Reservoir fracture-cavity identification method and device, electronic equipment and storage medium

Abstract

The invention discloses a reservoir fracture-cavity identification method and device, electronic equipment and a storage medium. The method comprises the steps that front detection data are obtained through a drill bit front detection device arranged at the first position of a drilling tool, and lateral detection data are obtained through a sound wave far detection short section arranged at the second position of the drilling tool; sending the front detection data and the lateral detection data to a ground data center in a preset transmission mode, and fusing the front detection data, the lateral detection data, predetermined seismic data and predetermined geological modeling data to obtain multi-source fusion data; and inputting the multi-source fusion data into a pre-constructed reservoir fracture-cavity identification model, and identifying the reservoir fracture-cavity according to an output result of the reservoir fracture-cavity identification model. According to the technical scheme provided by the embodiment of the invention, all-directional real-time detection of stratum fractures and cavities in the lateral direction of a shaft and the drilling direction of a drill bit is realized, and meanwhile, the reservoir fracture and cavity identification precision can be improved, so that the drilling efficiency and safety are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas development, and particularly relates to a reservoir fracture and cave identification method and device, an electronic device and a storage medium. BACKGROUND

[0002] In oil and gas development, fractures and caves of carbonate reservoirs are the main reservoir spaces of carbonate reservoirs, but they have strong heterogeneity and complex distribution, are prone to well leakage accidents, and seriously affect drilling efficiency. In the drilling process, fractures (width greater than 1 mm) or large caves (diameter greater than 0.5 m) are prone to cause malignant leakage (leakage rate greater than 50 m 3 / h), causing accidents such as sticking and blowout, and bringing great challenges to drilling efficiency and safety. The prior art cannot realize dynamic high-precision detection and identification and real-time warning of reservoir fractures and caves: the resolution of seismic exploration technology is insufficient, and it is difficult to identify small-scale fractures, and there are still blind areas in imaging complex cave systems. Logging technology can only identify drilled fractures after the fact, and cannot realize early warning. While the real-time performance of logging while drilling has improved, it still lags behind the drill bit position, and there is a detection blind area in front of the drill bit. SUMMARY

[0003] The present application provides a reservoir fracture and cave identification method and device, an electronic device and a storage medium, which realizes full-range real-time detection of fractures and caves in the formation on the lateral side of the wellbore and the drilling direction of the drill bit, and can improve the identification accuracy of reservoir fractures and caves, and further improve drilling efficiency and safety.

[0004] According to an aspect of the present application, a reservoir fracture and cave identification method is provided, which comprises:

[0005] A front-probing device arranged at a first position of a drilling tool acquires front-probing data, and a sonic far-probing sub arranged at a second position of the drilling tool acquires lateral-probing data; wherein the first position is at a first preset distance from the drill bit in the drilling direction, and the second position is at a second preset distance from the drill bit in the drilling direction;

[0006] The front-probing data and the lateral-probing data are sent to a ground data center by a preset transmission mode, and the front-probing data, the lateral-probing data, pre-determined seismic data and pre-determined geological modeling data are fused to obtain multi-source fusion data;

[0007] The multi-source fusion data is input into a pre-constructed reservoir fracture and cave identification model, and the reservoir fracture and cave are identified according to the output result of the reservoir fracture and cave identification model.

[0008] According to another aspect of the present application, a reservoir fracture and cave identification device is provided, which comprises:

[0009] The probe data acquisition module is configured to acquire the front probe data through a front probe device arranged at a first position of the drilling tool and to acquire the lateral probe data through an acoustic wave far probe sub arranged at a second position of the drilling tool, wherein the first position is located at a first preset distance from the drill bit in the drilling direction, and the second position is located at a second preset distance from the drill bit in the drilling direction.

[0010] The fusion data generation module is configured to send the front probe data and the lateral probe data to a ground data center through a preset transmission mode, and to fuse the front probe data, the lateral probe data, pre-determined seismic data, and pre-determined geological modeling data to obtain multi-source fusion data.

[0011] The reservoir fracture and cave identification module is configured to input the multi-source fusion data into a pre-constructed reservoir fracture and cave identification model, and to identify reservoir fractures and caves according to an output result of the reservoir fracture and cave identification model.

[0012] According to another aspect of the present application, an electronic device is provided, which comprises:

[0013] at least one processor; and

[0014] a memory connected to the at least one processor in communication; wherein

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the reservoir fracture and cave identification method according to any one of the embodiments of the present application.

[0016] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the reservoir fracture and cave identification method according to any one of the embodiments of the present application when executed by the processor.

[0017] The technical scheme of the embodiment of the application obtains the front-probing data through the drill bit front-probing device arranged at the first position of the drilling tool, and obtains the lateral-probing data through the acoustic wave far-probing sub arranged at the second position of the drilling tool; the first position is located at a first preset distance from the drill bit in the drilling direction, and the second position is located at a second preset distance from the drill bit in the drilling direction; the front-probing data and the lateral-probing data are sent to the ground data center through a preset transmission mode, and the front-probing data, the lateral-probing data, the pre-determined seismic data and the pre-determined geological modeling data are fused to obtain multi-source fusion data; the multi-source fusion data is input into the pre-constructed reservoir fracture and cave identification model, and the reservoir fracture and cave are identified according to the output result of the reservoir fracture and cave identification model. The technical scheme of the embodiment of the application realizes the all-around real-time probing of the formation fracture and cave in the wellbore lateral direction and the drilling direction of the drill bit through the probing instruments arranged at different positions of the drilling tool, and the multi-source fusion data obtained by fusing the probing data, the pre-determined seismic data and the pre-determined geological modeling data is input into the fracture and cave identification model for fracture and cave identification, thereby improving the accuracy of reservoir fracture and cave identification, and further improving the drilling efficiency and safety.

[0018] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the application, nor is it used to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0020] Figure 1 is a flow chart of a reservoir fracture and cave identification method according to the first embodiment of the application;

[0021] Figure 2 is an installation position schematic diagram of a drill bit front-probing device and an acoustic wave far-probing sub according to the first embodiment of the application;

[0022] Figure 3 is an installation position schematic diagram of a first probe and a second probe of a drill bit front-probing device according to the first embodiment of the application;

[0023] Figure 4 is a flow chart of a reservoir fracture and cave identification method according to the second embodiment of the application;

[0024] Figure 5is a schematic diagram of an arrangement of a target cable according to Embodiment Two of the present application;

[0025] Figure 6 is a schematic diagram of mud pulse transmission according to Embodiment Two of the present application;

[0026] Figure 7 is a structural schematic diagram of a reservoir fracture-cave identification device according to Embodiment Three of the present application;

[0027] Figure 8 is a structural schematic diagram of an electronic device implementing a reservoir fracture-cave identification method according to Embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely below in combination with the drawings in the embodiment of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] Embodiment One

[0031] Figure 1 A flowchart of a reservoir fracture-cave identification method is provided for Embodiment One of the present application. The present embodiment can be applicable to the identification of fracture and cave development areas in a reservoir. The reservoir fracture-cave identification method can be executed by a reservoir fracture-cave identification device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device. As shown in the figure, the method comprises: Figure 1

[0032] ​S110, obtaining the front-probing data by the drill bit front-probing device arranged at the first position of the drilling tool and obtaining the lateral-probing data by the acoustic wave far-probing sub arranged at the second position of the drilling tool; wherein the first position is located at a first preset distance from the drill bit in the drilling direction, and the second position is located at a second preset distance from the drill bit in the drilling direction.

[0033] The drill bit front-probing device comprises a first probe and a second probe, and the front-probing data comprises front warning data and guiding data. The first probe is a single low-frequency probe, and the direction of the probe is opposite to the drilling direction of the drill bit, and is used for obtaining the front warning data. The second probe is a plurality of medium-high frequency probes designed by using a multi-element acoustic array, and the number of the probes is usually 2-3. The direction of the probe can be adjusted, and the probe is used for obtaining the guiding data. The acoustic wave far-probing sub adopts a conventional acoustic wave far-probing technology, and the direction of the probe is the lateral direction of the wellbore, and is used for obtaining the lateral-probing data.

[0034] The drill bit front-probing device is arranged at the first position of the drilling tool, that is, at the first preset distance from the drill bit in the drilling direction. Generally, the drill bit front-probing device is installed near the drill bit. The acoustic wave far-probing sub is arranged at the second position of the drilling tool, that is, at the second preset distance from the drill bit in the drilling direction. Generally, the acoustic wave far-probing sub is installed in the middle and upper part of the drilling tool. The first preset distance and the second preset distance can be set by the technical personnel according to the actual situation, and the embodiments of the present application do not limit this. Exemplarily, Figure 2 A schematic diagram of the installation positions of the drill bit front-probing device and the acoustic wave far-probing sub is shown in FIG. 2. Figure 2 As shown in FIG. 2, 1 is the acoustic wave far-probing sub, 2 is the drill bit front-probing device, 3 is the probing signal emitted by the acoustic wave far-probing sub, 4 is the low-frequency probing signal emitted by the first probe in the drill bit front-probing device, and 5 is the medium-high frequency probing signal emitted by the second probe in the drill bit front-probing device. Exemplarily, Figure 3 A schematic diagram of the installation positions of the first probe and the second probe of the drill bit front-probing device is shown in FIG. 3. Figure 3 As shown in FIG. 3, 6 is a drill bit tooth, 7 is the second probe, and 8 is the first probe.

[0035] In the embodiment of the present application, the front-probing device arranged at the first position of the drilling tool can be used to obtain the front-probing data, and the acoustic far-probing sub arranged at the second position of the drilling tool can be used to obtain the lateral-probing data. Specifically, the front-probing device installed near the drill bit can be used to emit a probing signal to probe a preset range of the drilling direction of the drill bit, and obtain the front-probing data including the front-warning data and the guiding data. The acoustic far-probing sub installed in the upper part of the drilling tool can be used to emit an acoustic signal to scan the stratum within a range of 10-30 m laterally of the wellbore, and receive echo signals from the stratum to generate the lateral-probing data. It should be noted that before the drilling operation and the acquisition of the probing data, the drilling tool and the probing device installed on the drilling tool need to be debugged to ensure that they can work normally.

[0036] Optionally, the front-probing device arranged at the first position is used to obtain the front-probing data, including: the first probe is used to emit a first acoustic signal to a first preset range of the drilling direction of the drill bit, and receive echo signals corresponding to the first acoustic signal to generate the front-warning data; the second probe is started to emit a second acoustic signal, and the second probe is used to scan a second preset range of the drilling direction of the drill bit by adjusting the direction of the second probe to generate the guiding data; and the second preset range is smaller than the first preset range.

[0037] The first acoustic signal is a low-frequency acoustic signal, and the second acoustic signal is a medium-high frequency acoustic signal. The first preset range and the second preset range can be set by a technician according to the actual situation, and the embodiment of the present application does not limit this. It should be noted that the second preset range is smaller than the first preset range.

[0038] In the embodiment of the present application, the first probe can be used to emit a first acoustic signal to a first preset range (such as 1-10 m) of the drilling direction of the drill bit. After the first acoustic signal penetrates the stratum, it will produce an echo signal when encountering a fracture-vug interface. The first probe receives the echo signal to generate the front-warning data to provide early warning for the drilling of the drill bit. Meanwhile, the second probe can be started to emit a second acoustic signal, and the second probe can be used to scan a second preset range (such as a range of 1 m) of the drilling direction of the drill bit by adjusting the direction of the second probe to generate the guiding data. The second probe adopts a multi-element acoustic array design, which can adjust the acoustic probing direction by controlling each small probe unit to achieve high-precision imaging, so as to accurately identify the fracture-vug distribution of the stratum within the second preset range of the drilling direction of the drill bit, and provide accurate guiding data for the subsequent drilling of the drill bit.

[0039] In S120, the front-probing data and the lateral-probing data are sent to the ground data center through a preset transmission mode, and the front-probing data, the lateral-probing data, the pre-determined seismic data, and the pre-determined geological modeling data are fused to obtain multi-source fusion data.

[0040] The preset transmission mode includes cable transmission and mud pulse transmission. The cable transmission is to transmit the front detection data and the lateral detection data through a cable laid between the drilling equipment and the ground data center. The mud pulse transmission is to use drilling fluid (mud) as a transmission medium, to encode the front detection data and the lateral detection data into a pressure pulse signal by generating a pressure pulse in the downhole control equipment, and then to transmit the pressure pulse signal to the ground along the drilling fluid column.

[0041] The seismic data is obtained by a seismic exploration method, and records the propagation of seismic waves in the underground medium, and can reflect the distribution and properties of geological layers at different depths, thereby providing an important basis for fine description of the geological structure. The geological modeling data is a three-dimensional geological model established based on existing geological data, geophysical data and the like, and integrates multiple information to preliminarily simulate and predict the shape and spatial position of the underground geological body.

[0042] In the embodiment of the present application, after the front detection data and the lateral detection data are obtained, the front detection data and the lateral detection data can be sent to the ground data center by cable or mud pulse, and the front detection data, the lateral detection data, the pre-determined seismic data and the pre-determined geological modeling data are fused in the ground data center to obtain multi-source fusion data, so as to comprehensively utilize the advantages of various data and make up for the limitations of single data, thereby providing more reliable data support for subsequent fracture-cave identification.

[0043] S130, inputting the multi-source fusion data into a pre-constructed reservoir fracture-cave identification model, and identifying the reservoir fracture-cave according to an output result of the reservoir fracture-cave identification model.

[0044] The reservoir fracture-cave identification model is composed of a convolutional neural network and a long short-term memory network (LSTM), and includes an input layer, a convolutional layer, an LSTM layer and an output layer.

[0045] In the embodiment of the present application, the multi-source fusion data can be input into the pre-constructed reservoir fracture-cave identification model, and the reservoir fracture-cave is identified according to the output result of the reservoir fracture-cave identification model. Specifically, first, the multi-source fusion data F(x) can be input into the reservoir fracture-cave identification model, and the input layer of the reservoir fracture-cave identification model receives the multi-source fusion data. Second, the spatial features of the multi-source fusion data can be extracted by the convolutional layer of the reservoir fracture-cave identification model, and the formula is as follows:

[0046] C(x) = σ(W c *F(x) + b c );

[0047] Then, the time sequence features of the multi-source fusion data can be extracted by the LSTM layer of the reservoir fracture-cave identification model, and the formula is as follows:

[0048] h t =LSTM(C(x), h t-1 );

[0049] After that, the output layer of the reservoir fracture-cave recognition model is used to generate and output the fracture-cave recognition results (such as fracture width, cave diameter, etc.), and the formula is as follows:

[0050] y = Softmax(W0 * h t + b0);

[0051] Wherein, C(x) is the spatial feature of multi-source fusion data, σ is the activation function, W c is the convolution kernel weight, b c is the bias term, h t is the hidden state of the current time step, h t-1 is the hidden state of the previous time step, LSTM represents the LSTM layer, Softmax represents the Softmax function, y is the output result, W0 is the output layer weight, and b0 is the bias term.

[0052] Finally, the reservoir fracture-cave can be recognized according to the output result of the reservoir fracture-cave recognition model, the accuracy of reservoir fracture-cave recognition is improved, and the drilling efficiency and safety are improved.

[0053] Optionally, the construction process of the reservoir fracture-cave recognition model comprises: obtaining historical multi-source fusion data inputting a deep learning model composed of a convolutional neural network and a long short-term memory network, obtaining an output result of the deep learning model; calculating the cross-entropy between the output result and the true result, and optimizing the parameters of the deep learning model by gradient descent method according to the cross-entropy until a preset iteration number is reached, obtaining the reservoir fracture-cave recognition model.

[0054] Wherein, the cross-entropy is an index for measuring the difference between two probability distributions, which is often used as a loss function in machine learning and deep learning to evaluate the gap between the probability distribution predicted by the model and the true probability distribution. Gradient descent method is an iterative optimization algorithm for finding the minimum (or maximum) value of a function, which is widely used in model parameter optimization in machine learning and deep learning. The core idea is to adjust the parameters constantly, move along the opposite direction of the gradient of the target function, and gradually approach the minimum value of the function.

[0055] In the embodiment of the present application, when constructing the reservoir fracture and pore identification model, the deep learning model composed of the convolutional neural network and the long short-term memory network can be repeatedly trained for the same block reservoir, and the cross-entropy loss function is used for optimization. Specifically, the historical multi-source fusion data can be input into the deep learning model to obtain the output result of the deep learning model, the cross-entropy between the output result and the true result is calculated, and then the parameters of the deep learning model are optimized by the gradient descent method according to the cross-entropy until a preset iteration number is reached, and the reservoir fracture and pore identification model is obtained. Alternatively, the calculation formula of the cross-entropy is as follows:

[0056]

[0057] Wherein, L is the cross-entropy, y i is the true result, is the output result.

[0058] The technical scheme of the embodiment of the present application obtains the front detection data through the drill bit front detection device arranged at the first position of the drilling tool, and obtains the lateral detection data through the acoustic wave far detection sub obtained at the second position of the drilling tool; wherein the first position is located at a first preset distance from the drill bit in the drilling direction, and the second position is located at a second preset distance from the drill bit in the drilling direction; the front detection data and the lateral detection data are sent to the ground data center through a preset transmission mode, and the front detection data, the lateral detection data, the pre-determined seismic data and the pre-determined geological modeling data are fused to obtain multi-source fusion data; the multi-source fusion data is input into the pre-constructed reservoir fracture and pore identification model, and the reservoir fracture and pore are identified according to the output result of the reservoir fracture and pore identification model. The technical scheme of the embodiment of the present application realizes the full-range real-time detection of the formation fracture and pore in the wellbore lateral and the drilling direction of the drill bit through the detection instruments arranged at different positions of the drilling tool, and improves the accuracy of the reservoir fracture and pore identification, and further improves the drilling efficiency and safety by inputting the multi-source fusion data obtained by fusing the detection data, the pre-determined seismic data and the pre-determined address modeling data into the fracture and pore identification model for fracture and pore identification.

[0059] Embodiment two

[0060] Figure 4 The flowchart of a reservoir fracture and pore identification method provided in the second embodiment of the present application is based on the optimization of the above-mentioned embodiments, and the schemes not described in detail in the present embodiment are described in the above-mentioned embodiments. As shown in Figure 4 , the method comprises:

[0061] S210, front detection data is obtained through a drill bit front detection device arranged at a first position of a drilling tool, and lateral detection data is obtained through an acoustic wave far detection sub arranged at a second position of the drilling tool.

[0062] S220, sending the front detection data and the lateral detection data to a ground data center through a preset transmission mode.

[0063] Optionally, when the preset transmission mode is cable transmission, the sending the front detection data and the lateral detection data to the ground data center through the preset transmission mode comprises: arranging a target cable inside a drill rod to connect with downhole instruments, so as to connect the downhole instruments with a ground power supply and the ground data center through the target cable; wherein the downhole instruments comprise the drill bit front detection device and the acoustic wave far detection sub; and the front detection data and the lateral detection data are sent to the ground data center in real time through the target cable.

[0064] In the embodiments of the present application, when the preset transmission mode is cable transmission, a target cable needs to be arranged inside a drill rod to connect with downhole instruments, so as to connect the downhole instruments with a ground power supply and the ground data center through the target cable, to realize power supply of the downhole instruments and signal transmission between the downhole instruments and the ground data center, and to facilitate real-time sending of the front detection data and the lateral detection data to the ground data center through the target cable after the front detection data and the lateral detection data are acquired. Exemplarily, Figure 5 a schematic diagram of arrangement of a target cable is shown, as Figure 5 shown in the figure, 9 is a target cable plug, 10 is a target cable socket inside a drill tool, and 11 is a target cable.

[0065] Optionally, when the preset transmission mode is mud pulse transmission, the sending the front detection data and the lateral detection data to the ground data center through the preset transmission mode comprises: storing the front detection data and the lateral detection data in a pre-set storage unit; after integrated processing of the front detection data and the lateral detection data in the storage unit, extracting key directional information, and sending the key directional information to the ground data center through mud pulse.

[0066] In the embodiments of the present application, when the preset transmission mode is mud pulse transmission, a downhole battery compartment is arranged inside a drill tool to supply power to downhole instruments, and a storage unit is arranged to store acquired front detection data and lateral detection data, and integrated processing is performed on the front detection data and the lateral detection data in the storage unit, key directional information is extracted, and the key directional information is sent to the ground data center through mud pulse. Exemplarily, Figure 6 a schematic diagram of mud pulse transmission is shown, as Figure 6 shown in the figure, 12 is a mud pulse signal.

[0067] It can be understood that the embodiments of the present application provide two signal transmission schemes, which can adapt to different drilling environments and needs, and enhance the flexibility and applicability of the present scheme.

[0068] S230, pre-processing the front detection data and the lateral detection data; wherein the pre-processing includes at least one of denoising, normalization, and time alignment.

[0069] In the embodiments of the present application, before the front detection data, the lateral detection data, the pre-determined seismic data, and the pre-determined geological modeling data are fused, the obtained front detection data and lateral detection data need to be pre-processed, including denoising, normalization, time alignment, etc., to ensure data quality.

[0070] S240, fusing the pre-processed front detection data, the pre-processed lateral detection data, the seismic data, and the geological modeling data by a weighted fusion algorithm to obtain multi-source fusion data.

[0071] In the embodiments of the present application, the pre-processed front detection data, the pre-processed lateral detection data, the seismic data, and the geological modeling data can be fused by a weighted fusion algorithm to obtain multi-source fusion data. Specifically, the weighted fusion algorithm can be used to dynamically adjust the weight coefficients of each data source, and fuse each data source according to the weight coefficients of each data source to obtain multi-source fusion data. Alternatively, the pre-processed front detection data, the pre-processed lateral detection data, the seismic data, and the geological modeling data are fused by a weighted fusion algorithm, and the formula is as follows:

[0072]

[0073] wherein F(x) is the multi-source fusion data, D i (x) is the i-th data source, n is the number of data sources, w i is the weight coefficient of the i-th data source.

[0074] The pre-processed front detection data, the pre-processed lateral detection data, the seismic data, and the geological modeling data are fused by a weighted fusion algorithm to generate multi-source fusion data that comprehensively reflects the distribution of stratum fractures and holes, providing high-quality input for subsequent deep learning model processing.

[0075] S250, inputting the multi-source fusion data into a pre-constructed reservoir fracture and hole identification model, and identifying the reservoir fractures and holes according to the output result of the reservoir fracture and hole identification model.

[0076] S260, generating early warning information according to the identification result of the reservoir fracture and cavity, so as to prompt the technical personnel to generate a drilling trajectory adjustment strategy according to the identification result and the early warning information, and to perform drilling work according to the drilling trajectory adjustment strategy through the drilling control system.

[0077] In the embodiment of the present application, after identifying the reservoir fracture and cavity according to the output result of the reservoir fracture and cavity identification model, early warning information can be generated according to the identification result of the reservoir fracture and cavity, prompting the drilling direction of the drill bit and the existence of high-risk fractures and cavities on the side, etc., so as to facilitate the technical personnel to generate a drilling trajectory adjustment strategy according to the identification result and the early warning information, such as shifting the drill bit 15 degrees to the left to avoid the high-risk area, and transmitting the drilling trajectory adjustment strategy to the drilling control system to adjust the drilling direction of the drill bit in real time. Optionally, during the drilling process, data from the acoustic far-probing sub and the drill bit front-probing device can be continuously received, and the multi-source data fusion model and the deep learning model can be updated in real time to further improve the identification accuracy and early warning capability.

[0078] The technical scheme of the embodiment of the present application obtains front-probing data through the drill bit front-probing device arranged at the first position of the drilling tool, and obtains lateral-probing data through the acoustic far-probing sub arranged at the second position of the drilling tool; the front-probing data and the lateral-probing data are sent to the ground data center through a preset transmission mode; the front-probing data and the lateral-probing data are preprocessed; the preprocessing includes at least one of denoising, normalization, and time alignment; the preprocessed front-probing data, the preprocessed lateral-probing data, seismic data, and geological modeling data are fused through a weighted fusion algorithm to obtain multi-source fusion data; the multi-source fusion data is input into a pre-constructed reservoir fracture and cavity identification model, and the reservoir fracture and cavity are identified according to the output result of the reservoir fracture and cavity identification model; early warning information is generated according to the identification result of the reservoir fracture and cavity, so as to prompt the technical personnel to generate a drilling trajectory adjustment strategy according to the identification result and the early warning information, and to perform drilling work according to the drilling trajectory adjustment strategy through the drilling control system. The technical scheme of the embodiment of the present application realizes omnidirectional real-time probing of the formation fracture and cavity on the side of the wellbore and the drilling direction of the drill bit through the probing instruments arranged at different positions of the drilling tool, and improves the accuracy of reservoir fracture and cavity identification by inputting the multi-source fusion data obtained by fusing the probing data, pre-determined seismic data, and pre-determined geological modeling data into the fracture and cavity identification model. At the same time, early warning information can be generated according to the identification result to prompt the technical personnel to generate a drilling trajectory adjustment strategy to adjust the drilling trajectory, ensuring the efficiency and safety of drilling operations.

[0079] Embodiment three

[0080] Figure 7 A structural schematic diagram of a reservoir fracture and cavity identification device provided in the third embodiment of the present application.

[0081] AsFigure 7 The device comprises:

[0082] The probe data acquisition module 310 is configured to acquire front probe data through a drill bit front probe device arranged at a first position of a drilling tool and to acquire lateral probe data through a sonic far-probe sub arranged at a second position of the drilling tool; the first position is located at a first preset distance from the drill bit in a drilling direction, and the second position is located at a second preset distance from the drill bit in the drilling direction;

[0083] The fusion data generation module 320 is configured to send the front probe data and the lateral probe data to a ground data center through a preset transmission mode and to fuse the front probe data, the lateral probe data, pre-determined seismic data, and pre-determined geological modeling data to obtain multi-source fusion data;

[0084] The reservoir fracture and cave identification module 330 is configured to input the multi-source fusion data into a pre-constructed reservoir fracture and cave identification model and to identify reservoir fractures and caves according to an output result of the reservoir fracture and cave identification model.

[0085] Optionally, the drill bit front probe device comprises a first probe and a second probe, the second probe is designed in a multi-element acoustic array, and the front probe data comprises front warning data and guiding data.

[0086] The probe data acquisition module 310 comprises:

[0087] The warning data acquisition unit is configured to emit a first acoustic signal to a first preset range in the drilling direction of the drill bit through the first probe, to receive a corresponding echo signal of the first acoustic signal, and to generate front warning data.

[0088] The guiding data acquisition unit is configured to start the second probe to emit a second acoustic signal and to generate guiding data by adjusting the direction of the second probe to scan a second preset range in the drilling direction of the drill bit; the second preset range is smaller than the first preset range.

[0089] Optionally, the preset transmission mode comprises cable transmission.

[0090] The fusion data generation module 320 comprises:

[0091] The target cable arrangement unit is configured to arrange a target cable inside a drill pipe to connect with downhole instruments, so as to connect the downhole instruments with a ground power supply and a ground data center through the target cable; the downhole instruments comprise the drill bit front probe device and the sonic far-probe sub.

[0092] The first data transmission unit is configured to transmit the front detection data and the lateral detection data to the ground data center in real time through the target cable.

[0093] Optionally, the preset transmission mode includes mud pulse transmission.

[0094] The fusion data generation module 320 includes:

[0095] The detection data storage unit is configured to store the front detection data and the lateral detection data in a preset storage unit.

[0096] The second data transmission unit is configured to extract key steering information after integrated processing of the front detection data and the lateral detection data in the storage unit, and transmit the key steering information to the ground data center through mud pulse.

[0097] Optionally, the fusion data generation module 320 includes:

[0098] The detection data preprocessing unit is configured to preprocess the front detection data and the lateral detection data, and the preprocessing includes at least one of denoising, normalization, and time alignment.

[0099] The detection data fusion unit is configured to fuse the preprocessed front detection data, the preprocessed lateral detection data, the seismic data, and the geological modeling data through a weighted fusion algorithm to obtain multi-source fusion data.

[0100] Optionally, the reservoir fracture and cave identification module 330 includes:

[0101] The output result determination unit is configured to obtain historical multi-source fusion data input a deep learning model composed of a convolutional neural network and a long short-term memory network to obtain an output result of the deep learning model.

[0102] The identification model generation unit is configured to calculate cross-entropy between the output result and a true result, optimize parameters of the deep learning model through a gradient descent method according to the cross-entropy until a preset iteration number is reached, and obtain the reservoir fracture and cave identification model.

[0103] Optionally, the device further includes:

[0104] The drilling trajectory adjustment module is configured to generate early warning information according to the identification result of the reservoir fracture and cave, to prompt a technical personnel to generate a drilling trajectory adjustment strategy according to the identification result and the early warning information, and to perform drilling work according to the drilling trajectory adjustment strategy through a drilling control system.

[0105] The reservoir fracture-cave identification device provided by the embodiments of the present application can execute the reservoir fracture-cave identification method provided by any of the embodiments of the present application, has the function modules and beneficial effects corresponding to the execution method.

[0106] Embodiment Four

[0107] Figure 8 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the applications described and / or claimed in this document.

[0108] As shown in Figure 8 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0109] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, a loudspeaker, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0110] The processor 11 can be various general and / or special purpose processing components having processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the reservoir fracture-cave identification method.

[0111] In some embodiments, the reservoir fracture-cave identification method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the reservoir fracture-cave identification method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the reservoir fracture-cave identification method by any other suitable means, such as by means of firmware.

[0112] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0113] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor of the machine, causes the machine to implement the functions / acts specified in the flowchart and / or block diagram block or blocks. The computer program can also, or instead, be executed across

[0114] In the context of this application, a computer readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium can be a machine readable signal medium. More specific examples of the machine readable storage medium will include a one or more lines of a electrical connection, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0115] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0116] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), blockchain network, and the Internet.

[0117] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0118] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in this application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, and this application does not limit herein.

[0119] The above detailed description does not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A reservoir fracture and cavity identification method, characterized in that: The method comprises: Acquiring forward detection data by a drill bit forward detection device disposed at a first position of the drilling tool, and acquiring lateral detection data by an acoustic wave remote detection sub disposed at a second position of the drilling tool; wherein the first position is located at a first preset distance from the drill bit in the drilling direction, and the second position is located at a second preset distance from the drill bit in the drilling direction; The front detection data and the lateral detection data are transmitted to a ground data center via a preset transmission method, and the front detection data, the lateral detection data, the predetermined seismic data, and the predetermined geological modeling data are fused to obtain multi-source fused data; The multi-source fusion data is input into a pre-built reservoir fracture-vug identification model, and reservoir fractures and cavities are identified according to the output results of the reservoir fracture-vug identification model.

2. The method according to claim 1, characterized in that The drill bit forward detection device includes a first probe and a second probe, the second probe adopts a multi-element acoustic array design; the forward detection data includes forward warning data and guidance data; The method of obtaining the front detection data by the drill head front detection device provided at the first position includes: Transmitting a first acoustic wave signal to a first preset range in the drilling direction of the drill bit through the first probe, and receiving an echo signal corresponding to the first acoustic wave signal to generate forward warning data; The second probe is started to emit a second acoustic wave signal, and a second preset range of the drilling direction of the drill bit is scanned by adjusting the direction of the second probe to generate guidance data; wherein, the second preset range is smaller than the first preset range.

3. The method according to claim 1, characterized in that The preset transmission mode includes cable transmission; The sending of the front detection data and the lateral detection data to a ground data center through a preset transmission method includes: Arranging a target cable inside the drill pipe to connect the target cable to the downhole instrument, so that the downhole instrument is connected to the ground power supply and the ground data center respectively through the target cable; wherein the downhole instrument includes the drill bit advance device and the acoustic wave remote sounding sub; The front detection data and the lateral detection data are sent to the ground data center in real time via the target cable.

4. The method according to claim 1, wherein The preset transmission mode includes mud pulse transmission; The sending of the front detection data and the lateral detection data to a ground data center through a preset transmission method includes: storing the front detection data and the lateral detection data in a pre-set storage unit; After integrating the front detection data and the lateral detection data in the storage unit, key guidance information is extracted and sent to the ground data center through mud pulses.

5. The method according to claim 1, wherein The fusing of the front detection data, the lateral detection data, and the predetermined seismic data and geological modeling data to obtain multi-source fused data includes: Preprocessing the front detection data and the lateral detection data; wherein the preprocessing includes at least one of denoising, normalization, and time alignment; The pre-processed front detection data, the pre-processed lateral detection data, the seismic data and the geological modeling data are fused by a weighted fusion algorithm to obtain multi-source fused data.

6. The method according to claim 1, characterized in that The process of constructing the reservoir fracture-cavity identification model includes: Obtain historical multi-source fusion data as input into a deep learning model composed of a convolutional neural network and a long short-term memory network, and obtain an output result of the deep learning model; The cross entropy between the output result and the true result is calculated, and the parameters of the deep learning model are optimized by the gradient descent method according to the cross entropy until a preset number of iterations is reached to obtain the reservoir fracture and vug identification model.

7. The method according to claim 1, characterized in that After identifying reservoir fractures and holes according to the output result of the reservoir fracture and hole identification model, the method further includes: According to the recognition result of the reservoir fracture and cavity, early warning information is generated to prompt the technicians to generate a drilling trajectory adjustment strategy according to the recognition result and the early warning information, and perform drilling work according to the drilling trajectory adjustment strategy through the drilling control system.

8. A reservoir fracture and hole identification device, characterized in that: The device comprises: a detection data acquisition module, configured to acquire front detection data via a drill bit front detection device disposed at a first position of the drilling tool, and acquire lateral detection data via an acoustic wave telescopic sub disposed at a second position of the drilling tool; wherein the first position is located at a first preset distance from the drill bit in the drilling direction, and the second position is located at a second preset distance from the drill bit in the drilling direction; a fusion data generation module, configured to transmit the front detection data and the lateral detection data to a ground data center via a preset transmission method, and fuse the front detection data, the lateral detection data, predetermined seismic data, and predetermined geological modeling data to obtain multi-source fusion data; The reservoir fracture-vug identification module is used to input the multi-source fusion data into a pre-built reservoir fracture-vug identification model and identify reservoir fractures and cavities according to the output results of the reservoir fracture-vug identification model.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the reservoir fracture and vug identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the reservoir fracture and vug identification method according to any one of claims 1 to 7 when executed.

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