CN107630697A - Based on the formation resistivity joint inversion method for boring electromagnetic wave resistivity logging - Google Patents
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Abstract
The invention discloses a kind of based on the formation resistivity joint inversion method for boring electromagnetic wave resistivity logging, this method obtains with brill electromagnetic wave resistivity logging Amplitude Ratio and phase data first；Summation, the inversion objective function of structure joint constraint are weighted further according to Amplitude Ratio and phase data；The inversion objective function of joint constraint is finally based on, formation resistivity is obtained using particle cluster algorithm inverting.The formation resistivity curve that formation resistivity inversion method provided by the present invention obtains has taken into account the advantage of the preferable radial depth of investigetion of traditional Amplitude Ratio resistivity curve and the preferable longitudinal frame of phase difference resistivity curve, more fully it make use of with brill electromagnetic wave resistivity logging information, and the more traditional inversion method of inverting workload reduces half.
Description
Technical Field
The invention relates to the technical field of oil and gas reservoir development, in particular to a stratum resistivity joint inversion method based on electromagnetic wave resistivity logging while drilling, which is a method for jointly inverting the stratum resistivity by utilizing a phase difference signal and an amplitude ratio signal measured by an electromagnetic wave resistivity logging while drilling instrument.
Background
As one of the main logging-while-drilling technologies, the electromagnetic wave resistivity logging-while-drilling plays an important role in the aspects of geological guiding while drilling and the evaluation of the oil saturation of a reservoir. Compared with other logging instruments for directly measuring the formation resistivity, the actual measurement records of the electromagnetic wave resistivity logging while drilling are two signals of an amplitude ratio and a phase difference, so that the amplitude ratio and the phase difference signals need to be inverted into the formation resistivity when the formation resistivity is evaluated.
At present, when inversion of formation resistivity is carried out by using logging data of electromagnetic wave resistivity while drilling, algorithms such as least square and simulated annealing are usually adopted. The least square algorithm belongs to a local optimization algorithm, the inversion effect depends on the initial precision of model parameters to a great extent, the objective function is easy to fall into a local extreme value, and the simulated annealing algorithm can perform global optimization but still does not completely get rid of the influence of the initial model parameters. Due to the fact that the resistivity of the borehole stratum in different regions changes greatly, when the resistivity of the stratum is inverted, appropriate initial model parameters cannot be obtained accurately under many conditions.
In addition to the inversion algorithm, the constraint mode of inverting the objective function also has a large influence on the inversion result. When the formation resistivity is inverted by using the logging information of the electromagnetic wave resistivity while drilling, the amplitude ratio or the phase difference signal is usually restricted independently in an inversion target function, so that the amplitude ratio resistivity or the phase difference resistivity is obtained through inversion. Theoretical research and practical application results show that the amplitude is deeper than the detection depth of the resistivity, but the longitudinal resolution is poor and has better precision only in low-resistance stratum, and the phase difference resistivity has better longitudinal resolution but relatively shallow radial depth.
Therefore, the amplitude specific resistivity and the phase difference resistivity which are independently utilized cannot well reflect the real resistivity information of the stratum, and the problems of mismatching of longitudinal resolution and radial detection depth exist when the amplitude specific resistivity and the phase difference resistivity are utilized in a combined mode, and the application effect of the electromagnetic wave resistivity logging while drilling is seriously limited.
Disclosure of Invention
The invention has the defects in the prior art, and provides a stratum resistivity joint inversion method based on electromagnetic wave resistivity logging while drilling.
In order to achieve the purpose, the invention provides a formation resistivity joint inversion method based on electromagnetic wave resistivity logging while drilling, which comprises the following steps:
1) acquiring the amplitude ratio and the phase difference data of the resistivity logging of the electromagnetic wave while drilling;
2) carrying out weighted summation according to the amplitude ratio and the phase difference data to construct a joint constraint inversion target function E; because the amplitude ratio of the electromagnetic wave logging while drilling and the amplitude of the phase difference signal are often different by more than one order of magnitude, if the electromagnetic wave logging while drilling is directly combined with the constrained inversion in the objective function, data with larger amplitude can take the dominant role, so that the purpose of the combined constrained inversion cannot be achieved.
3) And (4) carrying out inversion on the target function based on joint constraint and carrying out inversion by utilizing a particle swarm algorithm to obtain the formation resistivity.
Further, in step 1), the amplitude ratio and phase difference data are actual measurement data and forward simulation data, where the amplitude ratio and phase difference data include a forward model amplitude ratio signal, an actual measurement amplitude ratio signal, a forward model phase difference signal, and an actual measurement phase difference signal.
Still further, in step 2), the inversion objective function of the joint constraint is:
E＝||EATTS-EATTM||2+||ΔφS-ΔφM||2·cor2
wherein, EATTSFor forward model amplitude ratio signals, EATTMFor measured amplitude ratio signals, Δ φSFor forward modeling of the phase difference signal, Δ φMIn order to measure the phase difference signal,
cor is amplitude ratio and phase difference signal weighting coefficient, and the expression is:
further, in the step 3), a step of obtaining the formation resistivity by inversion with a particle swarm algorithm is performed:
step S1, obtaining an inversion target function E, and setting the number Num of particles, the distribution range F of the particles and the maximum iteration number itermaxAnd an objective function threshold epsilon;
step S2, obtaining the initial positions of uniformly distributed particles in a given range and the initial movement speeds of randomly distributed particles;
step S3, calculating initial particle swarm individual extreme value P0bestAnd global extremum N0bestInitial model parameter X0＝N0best；
Step S4, according to the particle movement speed and position updating expression, obtaining the updated particle movement speed and updated position coordinate;
step S5, determining the current position and the particle swarm individual extreme value P according to the particle motion speed and the updated position obtained in the step 4)bestAnd global extremum NbestCurrent model parameter X ═ Nbest；
Step S6, initial model parameter X0And current model parametersX is brought into an inversion target function E to respectively obtain model parameter initial values E (X) of the target function E0) And a current value E (X);
step S7 is to obtain the difference Δ E (X) between the initial value and the current value of the model parameter of the objective function0) E (X), namely, determining whether the difference between the initial value and the current value of the model parameter of the target function meets a preset rule, if the current value of the target function is less than or equal to the target function threshold value epsilon, executing step S8, otherwise, returning to execute step S4 to obtain the updated particle motion speed and position again;
step S8, assigning the current model parameter X to the initial model parameter X0Then, step S9 is executed;
step S9, judging whether the iteration number iter is equal to the maximum iteration number itermaxIf equal, executing step S10, otherwise, returning to step S2;
step S10, outputting initial model parameter X0And the resistivity value of the stratum is obtained as an inversion.
And performing multiple joint constraint inversions on the obtained amplitude ratio and phase difference data by using a particle swarm algorithm, and taking the average value of the formation resistivity obtained by inversion as a final inversion result.
Particle Swarm Optimization (PSO) was inspired by Kennedy and Eberhar (1995) by the transfer of information between individuals and groups during foraging of a flock of birds. When a flock searches for a unique food in a particular area, all birds do not know the food there and the best strategy to find the food is to search the surrounding area of the bird that is currently closest to the food.
Based on the bird swarm foraging process, the particle swarm algorithm considers the solution of each optimization problem as a bird in the searched space, which is called a 'particle'. Each particle also has a velocity that determines the direction and distance they fly in each iteration, and in each iteration, the particles update themselves by tracking individual and global extrema, ultimately obtaining an optimal solution that minimizes the overall objective function.
Still further, in step S4), the particle movement speed and position update expressions are:
wherein,respectively updating the motion speeds of the ith particle before and after the iterative update;andrespectively updating the ith particle position before and after iteration; c. C12 and c22 is an acceleration constant used to adjust the maximum step size of the motion to the global optimal particle and the individual optimal particle directions, respectively; ω is a weight coefficient which is derived from ω according tomaxTo omegaminLinear reduction:
wherein itermaxIs the maximum number of iterations, iter is the current number of iterations, ωmax＝1，ωmin＝0。
The invention has the beneficial effects that:
1) in the technical scheme of the embodiment of the invention, the provided stratum resistivity inversion method is based on
The measurement data of the electromagnetic wave resistivity logging instrument while drilling is carried out, an inversion target function of amplitude ratio and phase difference weighting combined constraint is established, and the formation resistivity is obtained through inversion of a particle swarm algorithm.
2) Compared with the traditional inversion method which independently restricts the amplitude ratio and the phase difference, the stratum resistivity curve obtained by the stratum resistivity inversion method provided by the invention has the advantages of better radial detection depth of the traditional amplitude ratio resistivity curve and better longitudinal resolution of the phase difference resistivity curve, more fully utilizes the logging information of the resistivity of the electromagnetic waves while drilling, and the inversion workload is reduced by half compared with the traditional inversion method.
3) The particle swarm optimization is different from a generally adopted least square method or simulated annealing algorithm, not only local optimal values but also global optimal values are searched, and therefore local extreme values can be skipped. In addition, stratum resistivity prior information is not needed, the initial precision of model parameters is not depended on, and the stratum resistivity with higher precision can be obtained through inversion only by setting a more reasonable searching range.
Drawings
FIG. 1 is a schematic diagram of a process for inversion of formation resistivity provided by an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a particle swarm algorithm provided in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a structure of an electromagnetic wave logging while drilling coil system according to an embodiment of the present invention;
FIG. 4a is a schematic diagram of radial detection characteristics of a 500kHz source-distance coil system with a transmission frequency of 0.8m in model 1;
FIG. 4b is a radial detection characteristic diagram of the 500kHz transmission frequency 1m source-to-coil system in the model 1;
FIG. 4c is a schematic diagram of the radial detection characteristic of the 2MHz transmission frequency 0.8m source-distance coil system in model 1;
FIG. 4d is a schematic diagram of the radial detection characteristics of the 2MHz transmission frequency 1m source-distance coil system in model 1;
FIG. 5a is a schematic diagram showing the longitudinal resolution of the 500kHz transmission frequency 0.8m source-to-coil system in model 2;
FIG. 5b is a schematic diagram showing the longitudinal resolution of the 500kHz transmission frequency 1m source-to-coil array in model 2;
FIG. 5c is a schematic diagram of the longitudinal resolution of the 2MHz transmission frequency 0.8m source-to-coil system in model 2;
FIG. 5d is a diagram showing the longitudinal resolution of the 2MHz transmission frequency 1m source-to-coil system in model 2.
Detailed Description
In order to better explain the invention, the following further illustrate the main content of the invention in connection with specific examples, but the content of the invention is not limited to the following examples.
Example 1
The model 1 is an invaded stratum model, the stratum is divided into an invaded zone and an undisturbed stratum along the radial direction from the axial direction of the instrument, and the resistivity R of the invaded zone of the model is used for calculating the response of the electromagnetic wave logging-while-drilling instrument under different invasion conditionsinAnd undisturbed formation resistivity Rt2.5 Ω. m and 10 Ω. m, respectively, and the penetration depth was calculated to be in the range of 0.1 to 6 m. In order to obtain the resistivity value of the invaded stratum model through inversion, as shown in figures 1-2: the specific formation resistivity inversion steps are as follows:
s1) taking the response value of the electromagnetic wave resistivity logging while drilling instrument in the invaded stratum model as actual measurement data, and taking the response value of the instrument in the uniform stratum model as forward model data.
S2) carrying out weighted summation according to the amplitude ratio and the phase difference data to construct a joint constraint inversion target function E;
E＝||EATTS-EATTM||2+||ΔφS-ΔφM||2·cor2
wherein, EATTSFor forward model amplitude ratio signals, EATTMFor measured amplitude ratio signals, EΔφInverting the objective function for the phase difference, Δ φSFor forward modeling of the phase difference signal, Δ φMIn order to measure the phase difference signal,
cor is amplitude ratio and phase difference signal weighting coefficient, and the expression is:
s3) carrying out inversion on the basis of the joint constraint to obtain the formation resistivity by utilizing a particle swarm algorithm; the method comprises the following specific steps:
s301, obtaining an inversion target function E, and setting the number Num of particles, the distribution range F of the particles and the maximum iteration number itermaxAnd an objective function threshold epsilon;
step S302, obtaining uniformly distributed particle initial positions and randomly distributed particle initial movement speeds in a given range;
step S303, calculating an initial particle swarm individual extreme value P0bestAnd global extremum N0bestInitial model parameter X0＝N0best；
Step S304, updating the expression according to the particle movement speed and the position to obtain an updated particle movement speed and an updated position coordinate; the particle motion speed and position updating expressions are respectively as follows:
wherein,respectively updating the motion speeds of the ith particle before and after the iterative update;andrespectively updating the ith particle position before and after iteration; c. C12 and c22 is an acceleration constant used to adjust the maximum step size of the motion to the global optimal particle and the individual optimal particle directions, respectively; ω is a weight coefficient which is derived from ω according tomaxTo omegaminLinear reduction:
wherein itermaxIs the maximum number of iterations, iter is the current number of iterations, ωmax＝1，ωmin＝0；
Step S305, determining the current position and the particle swarm individual extreme value P according to the particle motion speed and the updated position obtained in the step 4)bestAnd global extremum NbestCurrent model parameter X ═ Nbest；
Step S306, initial model parameter X0And the current model parameter X is brought into the target function to respectively obtain the initial value E (X) of the model parameter of the inversion target function E0) And a current value E (X);
step S307 is to obtain a difference Δ E between the initial value and the current value of the model parameter of the objective function as E (X)0) E (X), namely, judging whether the difference value between the initial value and the current value of the model parameter of the objective function conforms to the standard valueA preset rule, if the current value of the target function is less than or equal to the target function threshold epsilon, executing step S308, otherwise, returning to execute step S304 to obtain the updated particle motion speed and position again;
step S308, endowing the current model parameter X to the initial model parameter X0Then, step S309 is performed;
step S309, judging whether the iteration number iter is equal to the maximum iteration number itermaxIf equal, executing step S310, otherwise, returning to step S2;
step S310, outputting initial model parameter X0Formation resistivity values R obtained as an inversiona。
According to the definition of the radial integral geometric factor Gr, when Gr is equal to 0.5, the corresponding radial invasion depth value is the radial detection depth of the electromagnetic wave resistivity logging while drilling instrument, and the calculation expression is as follows:
in the formula, RaFor the formation resistivity value obtained by the inversion of the electromagnetic wave logging while drilling, the amplitude ratio resistivity and the phase difference resistivity obtained by the traditional inversion method and the combined resistivity obtained by the combined inversion method are respectively substituted into the above formula, so that different radial integral geometric factor curves can be obtained by calculation. As shown in fig. 4, for different coil system structures and emission frequencies, the radial integral geometric factor Gr is equal to the radial detection depth value corresponding to 0.5, and it is found that the larger the coil source distance and the lower the emission frequency are, the larger the detection depth of the instrument is; under the fixed source distance and the emission frequency of the coil system, the amplitude ratio is the largest than the radial detection depth corresponding to the resistivity, the combined inversion is the second order of the radial detection depth corresponding to the resistivity, and the phase difference resistivity is the shallowest radial detection depth corresponding to the resistivity. The result shows that the radial depth detected by the apparent resistivity curve obtained by the joint inversion method in the invention is larger than that of the conventional inversion methodThe radial detection characteristics between the amplitude specific resistivity and the phase difference resistivity obtained by the method are better.
Example 2: longitudinal resolution analysis
The method of this example is substantially the same as that of example 1, except that:
the model 2 is a longitudinally layered stratum model, the background stratum resistivity and the interlayer resistivity are respectively 10 Ω. m and 1 Ω. m, the background stratum and the interlayer alternately appear in the model, the stratum thickness range is 0.2 to 2m, and an amplitude ratio and phase difference resistivity curve obtained according to a traditional inversion method and an apparent resistivity curve obtained by a joint inversion method in the invention are shown in fig. 5. As shown in the figure, according to the method for dividing the thickness of the stratum by the traditional curve half-range point, the longitudinal resolution of the apparent conductivity curve is better when the coil source distance is smaller and the transmitting frequency is higher; under the fixed source distance and the fixed emission frequency of the coil system, the vertical resolution of the apparent conductivity curve obtained by the traditional phase difference inversion is the best, the vertical resolution of the apparent conductivity curve obtained by the joint inversion is very close to the vertical resolution of the apparent conductivity curve obtained by the phase difference inversion, and the vertical resolution of the apparent conductivity curve obtained by the traditional amplitude ratio inversion is obviously lower than that obtained by the former two methods.
The results show that the stratum apparent conductivity curve obtained by the joint inversion method provided by the embodiment has the advantages of the traditional radial detection depth of amplitude ratio resistivity and the longitudinal resolution of phase difference resistivity, and can better evaluate the stratum than the traditional single apparent resistivity curve.
Other parts not described in detail are prior art. Although the present invention has been described in detail with reference to the above embodiments, it is only a part of the embodiments of the present invention, not all of the embodiments, and other embodiments can be obtained without inventive step according to the embodiments, and the embodiments are within the scope of the present invention.
Claims (5)
1. A stratum resistivity joint inversion method based on electromagnetic wave resistivity logging while drilling is characterized by comprising the following steps: the method comprises the following steps:
1) acquiring the amplitude ratio and the phase difference data of the resistivity logging of the electromagnetic wave while drilling;
2) carrying out weighted summation according to the amplitude ratio and the phase difference data to construct a joint constraint inversion target function E;
3) and (4) carrying out inversion on the target function based on joint constraint and carrying out inversion by utilizing a particle swarm algorithm to obtain the formation resistivity.
2. The method for jointly inverting the formation resistivity based on electromagnetic wave resistivity logging while drilling as recited in claim 1, wherein: in the step 1), the amplitude ratio and phase difference data are actual measurement data and forward modeling data, wherein the amplitude ratio and phase difference data include a forward model amplitude ratio signal, an actual measurement amplitude ratio signal, a forward model phase difference signal, and an actual measurement phase difference signal.
3. The method for jointly inverting the formation resistivity based on electromagnetic wave resistivity logging while drilling as recited in claim 1, wherein: in the step 2), the inversion objective function E of the joint constraint is as follows:
E＝||EATTS-EATTM||2+||△φS-△φM||2·cor2
wherein, EATTSFor forward model amplitude ratio signals, EATTMFor measured amplitude ratio signals, E△φinverting the target function for the phase difference, △ phiSfor forward modeling of the phase difference signal, Δ φMIn order to measure the phase difference signal,
cor is amplitude ratio and phase difference signal weighting coefficient, and the expression is:
4. the method for jointly inverting the formation resistivity based on electromagnetic wave resistivity logging while drilling as recited in claim 1, wherein: in the step 3), a particle swarm algorithm is used for inversion to obtain the formation resistivity:
step S1, obtaining an inversion target function E, and setting the number Num of particles, the distribution range F of the particles and the maximum iteration number itermaxAnd an objective function threshold epsilon;
step S2, obtaining the initial positions of uniformly distributed particles in a given range and the initial movement speeds of randomly distributed particles;
step S3, calculating initial particle swarm individual extreme value P0bestAnd global extremum N0bestInitial model parameter X0＝N0best；
Step S4, according to the particle movement speed and position updating expression, obtaining the updated particle movement speed and updated position coordinate;
step S5, determining the current position and the particle swarm individual extreme value P according to the particle motion speed and the updated position obtained in the step 4)bestAnd global extremum NbestCurrent model parameter X ═ Nbest；
Step S6, initial model parameter X0And the current model parameter X is brought into the target function to respectively obtain the initial value E (X) of the model parameter of the inversion target function E0) And a current value E (X);
step S7, a difference △ E between the initial value and the current value of the model parameter of the objective function is obtained as E (X)0) E (X), namely, determining whether the difference between the initial value and the current value of the model parameter of the target function meets a preset rule, if the current value of the target function is less than or equal to the target function threshold value epsilon, executing step S8, otherwise, returning to execute step S4 to obtain the updated particle motion speed and position again;
step S8, assigning the current model parameter X to the initial model parameter X0Then, step S9 is executed;
step S9, judging whether the iteration number iter is equal to the maximum iteration number itermaxIf equal, executing step S10, otherwise, returning to step S2;
step S10, outputting initial model parameter X0And the obtained value is used as the parameter value obtained by inversion.
5. The method for jointly inverting the formation resistivity based on electromagnetic wave resistivity logging while drilling as recited in claim 4, wherein: in the step S4), the step S,
the particle motion speed and position updating expressions are respectively as follows:
wherein,andrespectively updating the motion speeds of the ith particle before and after the iterative update;andrespectively updating the ith particle position before and after iteration; c. C12 and c22 is an acceleration constant used to adjust the maximum step size of the motion to the global optimal particle and the individual optimal particle directions, respectively; ω is a weight coefficient which is derived from ω according tomaxTo omegaminLinear reduction:
wherein itermaxIs the maximum number of iterations, iter is the current number of iterations, ωmax＝1ωmin＝0。
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| CN114047554A (en) * | 2021-11-05 | 2022-02-15 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Earth resistivity model modeling method and device, computer equipment and storage medium |
| CN114047554B (en) * | 2021-11-05 | 2024-04-02 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Earth resistivity model modeling method, apparatus, computer device and storage medium |
| CN115267927A (en) * | 2022-09-28 | 2022-11-01 | 中石化经纬有限公司 | Multi-boundary curtain type geosteering method based on ant colony-gradient series algorithm |
| CN115292771A (en) * | 2022-09-30 | 2022-11-04 | 电子科技大学 | Pseudo 2.5D simulation method for resistivity logging while drilling response |
| CN115292771B (en) * | 2022-09-30 | 2023-01-17 | 电子科技大学 | Pseudo 2.5D simulation method for resistivity logging while drilling response |
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