CN115291288B - While-drilling pulse neutron porosity intelligent processing method based on iron neutron marking - Google Patents
While-drilling pulse neutron porosity intelligent processing method based on iron neutron marking Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V5/00—Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity
- G01V5/04—Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity specially adapted for well-logging
- G01V5/08—Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity specially adapted for well-logging using primary nuclear radiation sources or X-rays
- G01V5/10—Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity specially adapted for well-logging using primary nuclear radiation sources or X-rays using neutron sources
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
Abstract
The invention discloses an intelligent processing method for porosity of pulse neutrons while drilling based on iron neutron marking, and relates to the technical field of geophysical logging in a mine field. The method includes the steps of measuring by a pulse-while-drilling neutron porosity logging instrument, measuring a standard graduated well by the pulse-while-drilling neutron porosity logging instrument, simulating by a Monte Carlo numerical simulation method, establishing a graduation relation between a simulation value and an actually-measured value of the pulse-while-drilling neutron porosity logging instrument, simulating by the Monte Carlo numerical simulation method to obtain pulse-while-drilling neutron porosity samples in different logging-while-drilling environments, constructing a sample database and a multilayer sensing neural network, training the multilayer sensing neural network by the sample database to calculate the pulse-while-drilling neutron porosity and verify the accuracy of a calculation result, inputting measured data of the pulse-while-drilling neutron porosity logging instrument after graduation processing into the multilayer sensing neural network, calculating the pulse-while-drilling neutron porosity, and improving the calculation accuracy of the pulse-while-drilling neutron porosity.
Description
Technical Field
The invention relates to the technical field of geophysical logging in a mine field, in particular to an intelligent processing method for porosity of pulse neutrons while drilling based on iron neutron marking.
Background
The accurate measurement of the formation porosity is taken as the key of high-precision formation evaluation, because the neutron porosity logging instrument adopting the traditional chemical source is limited by a series of problems of health, safety, environment and the like in development and application, the neutron porosity logging based on the pulse neutron source becomes the development direction of the porosity logging technology, the emission of neutrons can be controlled by adopting the D-T pulse neutron source, and the radiation risk of the traditional chemical source in the aspects of transportation, construction, storage and the like is avoided.
Compared with the traditional chemical source, the high-energy fast neutrons energy released by the pulse neutron source is 14MeV, and the neutron energy-space-time field distribution is different from that of the traditional chemical source in the process of acting with the formation medium, so that the porosity measurement sensitivity and the influence factors of the high-energy fast neutrons energy released by the pulse neutron source are greatly different from that of the conventional compensated neutrons, and the research in the present stage finds that the high-energy fast neutrons emitted by the pulse neutron source have the phenomenon that the neutron moderation process tends to be saturated in the high-porosity formation, so that the sensitivity of detecting the formation porosity by utilizing thermal neutrons is obviously lower than that of the chemical source, and how to eliminate the phenomenon of neutron moderation saturation in the high-porosity formation is realized, thereby improving the pulse neutron porosity measurement sensitivity to be the key for accurately obtaining the formation porosity.
The logging-while-drilling technology is used as an important means applied to exploration and development of unconventional reservoirs, deep oil gas and deep water oil gas, the drill collar is always in a vibration state in the measurement-while-drilling process, so that a gap exists between the pulsed neutron porosity instrument and a well wall, and meanwhile mud, formation water mineralization and the like adopted in the measurement-while-drilling process can also influence the measurement result of the pulsed neutron porosity while drilling. The traditional neutron porosity processing method corrects the influence of the environment on the neutron porosity by establishing a plate, but in the actual measurement process, the instrument gap and the formation condition are usually difficult to directly obtain. Therefore, it is desirable to provide a method for correcting the influence of environmental factors by using the measurement information of the apparatus itself, so as to improve the measurement accuracy of the porosity of the pulsed neutrons while drilling.
Disclosure of Invention
Aiming at the problem that the measurement precision of a pulse neutron porosity while drilling result is low due to the influence of a measurement-while-drilling environment, the invention discloses an intelligent processing method for the pulse neutron porosity while drilling based on iron neutron marking.
In order to realize the purpose, the invention adopts the following technical scheme:
an intelligent processing method for porosity of pulse neutrons while drilling based on iron neutron marking adopts a pulse neutron porosity logging while drilling instrument to measure, and specifically comprises the following steps:
step 1), placing a pulse neutron porosity logging-while-drilling instrument in a standard calibration well for measurement, obtaining a thermal neutron count and a neutron time spectrum of a near neutron detector, a thermal neutron count and a neutron time spectrum of a far neutron detector and a gamma energy spectrum of a gamma detector, and calculating to obtain measured values of the pulse neutron porosity logging-while-drilling instrument, wherein the measured values comprise a measured value of a macroscopic capture cross section of a borehole medium, a measured value of a macroscopic capture cross section of a formation medium, a measured value of a thermal neutron count ratio, a measured value of a non-bomb gamma count, a measured value of a capture gamma count, a measured value of an iron element characteristic gamma count and a measured value of a chlorine element characteristic gamma count;
step 2), according to the instrument structure of the pulse neutron porosity logging-while-drilling instrument, in combination with the structural parameters of a standard graduated well, establishing a pulse neutron porosity value calculation model while drilling by using a Monte Carlo numerical simulation method, and simulating to obtain a simulated value of the pulse neutron porosity logging-while-drilling instrument, wherein the simulated value comprises a simulated value of a macroscopic capture cross section of a borehole medium, a simulated value of a macroscopic capture cross section of a formation medium, a simulated value of a thermal neutron count ratio, a simulated value of a non-elastic gamma count, a simulated value of a capture gamma count, a simulated value of an iron element characteristic gamma count and a simulated value of a chlorine element characteristic gamma count;
step 3), establishing a scale relation between the analog value and the measured value of the pulse-while-drilling neutron porosity logging instrument based on the analog value and the measured value of the pulse-while-drilling neutron porosity logging instrument;
step 4), changing the size of a drill collar of the pulse neutron porosity logging while drilling instrument according to the instrument structure of the pulse neutron porosity logging while drilling instrument, sequentially changing the lithology, the formation water mineralization, the drill collar gap, the mud type and the formation porosity of a formation medium under different drill collar sizes, reestablishing a pulse neutron porosity value calculation model while drilling, simulating and obtaining the thermal neutron count and the neutron time spectrum of a near neutron detector, the thermal neutron count and the neutron time spectrum of a far neutron detector and the gamma energy spectrum of a gamma detector under different logging while drilling environments, and forming a plurality of groups of pulse neutron porosity while drilling samples according to the drill collar sizes, the macro capture section of a borehole medium, the macro capture section of the formation medium, the counting ratio of iron-marked thermal neutrons, the non-elastic gamma count, the capture gamma count and the gamma count of chlorine element characteristics under different logging while drilling environments;
step 5), constructing a sample database based on the multiple groups of while-drilling pulse neutron porosity samples obtained in the step 4), and dividing the sample database into a training set and a verification set;
step 6), constructing a multilayer perception neural network, and calculating the porosity of the while-drilling pulse neutrons by using the training set to train the multilayer perception neural network;
step 7), verifying the accuracy of the porosity of the pulse-while-drilling neutrons by using the multi-layer perceptive neural network after training by using a verification set;
and 8) based on the scale relation between the analog value and the measured value of the pulse-while-drilling neutron porosity logging instrument established in the step 3), performing scale processing on a borehole medium macroscopic capture section, a formation medium macroscopic capture section, a thermal neutron count ratio, a non-ballistic gamma count, a capture gamma count, an iron element characteristic gamma count and a chlorine element characteristic gamma count which are actually measured by the pulse-while-drilling neutron porosity logging instrument, marking the thermal neutron count ratio by using the iron element characteristic gamma count, inputting the borehole medium macroscopic capture section, the formation medium macroscopic capture section, the non-ballistic gamma count, the capture gamma count, the chlorine element characteristic gamma count, the iron-marked thermal neutron count ratio and the drill collar size of the pulse-while-drilling neutron porosity logging instrument after scale processing into a multilayer sensing neural network, and calculating by using the multilayer sensing neural network to obtain the pulse-while-drilling neutron porosity.
Preferably, the pulse-while-drilling neutron porosity logging instrument is arranged in a drill collar, a slurry flow guide channel is arranged in the drill collar, the outer wall of a shell of the pulse-while-drilling neutron porosity logging instrument is attached to the inner wall of the drill collar, and a far neutron detector, a gamma detector, a near neutron detector, a tungsten-nickel-iron shield and a pulse neutron source are arranged in the shell at intervals from top to bottom.
Preferably, the near neutron detector and the far neutron detector are both in a cylinder structure, the diameter of the near neutron detector is set to be 4cm, the length of the near neutron detector is set to be 5cm, the diameter of the far neutron detector is set to be 6cm, the length of the far neutron detector is set to be 10cm, the diameter of the gamma detector is set to be 4cm, the length of the gamma detector is set to be 4cm, the source distance of the near neutron detector is set to be 35cm, the source distance of the far neutron detector is set to be 70cm, and the source distance of the gamma detector is set to be 50cm.
Preferably, the step 3) includes a calibration relation between the borehole medium macroscopic capture section simulation value and the measured value, a calibration relation between the formation medium macroscopic capture section simulation value and the measured value, a calibration relation between the thermal neutron count ratio simulation value and the measured value, a calibration relation between the non-bomb-gamma-count simulation value and the measured value, a calibration relation between the capture gamma-count simulation value and the measured value, a calibration relation between the iron element characteristic gamma-count simulation value and the measured value, and a calibration relation between the chlorine element characteristic gamma-count simulation value and the measured value.
Preferably, in the step 4), the drill collar sizes of the logging-while-drilling pulsed neutron porosity logging instrument are sequentially set to be 12.065cm, 17.145cm and 20.32cm, the lithology of the formation medium is sequentially set to be sandstone, limestone, dolomite, sand-ash mixed rock and sand-dolomite mixed rock, wherein the sand-ash mixed rock is formed by mixing sandstone and limestone, the sand-dolomite mixed rock is formed by mixing sandstone and dolomite, the water mineralization of the formation is sequentially set to be 0kppm, 5kppm, 10kppm, 20kppm, 50kppm, 100kppm and 200kppm, the drill collar gaps are sequentially set to be 0cm, 0.5cm, 1cm, 1.5cm and 2cm, the mud types include oil-based mud and water-based mud, and the formation porosity is sequentially set to be 0, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45% and 50%.
Preferably, in the step 4), the thermal neutron count ratio is marked by using the characteristic gamma count of the iron element, so as to obtain an iron-marked thermal neutron count ratio, as shown in formula (1):
in the formula (I), the compound is shown in the specification,
the counting ratio of the thermal neutrons is marked for the iron,
is the counting ratio of the thermal neutrons,
and (4) performing gamma counting for the characteristic iron element after normalization treatment.
Preferably, the step 6) specifically comprises the following steps:
6.1, constructing a multilayer perception neural network, wherein the multilayer perception neural network is provided with an input layer, a first hidden layer, a second hidden layer and an output layer, the input layer, the first hidden layer, the second hidden layer and the output layer are sequentially connected, 7 nodes are arranged in the input layer and are used for obtaining input parameters of the multilayer perception neural network, the input parameters comprise the size of a drill collar in a pulse neutron porosity while drilling sample, a macroscopic capture section of a borehole medium, a macroscopic capture section of a formation medium, a counting ratio of iron-marked thermal neutrons, a non-elastic gamma count, a capture gamma count and a chlorine element characteristic gamma count, 10 nodes are arranged in the first hidden layer, 15 nodes are arranged in the second hidden layer, the first hidden layer and the second hidden layer are connected through a ReLU activation function, the second hidden layer and the output layer are connected through a tanh activation function, and 1 node is arranged in the output layer and is used for outputting the calculated pulse neutron porosity while drilling;
step 6.2, setting a porosity precision value of the pulse neutrons while drilling;
step 6.3, randomly selecting a pulse neutron porosity while drilling sample in the training set, inputting the pulse neutron porosity while drilling sample into the multilayer perception neural network, enabling nodes in the input layer of the multilayer perception neural network to obtain input parameters in the pulse neutron porosity while drilling sample, activating the input parameters through a ReLU function, and transmitting the activated input parameters to the first hidden layer, wherein the output result of the first hidden layer is shown as a formula (2):
in the formula (I), the compound is shown in the specification,
as a result of the output of the first hidden layer,
is an input parameter of the multi-layer perceptive neural network,
is the size of a drill collar,
the ratio of the counts of the thermal neutrons to the iron marker,
the counting is carried out for the non-bullet gamma counting,
in order to capture the gamma counts,
is the gamma counting of the characteristic of the chlorine element,
for the macroscopic capture cross-section of the wellbore medium,
is a macroscopic trapping cross section of the formation medium,
is the weight of the first hidden layer,
is a first hidingBiasing of the layers;
the output result of the first hidden layer is activated by the ReLU function and then transmitted to the second hidden layer, and the output result of the second hidden layer is shown in formula (3):
in the formula (I), the compound is shown in the specification,
is the output result of the second hidden layer,
is the weight of the second hidden layer,
a bias for the second hidden layer;
the output result of the second hidden layer is activated by the tanh function and then transmitted to the output layer, and the porosity of the while-drilling pulse neutrons output by the output layer is as follows:
in the formula (I), the compound is shown in the specification,
the porosity of the pulse neutron output by the output layer while drilling,
as a weight of the output layer,
is the bias of the output layer;
step 6.4, calculating the formation porosity of the model by combining the porosity value of the while-drilling pulse neutrons corresponding to the while-drilling pulse neutron porosity sample according to the porosity of the while-drilling pulse neutrons output by the output layer, calculating the porosity error value of the while-drilling pulse neutrons of the multilayer perception neural network, and comparing the porosity error value with the preset precision value of the porosity of the while-drilling pulse neutrons; if the while-drilling pulse neutron porosity error value of the multilayer perception neural network is larger than the set while-drilling pulse neutron porosity precision value, updating the weight and bias of each layer in the multilayer perception neural network by using a logcosh loss function and adopting a back propagation and random gradient descent method, returning to the step 6.3, and continuing to train the multilayer perception neural network; and if the while-drilling pulse neutron porosity error value of the multilayer perception neural network is not less than the set while-drilling pulse neutron porosity precision value, stopping training the multilayer perception neural network, and entering the step 7).
Preferably, in step 6.4, the update formula of the weights and the biases of each layer in the multilayer perceptive neural network is as follows:
in the formula (I), the compound is shown in the specification,
to minimize the value of the variable at which the logcosh loss function takes a minimum,
as logcosh loss function.
Preferably, in the step 7), all the pulse-while-drilling neutron porosities in the verification set are input into the trained multilayer perceptive neural network, a calculation value of the pulse-while-drilling neutron porosity is calculated by using the multilayer perceptive neural network for each pulse-while-drilling neutron porosity sample, the formation porosity of the model is calculated by combining the pulse-while-drilling neutron porosity values corresponding to the pulse-while-drilling neutron porosity samples, an absolute error value of the pulse-while-drilling neutron porosity is calculated by using the multilayer perceptive neural network by using an absolute error function, and if the absolute error value is less than 1%, the calculation result of the pulse-while-drilling neutron porosity of the multilayer perceptive neural network is proved to be accurate;
the calculation formula of the absolute error value of the porosity of the while-drilling pulse neutrons is as follows:
in the formula (I), the compound is shown in the specification,
is the absolute error value of the porosity of the while-drilling pulse neutrons,
to verify the number of concentrated while-drilling pulsed neutron porosity samples,
in order to verify the serial number of the porosity sample of the concentrated while-drilling pulse neutron,
for multi-layer perceptive neural network according to
A pulse-while-drilling neutron porosity value calculated for each pulse-while-drilling neutron porosity sample,
is as follows
And calculating the formation porosity of the model according to the pulse-while-drilling neutron porosity value corresponding to the pulse-while-drilling neutron porosity sample.
The invention has the beneficial effects that:
according to the intelligent processing method for the porosity of the pulse neutrons while drilling based on the iron neutron marker, provided by the invention, the iron element characteristic gamma counting is used as a marking factor to process the counting ratio of the thermal neutrons, so that the influence of the energy of a pulse neutron source on the distribution of a neutron field is eliminated, and the measurement sensitivity of a pulse neutron porosity logging-while-drilling instrument is improved.
Meanwhile, the method trains a multilayer perception neural network by using a pulse-while-drilling neutron porosity sample obtained by Monte Carlo simulation, inputs the drill collar size of the pulse-while-drilling neutron porosity instrument, the measured macroscopic capture cross section of a borehole medium, the macroscopic capture cross section of a stratum medium, the iron-marked thermal neutron count ratio, the non-elastic gamma count, the capture gamma count and the chlorine element characteristic gamma count into the multilayer perception neural network as input parameters, calculates and obtains the real pulse-while-drilling neutron porosity by using the multilayer perception neural network, automatically eliminates errors caused by measurement-while-drilling environments such as the borehole environment, the drill collar and the like, improves the calculation precision of the pulse-while-drilling neutron porosity, ensures the accuracy of the calculation result of the pulse-while-drilling neutron porosity, and lays a foundation for high-precision stratum evaluation.
Drawings
FIG. 1 is a schematic structural diagram of a pulse-neutron porosity while drilling logging instrument according to the present invention. In the figure, 1 is a stratum, 2 is a drill collar, 3 is a slurry diversion channel, 4 is a far neutron detector, 5 is a gamma detector, 6 is a near neutron detector, 7 is a tungsten-nickel-iron shield, and 8 is a pulse neutron source.
FIG. 2 is a graph of capture gamma energy for different formation porosity limestone formations.
FIG. 3 is a graph of the characteristic gamma counts of elemental iron as a function of formation porosity.
FIG. 4 is a graph of the response of thermal neutron count ratio and iron-labeled thermal neutron count ratio with formation porosity.
FIG. 5 is a plot of formation porosity sensitivity versus thermal neutron count ratio and iron-labeled thermal neutron count ratio.
Fig. 6 is a curve of logcosh loss function value with training times in the process of multi-layer perceptive neural network training.
FIG. 7 is a distribution frequency diagram for verifying absolute error values of concentrated while-drilling pulsed neutron porosity samples.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
After entering the stratum, fast neutrons emitted by the pulse neutron source react with the nuclei of stratum substances through inelastic scattering, elastic scattering, radiation capture and the like, and the energy of the neutrons is reduced to become epithermal neutrons and thermal neutrons. The distribution of thermal neutron flux in the formation depends on the distance traveled by the neutrons
And the deceleration length of the formation medium
The formation density also has negligible effect on the thermal neutron distribution, so the distribution of thermal neutrons in the formation is affected by both the hydrogen index and the formation density.
According to the theory of double-group diffusion, the ratio of the counts of thermal neutrons recorded by two neutron detectors in an infinite homogeneous medium
The approximate description is:
in the formula (I), the compound is shown in the specification,
is the thermal neutron count rate of the near neutron detector,
is the thermal neutron count rate of the far neutron detector,
is the source distance of the near-neutron detector,
is the source distance of the far neutron detector,
is the deceleration length of the formation medium.
Because the energy of the pulse neutron source is higher than that of an americium-beryllium neutron source, high-energy neutrons emitted by the pulse neutron source mainly have an inelastic scattering effect with a rock framework after entering a stratum and are greatly influenced by the stratum density, and the porosity sensitivity measured by the pulse neutron porosity logging method in a high-porosity stratum is lower than that of an Am-Be neutron source.
The method is characterized in that the characteristic gamma counting of iron element is only related to the flux distribution of thermal neutrons, the characteristic gamma counting of silicon element and the characteristic gamma counting of calcium element are mainly related to the lithology of the stratum, and the characteristic gamma counting of chlorine element and the macroscopic capture cross section are mainly related to the mineralization of stratum water.
The invention provides an intelligent processing method of pulse-while-drilling neutron porosity based on iron neutron marking, which is characterized in that a pulse-while-drilling neutron porosity logging instrument is adopted to measure in a stratum, as shown in figure 1, the pulse-while-drilling neutron porosity logging instrument is arranged in a drill collar 2, a mud diversion channel 3 is arranged in the drill collar 2, the outer wall of a shell of the pulse-while-drilling neutron porosity logging instrument is attached to the inner wall of the drill collar 2, and a far neutron detector 4, a gamma detector 5, a near neutron detector 6, a tungsten-nickel-iron shield 7 and a pulse neutron source 8 are arranged in the shell at intervals from top to bottom.
In this embodiment, the pulse neutron source 8 is set as a D-T pulse neutron generator, the D-T pulse neutron generator emits fast neutrons into the formation 1 in a pulse form, the near neutron detector and the far neutron detector are both in a cylinder structure, the diameter of the near neutron detector is set to be 4cm, the length of the near neutron detector is set to be 5cm, the diameter of the far neutron detector is set to be 6cm, the length of the far neutron detector is set to be 10cm, the diameter of the gamma detector is set to be 4cm, the length of the gamma detector is set to be 4cm, the source distance of the near neutron detector is set to be 35cm, the source distance of the far neutron detector is set to be 70cm, and the source distance of the gamma detector is set to be 50cm.
The invention provides an intelligent processing method for porosity of pulse neutrons while drilling based on iron neutron marking, which specifically comprises the following steps:
step 1, placing a pulse neutron porosity logging-while-drilling instrument in a standard calibration well for measurement, setting the iron element energy window range of a gamma detector to be 6.4-7.8MeV, the chlorine element energy window range to be 1.02-1.23MeV, setting the non-bomb gamma recording time period of the gamma detector to be 0-20 mu s, setting the capture gamma recording time period to be 60-160 mu s, obtaining the thermal neutron count and neutron time spectrum of a near neutron detector, the thermal neutron count and neutron time spectrum of a far neutron detector and the gamma energy spectrum of the gamma detector, and calculating to obtain the measured value of the pulse neutron porosity logging-while-drilling instrument, the method comprises an actual measurement value of a macroscopic capture section of a borehole medium, an actual measurement value of a macroscopic capture section of a formation medium, an actual measurement value of a thermal neutron count ratio, an actual measurement value of a non-bomb gamma counting, an actual measurement value of a capture gamma counting, an actual measurement value of an iron element characteristic gamma counting and an actual measurement value of a chlorine element characteristic gamma counting, wherein the macroscopic capture section of the borehole medium and the macroscopic capture section of the formation medium are determined by utilizing neutron time spectrums of a near neutron detector and a far neutron detector, the thermal neutron count ratio is determined by utilizing the thermal neutron counting of the near neutron detector and the far neutron detector, and the non-bomb gamma counting, the capture gamma counting, the iron element characteristic gamma counting and the chlorine element characteristic gamma counting are determined by utilizing a gamma energy spectrum.
And 2, establishing a pulse neutron porosity while drilling numerical calculation model by using a Monte Carlo numerical simulation method according to the instrument structure of the pulse neutron porosity while drilling instrument and combining the structural parameters of a standard graduated well, and simulating to obtain a simulated value of the pulse neutron porosity while drilling instrument, wherein the simulated value comprises a simulated value of a macroscopic capture cross section of a borehole medium, a simulated value of a macroscopic capture cross section of a formation medium, a simulated value of a thermal neutron count ratio, a simulated value of a non-elastic gamma count, a simulated value of a capture gamma count, a simulated value of an iron element characteristic gamma count and a simulated value of a chlorine element characteristic gamma count. FIG. 2 is a capture gamma energy spectrum of a limestone formation under different formation porosity conditions, wherein the capture gamma energy spectrum of the limestone formation with the formation porosity of 5%, the capture gamma energy spectrum of the limestone formation with the formation porosity of 20% and the capture gamma energy spectrum of the limestone formation with the porosity of 40% are shown, and the capture gamma energy spectra under different formation porosity conditions are compared, so that the capture gamma counts of the capture gamma energy spectra under the energy range of 6.5 to 7.8MeV are reduced along with the increase of the formation porosity, and the difference between the capture gamma counts in other energy sections is smaller. Fig. 3 shows the change rule of the characteristic gamma count of the iron element with the porosity of the formation, which can be obtained from fig. 3, and even in a high-porosity formation, the characteristic gamma count of the iron element still changes with the porosity of the formation, and by analyzing fig. 2 and fig. 3, the characteristic gamma count of the iron element can reflect the distribution of the thermal neutron field in the formation. Therefore, the influence of the neutron source type on the neutron field distribution can be eliminated by marking the thermal neutron counting ratio by utilizing the characteristic gamma counting of the iron element, and the sensitivity of the instrument on the porosity measurement of the pulse neutrons is improved. Marking the thermal neutron count ratio by utilizing iron element characteristic gamma counting to obtain the iron-marked thermal neutron count ratio as shown in a formula (1):
in the formula (I), the compound is shown in the specification,
the counting ratio of the thermal neutrons is marked for the iron,
is the counting ratio of the thermal neutrons,
and (4) performing gamma counting for the characteristic iron element after normalization treatment.
FIG. 4 is a graph showing the response law of the thermal neutron count ratio and the iron-labeled thermal neutron count ratio with the formation porosity, which can be obtained from FIG. 4, wherein the thermal neutron count ratios all increase with the increase of the formation porosity, the thermal neutron count ratio remains unchanged after the formation porosity exceeds 20%, and the iron-labeled thermal neutron count ratio has an obvious change law in the whole formation porosity distribution range, a change curve of the formation porosity relative sensitivity of the thermal neutron count ratio with the formation porosity and a change curve of the formation porosity relative sensitivity of the iron-labeled thermal neutron count ratio with the formation porosity are obtained through calculation, as shown in FIG. 5, the formation porosity relative sensitivity of the iron-labeled thermal neutron count ratio is higher than that of the thermal neutron count ratio, and when the formation porosity is 30%, the formation porosity relative sensitivity of the iron-labeled thermal neutron count ratio is about 2.7 times that of the thermal neutron count ratio.
And 3, establishing a scale relation between the analog value and the measured value of the pulse-while-drilling neutron porosity logging instrument based on the analog value and the measured value of the pulse-while-drilling neutron porosity logging instrument, wherein the scale relation between the analog value and the measured value of the pulse-while-drilling neutron porosity logging instrument comprises a scale relation between a macroscopic capturing cross section analog value and the measured value of the borehole medium, a scale relation between a macroscopic capturing cross section analog value and the measured value of the formation medium, a scale relation between a thermal neutron count ratio analog value and the measured value, a scale relation between a non-bomb gamma counting analog value and the measured value, a scale relation between a capturing gamma counting analog value and the measured value, a scale relation between an iron element characteristic gamma counting analog value and the measured value and a scale relation between a chlorine element characteristic gamma counting analog value and the measured value.
And 4, in addition to the source energy difference, the distribution difference of the neutron field in the stratum can be caused by environmental factors such as lithology, stratum water mineralization and the like, and the problem that iron marking factors are difficult to obtain exists in the actual measurement process, so that the measurement sensitivity and the measurement accuracy of the pulse neutron porosity logging-while-drilling instrument under different measurement-while-drilling environments are improved.
According to the instrument structure of the pulse-while-drilling neutron porosity logging instrument, the size of a drill collar of the pulse-while-drilling neutron porosity logging instrument is changed, the lithology, the formation water mineralization, the drill collar gap, the mud type and the formation porosity of a formation medium are sequentially changed under the condition of different drill collar sizes, a pulse-while-drilling neutron porosity value calculation model is reestablished, the thermal neutron count and the neutron time spectrum of a near neutron detector, the thermal neutron count and the neutron time spectrum of a far neutron detector and the gamma energy spectrum of a gamma detector under different logging-while-drilling environments are obtained in a simulated mode, and a plurality of groups of pulse-while-drilling neutron porosity samples are formed according to the drill collar sizes, the macro capture cross section of a borehole medium, the macro capture cross section of the formation medium, the counting ratio of iron-marked thermal neutrons, the non-elastic gamma count, the capture gamma count and the gamma counting of chlorine element characteristics under different logging-while-drilling environments.
In the embodiment, the drill collar sizes of the pulse neutron porosity logging-while-drilling instrument are sequentially set to be 12.065cm, 17.145cm and 20.32cm, after the drill collar sizes are fixed, the lithology of a formation medium, the formation water mineralization, the drill collar gap, the mud type and the formation porosity in the model are calculated by sequentially modifying the value of the pulse neutron porosity while-drilling value, wherein the lithology of the formation medium is sequentially set to be sandstone, limestone, dolomite and sand-lime mixed rock (formed by mixing sandstone and limestone, the proportion of the limestone is increased from 0 to 100%, and is increased by 10% each time), the formation water mineralization is sequentially set to be 0kppm, 5kppm, 10kppm, 20kppm, 50kppm, 100kppm and 200kppm, the drill collar gap is sequentially set to be 0cm, 0.5cm, 1cm, 1.5cm and 2cm, the water-based mud type is sequentially set to be 0kppm, 20kppm, 50kppm, 100kppm and 200kppm, the mud porosity is sequentially set to be 0cm, 0.5cm, 25%, 40%, 35%, and 35%. Different measurement-while-drilling environments are simulated by modifying the calculation model of the porosity value of the pulse-while-drilling neutrons, so that the sizes of drill collars, the macroscopic capture cross section of a borehole medium, the macroscopic capture cross section of a formation medium, the counting ratio of iron-marked thermal neutrons, the non-bomb gamma counting, the capture gamma counting and the chlorine element characteristic gamma counting in the different measurement-while-drilling environments are obtained, and 10000 groups of measurement-while-drilling pulse neutron porosity samples are obtained in the embodiment.
And 5, constructing a sample database based on the multiple groups of pulse-while-drilling neutron porosity samples obtained in the step 4, and dividing the sample database into a training set and a verification set, wherein the number of pulse-while-drilling neutron porosity samples in the training set accounts for 85% of the total number of pulse-while-drilling neutron porosity samples in the sample database, and the number of pulse-while-drilling neutron porosity samples in the verification set accounts for 15% of the total number of pulse-while-drilling neutron porosity samples in the sample database.
6.1, constructing a multilayer perception neural network, wherein the multilayer perception neural network is provided with an input layer, a first hidden layer, a second hidden layer and an output layer, the input layer, the first hidden layer, the second hidden layer and the output layer are sequentially connected, 7 nodes are arranged in the input layer and used for obtaining input parameters of the multilayer perception neural network, the input parameters comprise drill collar size, borehole medium macroscopic capture cross section, formation medium macroscopic capture cross section, iron marker thermal neutron count ratio, non-bomb gamma counting, capture gamma counting and chlorine element characteristic gamma counting in a while-drilling pulse neutron porosity sample, 10 nodes are arranged in the first hidden layer, 15 nodes are arranged in the second hidden layer, the first hidden layer and the second hidden layer are connected through a ReLU activation function, the second hidden layer and the output layer are connected through a tanh activation function, and 1 node is arranged in the output layer and used for outputting calculated while-drilling pulse neutron porosity.
And 6.2, setting a porosity precision value of the pulse neutrons while drilling.
Step 6.3, randomly selecting a pulse neutron porosity while drilling sample in the training set, inputting the pulse neutron porosity while drilling sample into the multilayer perception neural network, enabling nodes in the input layer of the multilayer perception neural network to obtain input parameters in the pulse neutron porosity while drilling sample, activating the input parameters through a ReLU function, and transmitting the activated input parameters to the first hidden layer, wherein the output result of the first hidden layer is shown as a formula (2):
in the formula (I), the compound is shown in the specification,
as a result of the output of the first hidden layer,
is an input parameter of the multi-layer perceptive neural network,
is the size of a drill collar,
the ratio of the counts of the thermal neutrons to the iron marker,
the counting is carried out for the non-bullet gamma counting,
in order to capture the gamma counts,
is the gamma counting of the characteristic of the chlorine element,
is a macroscopic capture cross-section for the wellbore medium,
is a macroscopic trapping cross section of the formation medium,
is the weight of the first hidden layer,
is the bias of the first hidden layer.
The output result of the first hidden layer is activated by the ReLU function and then transmitted to the second hidden layer, and the output result of the second hidden layer is shown in formula (3):
in the formula (I), the compound is shown in the specification,
as a result of the output of the second hidden layer,
is the weight of the second hidden layer and,
is the bias of the second hidden layer.
The output result of the second hidden layer is activated by the tanh function and then transmitted to the output layer, and the porosity of the while-drilling pulse neutrons output by the output layer is as follows:
in the formula (I), the compound is shown in the specification,
the porosity of the pulse neutron output by the output layer while drilling,
as a weight of the output layer,
is the bias of the output layer.
Step 6.4, calculating the formation porosity of the model by combining the porosity value of the while-drilling pulse neutrons corresponding to the while-drilling pulse neutron porosity sample according to the porosity of the while-drilling pulse neutrons output by the output layer, calculating the porosity error value of the while-drilling pulse neutrons of the multilayer perception neural network, and comparing the porosity error value with the preset precision value of the porosity of the while-drilling pulse neutrons; if the porosity error value of the while-drilling pulse neutrons of the multilayer perception neural network is larger than the set porosity precision value of the while-drilling pulse neutrons, updating the weight and bias of each layer in the multilayer perception neural network by using a formula (5) based on a logcosh loss function through a back propagation and random gradient descent method, returning to the step 6.3, and continuing to train the multilayer perception neural network; and if the porosity error value of the while-drilling pulse neutrons of the multilayer perception neural network is not less than the set porosity precision value of the while-drilling pulse neutrons, stopping training the multilayer perception neural network, and entering the step 7.
In this embodiment, as shown in fig. 6, when the training frequency of the multilayer perceptual neural network reaches 400 times, the loss function value is stable, the error value of the pulse-while-drilling neutron porosity of the multilayer perceptual neural network is not less than the set precision value of the pulse-while-drilling neutron porosity, and the calculation precision of the pulse-while-drilling neutron porosity of the multilayer perceptual neural network is stable.
And 7, verifying the accuracy of the porosity of the pulse-while-drilling neutrons by using the multi-layer perceptive neural network after training by using a verification set, inputting all the porosity of the pulse-while-drilling neutrons in the verification set into the multi-layer perceptive neural network after training, respectively calculating the porosity of the pulse-while-drilling neutrons by using the multi-layer perceptive neural network for each sample of the porosity of the pulse-while-drilling neutrons to obtain a calculated value of the porosity of the pulse-while-drilling neutrons, calculating the absolute error value of the porosity of the pulse-while-drilling neutrons by using a formula (6) in combination with the porosity of the pulse-while-drilling neutrons corresponding to the sample of the porosity of the pulse-while-drilling neutrons, and proving that the calculation result of the porosity of the pulse-while-drilling neutrons in the multi-layer perceptive neural network is accurate if the absolute error value is less than 1%.
In the embodiment, a distribution frequency diagram of absolute error values of concentrated while-drilling pulse neutron porosity samples is verified, as shown in fig. 7, the distribution frequency diagram can be obtained from fig. 7, the true value that the porosity of the while-drilling pulse neutrons is close to the porosity of the while-drilling pulse neutrons is obtained through calculation of the multilayer perception neural network trained by the method, the absolute error between the porosity of the while-drilling pulse neutrons calculated by the multilayer perception neural network after training and the true value of the formation porosity is basically smaller than 1%, and the accuracy of the multilayer perception neural network used for calculating the porosity of the while-drilling pulse neutrons after training by the method is verified.
And 8, based on the scale relation between the simulated value and the measured value of the pulse-while-drilling neutron porosity logging instrument established in the step 3, performing scale processing on a borehole medium macroscopic capture section, a formation medium macroscopic capture section, a thermal neutron count ratio, a non-elastic gamma count, a capture gamma count, an iron element characteristic gamma count and a chlorine element characteristic gamma count which are actually measured by the pulse-while-drilling neutron porosity logging instrument, marking the thermal neutron count ratio by using the iron element characteristic gamma count, inputting the borehole medium macroscopic capture section, the formation medium macroscopic capture section, the non-elastic gamma count, the capture gamma count, the chlorine element characteristic gamma count, the iron mark thermal neutron count ratio and the drill collar size of the pulse-while-drilling neutron porosity logging instrument after scale processing into a multilayer perception neural network, and calculating by using the multilayer perception neural network to obtain the pulse-while-drilling neutron porosity.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (8)
1. An intelligent processing method for porosity of pulse neutrons while drilling based on iron neutron marking is characterized in that a pulse neutron porosity logging while drilling instrument is adopted for measurement, and comprises the following steps:
1) Placing a pulse neutron porosity logging-while-drilling instrument in a standard calibration well for measurement to obtain an actual measurement value of the pulse neutron porosity logging-while-drilling instrument;
2) Establishing a while-drilling pulse neutron porosity numerical calculation model by using a Monte Carlo numerical simulation method to obtain a simulation value of a while-drilling pulse neutron porosity logging instrument;
3) Establishing a scale relation between the analog value and the measured value of the pulse-while-drilling neutron porosity logging instrument based on the analog value and the measured value of the pulse-while-drilling neutron porosity logging instrument;
4) Changing the size of a drill collar of a pulse-while-drilling neutron porosity logging instrument, sequentially changing the lithology, the water mineralization of the stratum, the gap of the drill collar, the mud type and the porosity of the stratum under different drill collar sizes, reestablishing a pulse-while-drilling neutron porosity value calculation model, and simulating to obtain the size of the drill collar, the macroscopic capture section of the borehole medium, the macroscopic capture section of the stratum medium, the counting ratio of iron-marked thermal neutrons, the non-elastic gamma counting, the capture gamma counting and the chlorine element characteristic gamma counting under different logging-while-drilling environments to form a plurality of groups of pulse-while-drilling neutron porosity samples;
5) Constructing a sample database based on a plurality of groups of while-drilling pulse neutron porosity samples, and dividing the sample database into a training set and a verification set;
6) Constructing a multilayer perception neural network, and calculating the porosity of the pulse neutrons while drilling by using a training set to train the multilayer perception neural network;
7) Verifying the accuracy of the porosity of the neutrons in the while-drilling pulse by using the multi-layer perception neural network after training by using a verification set;
8) Based on the scale relation between the analog value and the measured value of the pulse neutron porosity logging-while-drilling instrument, after a borehole medium macroscopic capture section, a formation medium macroscopic capture section, a non-elastic gamma count, a capture gamma count, a chlorine element characteristic gamma count and an iron-marked thermal neutron count ratio which are actually measured by the pulse neutron porosity logging-while-drilling instrument are obtained through scale processing, the obtained values and the size of the drill collar are input into a multilayer perception neural network together, and the pulse neutron porosity-while-drilling is obtained through calculation;
in the step 4), the iron-labeled thermal neutron counting ratio is obtained by labeling the thermal neutron counting ratio by iron element characteristic gamma counting, as shown in formula (1):
in the formula (I), the compound is shown in the specification,
the counting ratio of the thermal neutrons is marked for the iron,
is the counting ratio of the thermal neutrons,
and (4) carrying out gamma counting on the iron element characteristics after normalization treatment.
2. The intelligent processing method for porosity of pulse-while-drilling neutrons based on iron neutron marking as claimed in claim 1, wherein the pulse-while-drilling neutron porosity logging instrument is arranged in a drill collar, a mud diversion channel is arranged in the drill collar, the outer wall of a shell of the pulse-while-drilling neutron porosity logging instrument is closely attached to the inner wall of the drill collar, and a far neutron detector, a gamma detector, a near neutron detector, a ferrotungsten shield and a pulse neutron source are arranged in the shell at intervals from top to bottom.
3. The intelligent processing method for porosity of pulse-while-drilling neutrons based on iron neutron marking as claimed in claim 2, wherein the near neutron detector and the far neutron detector are both in a cylinder structure, the diameter of the near neutron detector is set to be 4cm, the length of the near neutron detector is set to be 5cm, the diameter of the far neutron detector is set to be 6cm, the length of the far neutron detector is set to be 10cm, the diameter of the gamma detector is set to be 4cm, the length of the gamma detector is set to be 4cm, the source distance of the near neutron detector is set to be 35cm, the source distance of the far neutron detector is set to be 70cm, and the source distance of the gamma detector is set to be 50cm.
4. The method as claimed in claim 1, wherein in the step 3), the calibration relationship between the simulated value and the measured value of the LWD tool includes a calibration relationship between the simulated value and the measured value of the macroscopic capture cross-section of the borehole medium, a calibration relationship between the simulated value and the measured value of the macroscopic capture cross-section of the formation medium, a calibration relationship between the simulated value and the measured value of the thermal neutron count ratio, a calibration relationship between the simulated value and the measured value of the non-bomb gamma count, a calibration relationship between the simulated value and the measured value of the capture gamma count, a calibration relationship between the simulated value and the measured value of the iron element gamma count, and a calibration relationship between the simulated value and the measured value of the chlorine element gamma count.
5. The intelligent iron neutron marker-based pulsed neutron porosity while drilling processing method as claimed in claim 1, wherein in step 4), the drill collar sizes of the pulsed neutron porosity while drilling logging tool are set to be 12.065cm, 17.145cm and 20.32cm in sequence, and the lithology of the formation medium is set to be sandstone, limestone, dolomite, sand-lime mixed rock and sand-dolomite mixed rock in sequence, wherein the sand-lime mixed rock is formed by mixing sandstone and limestone, the sand-lime mixed rock is formed by mixing sandstone and dolomite, the water mineralization of the formation is set to be 0kppm, 5kppm, 10kppm, 20kppm, 50kppm, 100kppm and 200kppm in sequence, the drill collar gaps are set to be 0cm, 0.5cm, 1cm, 1.5cm and 2cm in sequence, the mud types comprise oil-based mud and water-based mud, and the formation porosity is set to be 0, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45% and 50% in sequence.
6. The intelligent processing method for porosity of pulsed neutrons while drilling based on iron neutron marking as claimed in claim 1, wherein the step 6) comprises:
6.1, constructing a multilayer perception neural network, wherein the multilayer perception neural network is provided with an input layer, a first hidden layer, a second hidden layer and an output layer, the input layer, the first hidden layer, the second hidden layer and the output layer are sequentially connected, 7 nodes are arranged in the input layer and are used for obtaining input parameters of the multilayer perception neural network, the input parameters comprise the size of a drill collar in a pulse neutron porosity while drilling sample, a macroscopic capture section of a borehole medium, a macroscopic capture section of a formation medium, a counting ratio of iron-marked thermal neutrons, a non-elastic gamma count, a capture gamma count and a chlorine element characteristic gamma count, 10 nodes are arranged in the first hidden layer, 15 nodes are arranged in the second hidden layer, the first hidden layer and the second hidden layer are connected through a ReLU activation function, the second hidden layer and the output layer are connected through a tanh activation function, and 1 node is arranged in the output layer and is used for outputting the calculated pulse neutron porosity while drilling;
step 6.2, setting a porosity precision value of the pulse neutrons while drilling;
step 6.3, randomly selecting a pulse neutron porosity while drilling sample in the training set, inputting the pulse neutron porosity while drilling sample into the multilayer perception neural network, enabling nodes in the input layer of the multilayer perception neural network to obtain input parameters in the pulse neutron porosity while drilling sample, activating the input parameters through a ReLU function, and transmitting the activated input parameters to the first hidden layer, wherein the output result of the first hidden layer is shown as a formula (2):
in the formula (I), the compound is shown in the specification,
as a result of the output of the first hidden layer,
is an input parameter of the multi-layer perceptive neural network,
is the size of a drill collar,
the ratio of the counts of the thermal neutrons to the iron marker,
is a non-elastic gamma meterThe number of the first and second groups is counted,
in order to capture the gamma counts,
is the gamma counting of the characteristic of the chlorine element,
for the macroscopic capture cross-section of the wellbore medium,
is a macroscopic trapping cross section of the formation medium,
is the weight of the first hidden layer,
a bias for the first hidden layer;
the output result of the first hidden layer is activated by the ReLU function and then transmitted to the second hidden layer, and the output result of the second hidden layer is shown in formula (3):
in the formula (I), the compound is shown in the specification,
as a result of the output of the second hidden layer,
is the weight of the second hidden layer,
a bias for the second hidden layer;
and the output result of the second hidden layer is activated by the tanh function and then transmitted to the output layer, and the porosity of the pulse-while-drilling neutrons output by the output layer is as follows:
in the formula (I), the compound is shown in the specification,
the porosity of the pulse neutron output by the output layer while drilling,
as a weight of the output layer,
is the bias of the output layer;
step 6.4, calculating the formation porosity of the model by combining the porosity value of the while-drilling pulse neutrons corresponding to the while-drilling pulse neutron porosity sample according to the porosity of the while-drilling pulse neutrons output by the output layer, calculating the porosity error value of the while-drilling pulse neutrons of the multilayer perception neural network, and comparing the porosity error value with the preset precision value of the porosity of the while-drilling pulse neutrons; if the porosity error value of the while-drilling pulse neutrons of the multilayer perception neural network is larger than the set porosity precision value of the while-drilling pulse neutrons, updating the weight and bias of each layer in the multilayer perception neural network by using a logcosh loss function and adopting a back propagation and random gradient descent method, returning to the step 6.3, and continuing to train the multilayer perception neural network; and if the while-drilling pulse neutron porosity error value of the multilayer perception neural network is not less than the set while-drilling pulse neutron porosity precision value, stopping training the multilayer perception neural network, and entering the step 7).
7. The intelligent processing method for porosity of pulsed neutrons while drilling based on iron neutron marking as claimed in claim 6, wherein in step 6.4, the weight and bias updating formula of each layer in the multilayer perceptive neural network is as follows:
in the formula (I), the compound is shown in the specification,
to minimize the value of the variable at which the logcosh loss function takes a minimum,
as logcosh loss function.
8. The method for intelligently processing porosity of pulse-while-drilling neutrons based on iron neutron labeling according to claim 1, wherein in step 7), all porosity of pulse-while-drilling neutrons in the validation set are input into the trained multilayer perceptive neural network, a calculation value of the porosity of the pulse-while-drilling neutrons is obtained by using the multilayer perceptive neural network for each sample of the porosity of the pulse-while-drilling neutrons, and an absolute error value of the porosity of the pulse-while-drilling neutrons is calculated by using the multilayer perceptive neural network by combining the formation porosity of the calculation model of the porosity value of the pulse-while-drilling neutrons corresponding to the sample of the porosity of the pulse-while-drilling neutrons, and if the absolute error value is less than 1%, the calculation result of the porosity of the pulse-while-drilling neutrons in the multilayer perceptive neural network is proved to be accurate;
the calculation formula of the absolute error value of the porosity of the while-drilling pulse neutrons is as follows:
in the formula (I), the compound is shown in the specification,
is the absolute error value of the porosity of the neutron in the while-drilling pulse,
to verify the number of concentrated while-drilling pulsed neutron porosity samples,
in order to verify the serial number of the porosity sample of the concentrated while-drilling pulse neutron,
for multi-layer perceptive neural network according to
A pulse-while-drilling neutron porosity value calculated for each pulse-while-drilling neutron porosity sample,
is a first
And calculating the formation porosity of the model according to the pulse-while-drilling neutron porosity value corresponding to the pulse-while-drilling neutron porosity sample.
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