and a saturation index curve; substituting the curves into an Archie's formula so as to compute the oil saturation. According to the invention, computational accuracy of the oil saturation, which is obtained through the Archie's formula, is improved.
Description
Technical field
The present invention relates to the oil saturation technical field, mainly be applicable to ask for based on artificial neural network the method and system of oil saturation.
Background technology
When utilizing well-log information identification oil, gas, water layer, Archie formula is the main method of asking for reservoir oil-containing (gas) saturation ratio always, its effect mainly is subject to the impact of the litho-electric parameters such as lithology factor a, lithology constant b, cementation factor m, saturation exponent, particularly the value of cementation factor m and saturation exponent has directly determined that Archie formula calculates the accuracy of oil saturation (for lithology factor a and these two parameters of lithology constant b, normal supposition their change little, and approximately be taken as 1).At present in Archie formula, the numerical value of litho-electric parameters m, n is mainly measured mean value method and regional empirical value method obtains by experiment.
These two kinds of methods are all to take the litho-electric parameters result of the meticulous measurement in laboratory to be basis, get the average of some rocks electricity experimental result or directly call this zone litho-electric parameters empirical value and carry out oil saturation calculating in a certain zone or interval.The method has been carried out equalization to the value of litho-electric parameters m, n, fails to take into full account the concrete influence factor of reservoir litho-electric parameters, and the oil saturation error calculated is larger.
Summary of the invention
Technical problem to be solved by this invention is to provide a kind of method and system of asking for oil saturation based on artificial neural network, it can improve the computational accuracy of cementation factor and saturation exponent, thereby improves the computational accuracy that obtains oil saturation by Archie formula.
For solving the problems of the technologies described above, the invention provides a kind of method of asking for oil saturation based on artificial neural network, comprising:
Choose rock core in stratum, described rock core is carried out to the experiment of rock electricity, obtain the log data in Archie formula;
By the lithology factor a in Archie formula and lithology constant b assignment, be 1, inverse obtains cementation factor m and saturation exponent;
Using characterizing the input layer of the log of lithology, physical property and oiliness as described BP neural network model, select described cementation factor m and the described saturation exponent output layer as the BP neural network model; Set up the relation between described output layer and described input layer by simulation, set up the BP neural network model;
To calculate m with the m obtained by the experiment of described rock electricity, the described BP neural network model of log data substitution of the corresponding depth of stratum of n,, n;
M, the n that will obtain by the experiment of described rock electricity and the m obtained by described BP neural network model,, n, compare, obtain the BP neural network model of error minimum;
The BP neural network model of the described error minimum of data substitution on the described log of corresponding input layer is carried out to cementation factor curve and the saturation exponent curve that node-by-node algorithm obtains dynamic change;
The cementation factor curve of the described dynamic change that obtains and the data substitution Archie formula on the saturation exponent curve are carried out to the calculating of oil saturation.
Further, the node number in the intermediate layer of described BP neural network model is 2N+1, the nodes that wherein N is described input layer, and N is greater than 1 integer.
Further, described m, the n that will obtain by the experiment of rock electricity and the m obtained by the BP neural network model,, n, compare, the concrete grammar that obtains the BP neural network model of error minimum comprises: the span of regulating the input layer log of described BP neural network model, obtain different m,, n, the value; Will be by the experiment of described rock electricity m, the n that obtain and the m obtained by the BP neural network model,, n, carry out the size comparison; According to the relative coefficient of the BP neural network model corresponding with comparative result, obtain the BP neural network model of described error minimum.
Further, the relative coefficient of the BP neural network model that described basis is corresponding with comparative result, obtain the BP neural network model of error minimum, comprise: if the relative coefficient of described BP neural network model equals 0.85, the BP neural network model that the BP neural network model corresponding with relative coefficient 0.85 is described error minimum.
Further, the log of described sign lithology comprises gamma ray curve, spontaneous potential curve and CAL; The log of described sign physical property comprises interval transit time curve, porosity curve, neutron curve, density curve and permeability curve; The log of described sign oiliness comprises resistivity curve and stratum shallow resistivity curve in true formation resistivity curve, stratum.
The present invention also provides a kind of system of asking for oil saturation based on artificial neural network, comprising:
Rock electricity experiment module, for choose rock core in stratum, carry out the experiment of rock electricity to described rock core, obtains the log data in Archie formula;
The first computing module, be 1 for the lithology factor a by Archie formula and lithology constant b assignment, and inverse obtains cementation factor m and saturation exponent;
The one BP neural network model generation module, the input layer for the log that will characterize lithology, physical property and oiliness as described BP neural network model, select described cementation factor m and the described saturation exponent output layer as the BP neural network model; Set up the relation between described output layer and described input layer by simulation, set up the BP neural network model;
The second computing module, for calculating m with the m obtained by the experiment of described rock electricity, the described BP neural network model of log data substitution of the corresponding depth of stratum of n,, n;
The 2nd BP neural network model generation module, for m, the n that will obtain by the experiment of described rock electricity and the m obtained by described BP neural network model,, n, compare, obtain the BP neural network model of error minimum;
The 3rd computing module, carry out for the BP neural network model of the described error minimum of data substitution on the described log by corresponding input layer cementation factor curve and the saturation exponent curve that node-by-node algorithm obtains dynamic change;
The 4th computing module, carry out the calculating of oil saturation for the cementation factor curve of the described dynamic change by obtaining and the data substitution Archie formula on the saturation exponent curve.
Further, in a described BP neural network model generation module, the node number in the intermediate layer of described BP neural network model is 2N+1, the nodes that wherein N is described input layer, and N is greater than 1 integer.
Further, described the 2nd BP neural network model generation module comprises:
The index acquiring unit, the span for the input layer log of regulating described BP neural network model, obtain different m,, n, value;
Comparing unit, for will be by the experiment of described rock electricity m, the n that obtain and the m obtained by the BP neural network model,, n, carry out the size comparison;
BP neural network model generation unit, for the relative coefficient of the BP neural network model according to corresponding with comparative result, obtain the BP neural network model of described error minimum.
Further, described BP neural network model generation unit, if equal 0.85 specifically for the relative coefficient of described BP neural network model, the BP neural network model that the BP neural network model corresponding with relative coefficient 0.85 is described error minimum.
Further, the log of described sign lithology comprises gamma ray curve, spontaneous potential curve and CAL; The log of described sign physical property comprises interval transit time curve, porosity curve, neutron curve, density curve and permeability curve; The log of described sign oiliness comprises resistivity curve and stratum shallow resistivity curve in true formation resistivity curve, stratum.
Beneficial effect of the present invention is:
The method and system of asking for oil saturation based on artificial neural network provided by the invention, first obtain the log data in Archie formula by the experiment of rock electricity, then be 1 by a and b assignment, and inverse goes out m and n; M, n and the m ' obtained by the BP neural network model, the n ' that will obtain by the experiment of rock electricity compare again, and obtain the BP neural network model of error minimum; Obtain again cementation factor curve and the saturation exponent curve of dynamic change by the BP neural network model of error minimum, the cementation factor of dynamic change and saturation exponent substitution Archie formula are carried out to the calculating of oil saturation.Therefore on basis due to the log data of the present invention in known Archie formula, by being 1 by a and b assignment, inverse goes out m and n, has overcome in the conventional method the calculating defect of m and n equalization, has improved the computational accuracy of oil saturation.With conventional method, compare, the oil saturation curve that cementation factor and the saturation exponent of the present invention by dynamic change calculates is sensitiveer for the reaction of oil-gas Layer, and its variation tendency can be reacted the oil-bearing information of Reservoir Section more realistically.
The accompanying drawing explanation
Fig. 1 asks for the flow chart of the method for oil saturation for what the embodiment of the present invention provided based on artificial neural network;
Fig. 2 asks for based on artificial neural network the actual processing effect figure that the method for oil saturation obtains for what provide by the embodiment of the present invention.
The specific embodiment
Reach for further setting forth the present invention technological means and the effect that predetermined goal of the invention is taked, below in conjunction with accompanying drawing and preferred embodiment, the specific embodiment and the operating principle of asking for the method and system of oil saturation based on artificial neural network that foundation the present invention is proposed are elaborated.
As shown in Figure 1, the method for asking for oil saturation based on artificial neural network that the embodiment of the present invention provides comprises:
Choose rock core in stratum, rock core is carried out to the experiment of rock electricity, obtain the log data in Archie formula; Concrete, select survey region in stratum, choose rock core in survey region, rock core is carried out to the experiment of rock electricity, obtain the log data in Archie formula; Wherein, to guarantee to have at least the electric data of testing of rock of 20 samples in selected survey region.
By the lithology factor a in Archie formula and lithology constant b assignment, be 1, inverse obtains cementation factor m and saturation exponent;
Concrete, due to known formation resistivity factor, wherein, Φ is degree of porosity, and a is lithology factor, and m is cementation factor; Be 1 by lithology factor a assignment, can obtain cementation factor m by inverse.In like manner, due to known resistance enhancement coefficient, wherein, S
wfor oil saturation, b is the lithology constant, and n is saturation exponent; Be 1 by lithology constant b assignment, can obtain saturation exponent by inverse.
To characterize the input layer of the log of lithology, physical property and oiliness as the BP neural network model, the node number in the intermediate layer of BP neural network model is 2N+1, the nodes that wherein N is input layer, and N is greater than 1 integer.Select cementation factor m and the saturation exponent output layer as the BP neural network model; Set up the relation between output layer and input layer by simulation, set up the BP neural network model;
To calculate m ', n ' with the m obtained by the experiment of rock electricity, the log data substitution BP neural network model of the corresponding depth of stratum of n;
M, n and the m ' obtained by the BP neural network model, the n ' that will obtain by the experiment of rock electricity compare, and obtain the BP neural network model of error minimum; Concrete, the span of regulating the input layer log of BP neural network model, obtain different m ', n ' value; M, n and the m ' obtained by the BP neural network model, the n ' that will obtain by the experiment of rock electricity carry out size relatively; According to the relative coefficient of the BP neural network model corresponding with comparative result, obtain the BP neural network model of error minimum.Further, if the relative coefficient of the BP neural network model obtained equals 0.85, the BP neural network model that the BP neural network model corresponding with relative coefficient 0.85 is the error minimum.
The BP neural network model of the data substitution error minimum on the log of corresponding input layer is carried out to cementation factor curve and the saturation exponent curve that node-by-node algorithm obtains dynamic change;
The cementation factor curve of the dynamic change that obtains and the data substitution Archie formula on the saturation exponent curve are carried out to the calculating of oil saturation.
Preferably, the log of sign lithology comprises gamma ray curve, spontaneous potential curve and CAL; The log that characterizes physical property comprises interval transit time curve, porosity curve, neutron curve, density curve and permeability curve; The log that characterizes oiliness comprises resistivity curve and stratum shallow resistivity curve in true formation resistivity curve, stratum.In the present embodiment, the log of sign lithology is gamma ray curve; The log that characterizes physical property comprises interval transit time curve and porosity curve; The log that characterizes oiliness is the true formation resistivity curve.
The present invention also provides a kind of system of asking for oil saturation based on artificial neural network, comprising:
Rock electricity experiment module, for choose rock core in stratum, carry out the experiment of rock electricity to rock core, obtains the log data in Archie formula; Further, rock electricity experiment module, specifically for select survey region in stratum, is chosen rock core in survey region, and rock core is carried out to the experiment of rock electricity, obtains the log data in Archie formula; Wherein, to guarantee to have at least the electric data of testing of rock of 20 samples in selected survey region.
The first computing module, be 1 for the lithology factor a by Archie formula and lithology constant b assignment, and inverse obtains cementation factor m and saturation exponent;
Concrete, due to known formation resistivity factor, wherein, Φ is degree of porosity, and a is lithology factor, and m is cementation factor; Be 1 by lithology factor a assignment, can obtain cementation factor m by inverse.In like manner, due to known resistance enhancement coefficient, wherein, S
wfor oil saturation, b is the lithology constant, and n is saturation exponent; Be 1 by lithology constant b assignment, can obtain saturation exponent by inverse.
The one BP neural network model generation module, for characterizing the input layer of the log of lithology, physical property and oiliness as the BP neural network model, the node number in the intermediate layer of BP neural network model is 2N+1, the nodes that wherein N is input layer, and N is greater than 1 integer.Select cementation factor m and the saturation exponent output layer as the BP neural network model; Set up the relation between output layer and input layer by simulation, set up the BP neural network model;
The second computing module, for calculating m ', n ' with the m obtained by the experiment of rock electricity, the log data substitution BP neural network model of the corresponding depth of stratum of n;
The 2nd BP neural network model generation module, compare for m, n and the m ' obtained by the BP neural network model, the n ' that will obtain by the experiment of rock electricity, obtains the BP neural network model of error minimum; Particularly, the 2nd BP neural network model generation module comprises: the index acquiring unit, the span for the input layer log of regulating the BP neural network model, obtain different m,, n, value; Comparing unit, for will m, the n that obtain by rock electricity experiment and the m obtained by the BP neural network model,, n, carry out the size comparison; BP neural network model generation unit, for the relative coefficient according to by the BP neural network model relatively obtained, obtain the BP neural network model of error minimum.Further, BP neural network model generation unit, if equal 0.85, the BP neural network model that the BP neural network model corresponding with relative coefficient 0.85 is the error minimum specifically for the relative coefficient of the BP neural network model that obtains.
The 3rd computing module, carry out for the BP neural network model of the data substitution error minimum on the log by corresponding input layer cementation factor curve and the saturation exponent curve that node-by-node algorithm obtains dynamic change;
The 4th computing module, carry out the calculating of oil saturation for the cementation factor curve of the dynamic change by obtaining and the data substitution Archie formula on the saturation exponent curve.
Preferably, the log of sign lithology comprises gamma ray curve, spontaneous potential curve and CAL; The log that characterizes physical property comprises interval transit time curve, porosity curve, neutron curve, density curve and permeability curve; The log that characterizes oiliness comprises resistivity curve and stratum shallow resistivity curve in true formation resistivity curve, stratum.
Embodiment
1, choose Ji's plateau area, long celebrating oil field for survey region, survey region is carried out to stratigraphic division.Wherein, No. 1 survey region comprises 12 mouthfuls of core holes and 29 mouthfuls of offset wells, for long 8, with 9 two of length, studies the electric experimental result of rock that layer position collected 65 core samples altogether.
2, the lithology factor a 65 core samples tested and lithology constant b assignment are 1, use the Archie formula inverse to go out m, n value.
3, choosing interval transit time (AC), natural gamma (GR), degree of porosity (Por) and true formation resistivity (Rt) is the input layer of BP neural network model, the nodes in intermediate layer is 9, using m, the n rock electricity experimental result of the corresponding degree of depth as output layer, set up the relation between output layer and input layer by simulation, set up respectively m, n computation models long by 8, long 9 stratum.
4, will calculate m ', n ' with the m obtained by the experiment of rock electricity, the log data substitution BP neural network model of the corresponding depth of stratum of n.
5, model accuracy is carried out to self-checking, constantly adjust the span of the input layer log of BP neural network model, make the correlation of assay reach maximum, thereby obtain the BP neural network model of error minimum.
6, by the data on the log of corresponding input layer respectively the BP neural network model of long by 8, the long 9 interval error minimums of substitution carry out node-by-node algorithm, can obtain cementation factor curve and saturation exponent curve with degree of depth dynamic change, finally use dynamic cementation factor curve and saturation exponent curve negotiating Archie formula to calculate the oil saturation of the corresponding degree of depth.Referring to Fig. 2, long 8 layers of 2551.4m to the 2563.2m degree of depth section of this well, the average oil saturation that utilizes conventional saturation computation method to obtain is 43%, conclusion is the oil-containing water layer; The average oil saturation of trying to achieve by the present invention is 53%, and upper and lower two sections oiliness gaps are larger, and conclusion is that top is oil-water common-layer, and bottom is dry layer.After actual perforation and fracturing reform, this interval day produce oil 5.44t, daily output water 15.8m
3, the formation testing result is oil-water-layer, reaches commercial oil, the conclusion that the formation testing result obtains with saturation computation method by conventional does not conform to, and consistent with the conclusion obtained by the present invention.Hence one can see that, and the present invention is more accurate for asking for of oil saturation.It should be noted that, the oil-containing water layer is to contain oily water layer, and its oil saturation is lower.Oil-water-layer is both to have contained oil, contains again the stratum of water, with the oil-containing water layer, compares, and the oil saturation of oil-water-layer is higher.Therefore, oil-containing water layer and oil-water-layer, because the difference of oil saturation is different, are 2 different concepts.
What the embodiment of the present invention provided asks for the method and system of oil saturation based on artificial neural network, first by the experiment of rock electricity, obtains the log data in Archie formula, then is 1 by a and b assignment, and inverse goes out m and n; M, n and the m ' obtained by the BP neural network model, the n ' that will obtain by the experiment of rock electricity compare again, and obtain the BP neural network model of error minimum; Obtain again cementation factor curve and the saturation exponent curve of dynamic change by the BP neural network model of error minimum, the cementation factor of dynamic change and saturation exponent substitution Archie formula are carried out to the calculating of oil saturation.On basis due to the log data of the present invention in known Archie formula, by being 1 by a and b assignment, therefore inverse goes out m and n, has overcome in the conventional method the calculating defect of cementation factor and saturation exponent equalization, has improved the computational accuracy of oil saturation.With conventional method, compare, the oil saturation curve that cementation factor and the saturation exponent of the present invention by dynamic change calculates is sensitiveer for the reaction of oil-gas Layer, and its variation tendency can be reacted the oil-bearing information of Reservoir Section more realistically.
It should be noted last that, the above specific embodiment is only unrestricted in order to technical scheme of the present invention to be described, although with reference to example, the present invention is had been described in detail, those of ordinary skill in the art is to be understood that, can modify or be equal to replacement technical scheme of the present invention, and not breaking away from the spirit and scope of technical solution of the present invention, it all should be encompassed in the middle of claim scope of the present invention.
Claims (10)
1. a method of asking for oil saturation based on artificial neural network, is characterized in that, comprising:
Choose rock core in stratum, described rock core is carried out to the experiment of rock electricity, obtain the log data in Archie formula;
By the lithology factor a in Archie formula and lithology constant b assignment, be 1, inverse obtains cementation factor m and saturation exponent;
Using characterizing the input layer of the log of lithology, physical property and oiliness as described BP neural network model, select described cementation factor m and the described saturation exponent output layer as the BP neural network model; Set up the relation between described output layer and described input layer by simulation, set up the BP neural network model;
To calculate m ', n ' with the m obtained by the experiment of described rock electricity, the described BP neural network model of log data substitution of the corresponding depth of stratum of n;
M, the n that will obtain by the experiment of described rock electricity and m ', the n ' obtained by described BP neural network model compare, and obtain the BP neural network model of error minimum;
The BP neural network model of the described error minimum of data substitution on the described log of corresponding input layer is carried out to cementation factor curve and the saturation exponent curve that node-by-node algorithm obtains dynamic change;
The cementation factor curve of the described dynamic change that obtains and the data substitution Archie formula on the saturation exponent curve are carried out to the calculating of oil saturation.
2. the method for asking for oil saturation based on artificial neural network as claimed in claim 1, is characterized in that, the node number in the intermediate layer of described BP neural network model is 2N+1, the nodes that wherein N is described input layer, and N is greater than 1 integer.
3. the method for asking for oil saturation based on artificial neural network as claimed in claim 2, it is characterized in that, described m, n and the m ' obtained by the BP neural network model, the n ' that will obtain by the experiment of rock electricity compares, the concrete grammar that obtains the BP neural network model of error minimum comprises: regulate the span of the input layer log of described BP neural network model, obtain different m ', n ' value; M, n and the m ' obtained by the BP neural network model, the n ' that will obtain by the experiment of described rock electricity carry out size relatively; According to the relative coefficient of the BP neural network model corresponding with comparative result, obtain the BP neural network model of described error minimum.
4. the method for asking for oil saturation based on artificial neural network as claimed in claim 3, it is characterized in that, the relative coefficient of the BP neural network model that described basis is corresponding with comparative result, obtain the BP neural network model of error minimum, comprise: if the relative coefficient of described BP neural network model equals 0.85, the BP neural network model that the BP neural network model corresponding with relative coefficient 0.85 is described error minimum.
5. the method for asking for oil saturation based on artificial neural network as described as any one in claim 1-4, is characterized in that, the log of described sign lithology comprises gamma ray curve, spontaneous potential curve and CAL; The log of described sign physical property comprises interval transit time curve, porosity curve, neutron curve, density curve and permeability curve; The log of described sign oiliness comprises resistivity curve and stratum shallow resistivity curve in true formation resistivity curve, stratum.
6. a system of asking for oil saturation based on artificial neural network, is characterized in that, comprising:
Rock electricity experiment module, for choose rock core in stratum, carry out the experiment of rock electricity to described rock core, obtains the log data in Archie formula;
The first computing module, be 1 for the lithology factor a by Archie formula and lithology constant b assignment, and inverse obtains cementation factor m and saturation exponent;
The one BP neural network model generation module, the input layer for the log that will characterize lithology, physical property and oiliness as described BP neural network model, select described cementation factor m and the described saturation exponent output layer as the BP neural network model; Set up the relation between described output layer and described input layer by simulation, set up the BP neural network model;
The second computing module, for calculating m ', n ' with the m obtained by the experiment of described rock electricity, the described BP neural network model of log data substitution of the corresponding depth of stratum of n;
The 2nd BP neural network model generation module, compare for m, the n that will obtain by the experiment of described rock electricity and m ', the n ' obtained by described BP neural network model, obtains the BP neural network model of error minimum;
The 3rd computing module, carry out for the BP neural network model of the described error minimum of data substitution on the described log by corresponding input layer cementation factor curve and the saturation exponent curve that node-by-node algorithm obtains dynamic change;
The 4th computing module, carry out the calculating of oil saturation for the cementation factor curve of the described dynamic change by obtaining and the data substitution Archie formula on the saturation exponent curve.
7. the system of asking for oil saturation based on artificial neural network as claimed in claim 6, it is characterized in that, in a described BP neural network model generation module, the node number in the intermediate layer of described BP neural network model is 2N+1, the nodes that wherein N is described input layer, N is greater than 1 integer.
8. the system of asking for oil saturation based on artificial neural network as claimed in claim 7, is characterized in that, described the 2nd BP neural network model generation module comprises:
The index acquiring unit, the span for the input layer log of regulating described BP neural network model, obtain different m ', n ' value;
Comparing unit, carry out size relatively for m, n and the m ' obtained by the BP neural network model, the n ' that will obtain by the experiment of described rock electricity;
BP neural network model generation unit, for the relative coefficient of the BP neural network model according to corresponding with comparative result, obtain the BP neural network model of described error minimum.
9. the system of asking for oil saturation based on artificial neural network as claimed in claim 8, it is characterized in that, described BP neural network model generation unit, if the relative coefficient specifically for described BP neural network model equals 0.85, the BP neural network model that the BP neural network model corresponding with relative coefficient 0.85 is described error minimum.
10. the system of asking for oil saturation based on artificial neural network as described as any one in claim 6-9, is characterized in that, the log of described sign lithology comprises gamma ray curve, spontaneous potential curve and CAL; The log of described sign physical property comprises interval transit time curve, porosity curve, neutron curve, density curve and permeability curve; The log of described sign oiliness comprises resistivity curve and stratum shallow resistivity curve in true formation resistivity curve, stratum.
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