CN116151480B - Shale oil well yield prediction method and device - Google Patents

Shale oil well yield prediction method and device Download PDF

Info

Publication number
CN116151480B
CN116151480B CN202310350371.1A CN202310350371A CN116151480B CN 116151480 B CN116151480 B CN 116151480B CN 202310350371 A CN202310350371 A CN 202310350371A CN 116151480 B CN116151480 B CN 116151480B
Authority
CN
China
Prior art keywords
static
dynamic
data
parameter data
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202310350371.1A
Other languages
Chinese (zh)
Other versions
CN116151480A (en
Inventor
李江昀
赵丹
李擎
张天翔
张毅思
苗磊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Science and Technology Beijing USTB
Original Assignee
University of Science and Technology Beijing USTB
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Science and Technology Beijing USTB filed Critical University of Science and Technology Beijing USTB
Priority to CN202310350371.1A priority Critical patent/CN116151480B/en
Publication of CN116151480A publication Critical patent/CN116151480A/en
Application granted granted Critical
Publication of CN116151480B publication Critical patent/CN116151480B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • Human Resources & Organizations (AREA)
  • General Physics & Mathematics (AREA)
  • Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Marketing (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • General Health & Medical Sciences (AREA)
  • General Business, Economics & Management (AREA)
  • Animal Husbandry (AREA)
  • Biomedical Technology (AREA)
  • Operations Research (AREA)
  • Agronomy & Crop Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Mining & Mineral Resources (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Primary Health Care (AREA)
  • Artificial Intelligence (AREA)
  • Quality & Reliability (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention provides a shale oil well yield prediction method and device, comprising the following steps: collecting historical production data of shale oil wells, and preprocessing the data, wherein the data comprises static parameter data, dynamic production arrangement parameter data and daily oil production data; inputting static parameter data into a static embedding initial bias module, extracting static embedding information, and taking the static embedding information as initial state bias from a sequence to a sequence model; inputting the static parameter data, the dynamic production scheduling parameter data and the daily oil production data into a dynamic-static complementary cross fusion module to obtain complementary fusion information of the dynamic daily oil production scheduling parameter data and the static parameter data, and inputting the complementary fusion information as a sequence to a sequence model; and (3) performing iterative prediction of any step length within a certain range of shale oil well yield by using a sequence-to-sequence model. The invention can adapt to complex geological engineering conditions, improves the yield prediction accuracy, and realizes the iterative prediction of any step length within a certain range of shale oil well yield.

Description

Shale oil well yield prediction method and device
Technical Field
The invention relates to the technical field of shale oil well yield prediction, in particular to a shale oil well yield prediction method and device.
Background
Shale oil is used as an unconventional clean energy source with huge reserves and wide application prospect, and the development and the utilization of the shale oil can effectively reduce the use amount of conventional fossil fuels such as coal, petroleum and the like. However, blindly performing the development and production of shale oil wells is not preferable; for the high benefit of shale oil development, the prediction of the production of shale oil wells is an important point in the oil and gas field.
The existing shale oil well yield prediction method comprises a prediction method based on mechanism modeling and a prediction method based on data driving. The prediction method based on the mechanism modeling mainly starts from an empirical formula and a mechanical principle, and is based on simplified models provided by different geological conditions. Such methods are generally based on the assumption of idealization, and have unsatisfactory prediction effect and weak generalization capability under complex geological and engineering conditions. The traditional data-driven yield prediction method for shale oil wells is generally established on the basis of autocorrelation of yield data in a time dimension, and a yield decreasing rule is developed and analyzed, so that the model can accurately describe the yield decreasing rule, but the model is complex in structure and difficult to solve. And the machine learning method is a novel data-driven prediction method. While some conventional machine learning methods may be used to analytically predict shale well production, these models tend to ignore the autocorrelation of the data in historical observations and do not extract timing dependent features well. The output of each time step in the long-short-period memory network can influence the output of the next time step, so that the time sequence dependent characteristics can be well extracted and the long-term dependent characteristics can be learned. In recent years, long and short term memory networks (Long Short Term Memory networks, LSTM) have been increasingly applied to production predictions for oil wells. However, the existing prediction model based on the long-short-period memory network rarely comprehensively considers the influence of factors such as geology, construction, production arrangement and the like on the yield and the complex cross-correlation relation thereof. Meanwhile, predicting a future production curve by using dynamic and static parameters such as geology, construction and the like and a known production curve is a dynamic iterative process, and the known production curve and the production curve to be predicted are not in one-to-one correspondence, namely, after an input is given, the model can meet the prediction of daily output of any step. Few models now consider this problem.
Disclosure of Invention
The invention provides a shale oil well yield prediction method and device, which are used for predicting the shale oil well yield. The technical scheme is as follows:
in one aspect, a shale oil well yield prediction method is provided, including:
collecting historical production data of shale oil wells, and preprocessing the data, wherein the data comprises static parameter data, dynamic production scheduling parameter data and daily oil production data;
inputting the static parameter data into a static embedding initial bias module, extracting static embedding information, and taking the static embedding information as initial state bias from a sequence to a sequence model;
inputting the static parameter data, the dynamic production scheduling parameter data and the daily oil production data into a dynamic-static complementary cross fusion module to obtain complementary fusion information of the dynamic daily oil production scheduling parameter data and the static parameter data, and taking the complementary fusion information as input of the sequence to a sequence model;
iterative predictions of shale well production are made using the sequence-to-sequence model.
Optionally, the main body of the static embedded initial bias module is a multi-layer perceptron MLP, and adopts a tanh activation function, and the input of the static embedded initial bias module is the static parameter data and the mining days The static parameter data includes: geological and fluid physical parameter data->Construction parameter data->The output is expressed as +.>Subsequently->Hidden state of the sequential feature extractor as the sequence-to-sequence model, +.>The cell state as the time sequence feature extractor is fed into the model as an initial state bias, and a specific formula is shown in (1):
(1)。
optionally, the dynamic and static complementary cross fusion module includes: two major parts, namely a cross attention module and a gate control fusion module;
the cross attention module is divided into a static passage and a dynamic passage, wherein the static passage firstly determines priori influence information of the dynamic production arrangement parameter data on daily oil production through the dynamic yield interaction moduleDetermining superposition influence information of the static parameter data on daily oil production by means of static channel attention>As an output of the static path; the dynamic path firstly determines priori influence information of the static parameter data on daily oil production through a static yield interaction module>Determining superposition influence information of the dynamic production schedule parameter data on daily oil production through dynamic channel attention>As an output of the dynamic path;
The said、/>、/>、/>And daily oil production data are input into the gating fusion module to obtain complementary fusion information +.>Said->Together with daily oil production data as input to a timing feature extractor of the sequence-to-sequence model.
Optionally, the specific operation of the static path includes:
introducing a priori influence by the dynamic yield interaction module as shown in a formula (2), wherein the formula (2) is as follows:
(2)
daily oil production dataBy means of a linear layer->Mapping to query vector +.>Dynamic production of scheduling parameter data->By two different linear layers +.>And->Mapped as key vectors +.>Sum vector->Then->And->Is subjected to a dot multiplication operation and divided by the channel dimension +.>Then generating importance weights in the time sequence dimension by using softmax function, and using the generated importance weight pairs +.>Re-weighting to obtain the data of the introduced dynamic production arrangement parameters->Daily oil production->Priori influence information after a priori influence +.>；
The saidAnd the main body of the static path is input with static parameter data +.>Into the static channel attention, determining superposition influence information of static parameter data on daily oil production +.>As shown in formula (3):
(3)
First through a linear layerWill->Mapping as +.>Will->By two linear layersAnd->Respectively mapped as +.>And->Then->Transpose and +.>Performing a dot multiplication operation and dividing by the channel dimension +.>Then generating importance weights in channel dimensions using a softmax function, using the generated importance weight pairs +.>Re-weighting to obtain the output +.>；
The specific operation of the dynamic path comprises the following steps:
introducing a priori influence by the static yield interaction module as shown in formula (4), wherein formula (4) is as follows:
(4)
will beBy means of a linear layer->Mapping to->Static parameter data->By two linear layers->And->Respectively mapped as +.>And->Then->And->Is subjected to a dot multiplication operation and divided by the channel dimensionThen generating importance weights in the time sequence dimension by using softmax function, and using the generated importance weight pairs +.>Re-weighting to obtain the introduced static parameter data +.>Daily oil production->Is->；
Will beAnd the main body of the dynamic path inputs dynamic production schedule parameter data +.>Into the dynamic channel attention, determining the superposition influence information of dynamic production schedule parameter data on daily oil production +. >As shown in formula (5):
(5)
first through a linear layerWill->Mapping as +.>Will->By two linear layersAnd->Respectively mapped as +.>And->Then->Transpose and +.>Performing a dot multiplication operation and dividing by the channel dimension +.>Then generating importance weights in channel dimensions by using softmax function, re-weighting by using the generated importance weights to obtain output of static channel->。
Optionally, said bringing said、/>、/>、/>And daily oil production data are input into the gating fusion module to obtain complementary fusion information +.>Specifically, as shown in formula (6):
(6)
prior influence information of static pathAnd a priori influence information of dynamic pathways +.>Daily oil production data +.>Stacked together, feed lineSex layer fusion for reducing blood dimension and using +.>Activating function to generate gating information->；
Utilizing the gating informationControlling the information retention degree after the superposition of the static information path, the operations comprise:
output of static pathThrough a linear layer->And gating information->By doing Hadamard product, the gating information element tends to be 1 with corresponding value +.>The information of (2) is preserved and the information corresponding to the value tending to 0 is discarded;
Output of dynamic pathThrough a linear layer->And->By doing Hadamard product, the gating information element tends to be 1 with corresponding value +.>The information of (2) is preserved and the information corresponding to the value tending to 0 is discarded;
to pass through gate control screeningStatic path information thereafterDynamic path informationPerforming element addition operation to obtain complementary fusion information after dynamic and static complementary cross fusion>。
Optionally, the sequence-to-sequence model main body architecture is composed of a time sequence feature extractor and an iteration predictor, both of which adopt a long-short-period memory network LSTM, the time sequence feature extractor uses the static embedded information extracted by the static embedded initial bias module as an initial state bias, uses the complementary fusion information output by the dynamic-static complementary cross fusion module and daily oil production data as input, and extracts the autocorrelation of the dynamic daily oil production data according to the characteristic that the output of the current time state of the LSTM is affected by the previous time state, as shown in formula (7):
(7)
time input segment of the time sequence feature extractorOutput +.>And historical daily oil production data- >Stacked together as input to the timing feature extractor, the output of the static embedded initial bias module>And->As the initial state bias of the time sequence feature extractor, the time sequence feature extractor is utilized to extract the autocorrelation of the dynamic daily output data, and finally the output state of the time sequence feature extractor is obtained>And->；
The saidAnd->Feeding into said iterative predictor, said iterative predictor being based on said +.>Anddynamic iterative prediction of oil production is performed in combination with dynamic production schedule parameter data, as shown in equation (8):
(8)
the iterative predictor inputs dynamic production schedule parameter data corresponding to the current time step at each time stepAnd the daily oil production prediction value of the iteration predictor of the previous time step +.>And input the output hidden state of the previous time step +.>And cell status->When being the first starting state of the iterative predictor, the +.>For the output state of the timing feature extractor +.>Then based on this, the daily oil production of the current time step is predicted +.>；
The process described by equation (8) is repeated until the desired prediction step size is reached.
Optionally, the method further includes dividing data into training data and verification data, training the static embedded initial bias module, the dynamic-static complementary cross fusion module and the sequence-to-sequence model by using the training data, including:
Dividing the input time segment and the predicted time segment corresponding to each sample, combining the normalized corresponding characteristics and labels, and sending the normalized corresponding characteristics and labels into each module according to the parameters required by each module, wherein the time segment step length input by each sample time sequence characteristic extractor is as followsThe corresponding iterative prediction time slice step length of the iterative predictor is +.>；
And (3) carrying out loss calculation on the predicted tag value corresponding to the output of the sequence-to-sequence model and the training data, wherein the loss function selection RMSLE is shown in a formula (9):
(9)
wherein, the liquid crystal display device comprises a liquid crystal display device,sample data amount representing one batch, < ->Representing prediction->And updating the model parameters according to the gradient back-propagation values of the model parameters by a time step.
In another aspect, there is provided a shale oil well production prediction apparatus comprising:
the data collection preprocessing module is used for collecting historical production data of the shale oil well and preprocessing the data, wherein the data comprises static parameter data, dynamic production arrangement parameter data and daily oil production data;
the static embedding initial bias module is used for inputting the static parameter data into the static embedding initial bias module, extracting static embedding information as an initialization state of the sequence-to-sequence model, and providing initial state bias of the reaction oil reserve information for the sequence-to-sequence model;
The dynamic-static complementary cross fusion module is used for inputting the static parameter data, the dynamic production scheduling parameter data and the daily oil production data into the dynamic-static complementary cross fusion module, fully exploring the mutual influence of the dynamic-static parameter data and the superposition influence of the dynamic-static parameter data on the yield, and obtaining the complementary fusion information of the dynamic daily oil production scheduling parameter data and the static parameter data as the input of the sequence to the sequence model;
a sequence-to-sequence model for use in performing iterative predictions of shale well production using the sequence-to-sequence model.
In another aspect, an electronic device is provided that includes a processor and a memory having at least one instruction stored therein that is loaded and executed by the processor to implement the shale well production prediction method described above.
In another aspect, a computer readable storage medium having stored therein at least one instruction loaded and executed by a processor to implement the shale well production prediction method described above is provided.
Compared with the prior art, the technical scheme has at least the following beneficial effects:
1) The dynamic and static multiple influence factors are fully utilized, the mutual influence between the dynamic and static multiple influence factors and the superposition influence of the dynamic and static multiple influence factors on the yield are furthest excavated, and meanwhile, the initial state bias of the reaction oil reserve information is set, so that the model can adapt to complex geological engineering conditions, and the yield prediction accuracy is improved;
2) Extracting the autocorrelation time sequence dependence characteristic of daily oil yield by using a sequence-to-sequence model, and simultaneously decoupling input and output by considering the actual demand of oil yield prediction, thereby realizing the dynamic iteration prediction of the yield of any step length in the output length range set during model training;
3) The model is trained based on historical oil yield data, and is simple in training and good in generalization capability.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a shale oil well yield prediction method provided by an embodiment of the invention;
FIG. 2 is a diagram of an overall network architecture provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a dynamic and static complementary cross fusion module according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a method for dividing input/output time slices according to an embodiment of the present invention;
FIG. 5 is a block diagram of a shale oil well production prediction apparatus provided by an embodiment of the invention;
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a shale oil well yield prediction method, including:
s1, collecting historical production data of a shale oil well, and preprocessing the data, wherein the data comprises static parameter data, dynamic production arrangement parameter data and daily oil production data;
s2, inputting the static parameter data into a static embedding initial bias module, extracting static embedding information, and taking the static embedding information as initial state bias from a sequence to a sequence model;
s3, inputting the static parameter data, the dynamic production scheduling parameter data and the daily oil production data into a dynamic-static complementary cross fusion module to obtain complementary fusion information of the dynamic daily oil production scheduling parameter data and the static parameter data, and using the complementary fusion information as input from the sequence to a sequence model;
S4, carrying out iterative prediction on the shale oil well yield by using the sequence-to-sequence model.
The following describes in detail a shale oil well yield prediction method provided by the embodiment of the invention with reference to fig. 2 to 4, which comprises the following steps:
s1, collecting historical production data of a shale oil well, and preprocessing the data, wherein the data comprises static parameter data, dynamic production arrangement parameter data and daily oil production data;
collecting historical production data of shale oil wells, analyzing the data, screening the original data by considering the relativity, comprehensiveness and quantifiability of parameters and the yield, selecting proper static parameter data, dynamic production arrangement parameter data and daily oil production data as data required by yield prediction, taking the shale oil wells as an example, wherein the static parameter data comprises geological and fluid physical parameter data and construction parameter data, and the geological and fluid physical parameter data comprises: the average thickness of the oil layer, the viscosity of the crude oil on the ground, the density of the ground, the solidifying point and the like, and the construction parameter data comprise: the number of fracturing sections, the number of clusters, the fracturing pressure, the carbon dioxide addition, the number of carbon dioxide addition sections, the liquid consumption, the sand addition amount, the fiber and the like, and the dynamic production arrangement parameter data comprise: a nipple, oil pressure, etc.
Preprocessing data, including:
removing or interpolating the empty data to remove abnormal values and obtain effective and clean data;
dividing the data to form training data and verification data, respectively normalizing the training data and the verification data, eliminating the influence of dimension, and forming a data setAnd->，/>Wherein N is the number of samples, ">To influence the independent variable of the oil yield, the constitution is +.>，/>For geological and fluid physical parameter data, +.>Is->Individual geological and fluid physical parameter data, +.>For construction parameter data>Is->Construction parameter data->Scheduling parameter data for dynamic production,/->Is->Dynamic production schedule parameter data,/->Is daily oil production.
S2, inputting the static parameter data into a static embedding initial bias module, extracting static embedding information, and taking the static embedding information as initial state bias from a sequence to a sequence model;
optionally, as shown in fig. 2, the main body of the static embedded initial bias module is a multi-layer perceptron MLP, and adopts a tanh activation function, and the input of the static embedded initial bias module is the static parameter data and the mining daysThe static parameter data includes: geological and fluid physical parameter data- >Construction parameter data->The output is expressed as +.>Subsequently->Hidden state of the sequential feature extractor as the sequence-to-sequence model, +.>The cell state as the time sequence feature extractor is fed into the model as an initial state bias, and a specific formula is shown in (1):
(1)。
considering that the daily recoverable oil quantity, namely daily oil production, is based on oil reserves of an oil field, the oil reserves and static parameters are in a nonlinear relation, and the oil reserves are in a decreasing rule along with the increase of the exploitation time, a static embedded initial bias module is arranged, the main body of the module is a multi-layer perceptron, and a tanh activation function is adopted, so that the initialization state and the static parameters obtained by a sequential characteristic extractor of a sequence-to-sequence model are ensured to be in a nonlinear relation.
Compared with the prior art, the hidden state of the time sequence feature extractor is simpleAnd cell status->Zero initialization mode, the embodiment of the invention obtains +.>By the method, the oil reserve information bias with the change of the production days can be provided for the main body framework of the sequence-to-sequence model, the change trend of the oil yield can be better followed on the basis, and the accuracy of yield prediction is improved.
S3, inputting the static parameter data, the dynamic production scheduling parameter data and the daily oil production data into a dynamic-static complementary cross fusion module to obtain complementary fusion information of the dynamic daily oil production scheduling parameter data and the static parameter data, and using the complementary fusion information as input from the sequence to a sequence model;
optionally, as shown in fig. 3, the dynamic and static complementary cross fusion module includes: (a) A cross attention module and (b) a gate control fusion module;
the cross attention module is divided into a static passage and a dynamic passage, wherein the static passage firstly determines priori influence information of the dynamic production arrangement parameter data on daily oil production through the dynamic yield interaction moduleDetermining superposition influence information of the static parameter data on daily oil production by means of static channel attention>As an output of the static path; the dynamic path firstly determines priori influence information of the static parameter data on daily oil production through a static yield interaction module>Determining superposition influence information of the dynamic production schedule parameter data on daily oil production through dynamic channel attention>As an output of the dynamic path;
the said、/>、/>、/>And daily oil production data are input into the gating fusion module to obtain complementary fusion information +. >Said->Together with daily oil production data as input to a timing feature extractor of the sequence-to-sequence model.
Alternatively, the goal of the static path is to explore the data of the production schedule parameters in the dynamic stateDaily oil production->Static data under a priori influence of +.>Daily oil production->Of (2), wherein->Representing stacking (concat) two matrix data together along an attribute dimension, the specific operations include:
introducing a priori influence by the dynamic yield interaction module as shown in a formula (2), wherein the formula (2) is as follows:
(2)
daily oil production dataBy means of a linear layer->Mapping to query vector +.>(Query) dynamic production scheduling parameter data +.>By two different linear layers +.>And->Mapped as key vectors +.>(Key) and value vector->(Value), then pair->And->Is subjected to a dot multiplication operation and divided by the channel dimension +.>Is prepared from (function: p->And->Scaling the dot product of (i) to avoid oversized result, entering into saturation region of softmax function), and generating importance weight (measuring dynamic production schedule parameter data by weight) in time sequence dimension by using softmax function>Degree of influence on daily oil production), using the generated importance weight pair +. >Re-weighting to obtain the data of the introduced dynamic production arrangement parameters->Daily oil production->Priori shadowPost-response a priori influence information->(the re-weighted data will enhance the value with greater impact on daily oil production and suppress the value with less impact);
the saidAnd the main body of the static path is input with static parameter data +.>The superposition influence of the dynamic daily output arrangement parameter data on the static parameters under the prior influence of the oil output is explored in the attention of the static channel, and superposition influence information of the static parameter data on the daily output is determined>As shown in formula (3):
(3)
first through a linear layerWill->Mapping as +.>Will->By two linear layersAnd->Respectively mapped as +.>And->Then->Transpose and +.>Performing a dot multiplication operation and dividing by the channel dimension +.>Then generating importance weights in channel dimensions using a softmax function, using the generated importance weight pairs +.>Re-weighting (attribute dimension with larger influence on daily oil production superposition and attribute dimension with smaller inhibition influence in static parameter data can be enhanced) to obtain output of a static passage>；
The basic operation of the dynamic path is similar to that of the static path, and the specific operation of the dynamic path comprises the following steps:
Introducing a priori influence by the static yield interaction module as shown in formula (4), wherein formula (4) is as follows:
(4)
will beBy means of a linear layer->Mapping to->Static parameter data->By two linear layers->And->Respectively mapped as +.>And->Then->And->Is subjected to a dot multiplication operation and divided by the channel dimension +.>Then generating importance weights in the time sequence dimension by using softmax function, and using the generated importance weight pairs +.>Re-weighting to obtain the introduced static parameter data +.>Daily oil production->Is->；
Will beAnd the main body of the dynamic path inputs dynamic production schedule parameter data +.>Into the dynamic channel attention, determining the superposition influence information of dynamic production schedule parameter data on daily oil production +.>As shown in formula (5):
(5)
first through a linear layerWill->Mapping as +.>Will->By two linear layersAnd->Respectively mapped as +.>And->Then->Transpose and +.>Performing a dot multiplication operation and dividing by the channel dimension +.>Is then used to generate a weight in the channel dimension using a softmax functionThe importance weight is re-weighted by the generated importance weight pair to obtain the output of the static channel +. >。
Optionally, in the embodiment of the invention, superposition influence information that two paths are prior to each other is obtainedAnd->Afterwards, the two are not directly fused but a gating fusion module is provided, said +.>、/>、/>、/>And daily oil production data are input into the gating fusion module to obtain complementary fusion information +.>Specifically, as shown in formula (6):
(6)
prior influence information of static pathAnd a priori influence information of dynamic pathways +.>Daily oil production data +.>Stacking (concat) together, feeding into linear layer fusion for dimension reduction, and using +.>Activating function to generate gating information->（/>The value of (2) is within +.>And most of the time tends to be 0 or 1), so embodiments of the present invention use it to generate gating information);
utilizing the gating informationControlling the information retention degree after the superposition of the static information path, the operations comprise:
output of static pathThrough a linear layer->And gating information->Hadamard product (Hadamard product) by which gating information elements tend to correspond to values of 1>The information of (2) is preserved and the information corresponding to the value tending to 0 is discarded;
output of dynamic pathThrough a linear layer->And->By doing Hadamard product, the gating information element tends to be 1 with corresponding value +. >The information of (2) is preserved and the information corresponding to the value tending to 0 is discarded;
for static channel information after gate screeningDynamic path informationPerforming element addition operation to obtain complementary fusion information after dynamic and static complementary cross fusion>。
The embodiment of the invention can avoid information redundancy and transmission of error information through the gating fusion module, and ensure the effectiveness of information transmission.
S4, carrying out iterative prediction on the shale oil well yield by using the sequence-to-sequence model.
Optionally, as shown in fig. 2, the sequence-to-sequence model main body architecture is composed of a time sequence feature extractor and an iteration predictor, both of which adopt a long-short-period memory network LSTM, the time sequence feature extractor uses the static embedded information extracted by the static embedded initial bias module as an initial state bias, uses the complementary fusion information output by the dynamic-static complementary cross fusion module and daily oil production data as inputs, and extracts the autocorrelation of the dynamic daily oil production data according to the characteristic that the output of the current time state of the LSTM is affected by the previous time state, as shown in a formula (7):
(7)
time input slice of the time sequence feature extractor Segment(s)Output +.>And historical daily oil production data->Stacked together as input to the timing feature extractor, the output of the static embedded initial bias module>And->As the initial state bias of the time sequence feature extractor, the time sequence feature extractor is utilized to extract the autocorrelation of the dynamic daily output data, and finally the output state of the time sequence feature extractor is obtained>And->；
This output stateAnd->Based on the initial state bias, the superposition influence of the dynamic state and the static state is fused, and the autocorrelation of daily oil yield of the input time segment is extracted.
The saidAnd->Feeding into said iterative predictor, said iterative predictor being based on said +.>Anddynamic iterative prediction of oil production is performed in combination with dynamic production schedule parameter data, as shown in equation (8):
(8)
the iterative predictor inputs dynamic production schedule parameter data corresponding to the current time step at each time stepAnd the daily oil production prediction value of the iteration predictor of the previous time step +.>And input the output hidden state of the previous time step +.>And cell status->When being the first starting state of the iterative predictor, the +. >For the output state of the timing feature extractor +.>Then based on this, the daily oil production of the current time step is predicted +.>；
The process described by equation (8) is repeated until the desired prediction step size is reached.
Equation (8) describes only one time step of the iterative prediction process, and the process described in equation (8) is repeated throughout the prediction processRun until reaching the required prediction step. Through a sequence-to-sequence architecture, the input time segment and the predicted time segment are decoupled, so that the autocorrelation of daily output data can be extracted, and the output length ++ ++set during model training can be realized>Dynamic yield iterative prediction of arbitrary step sizes in a range.
Optionally, the method further includes dividing data into training data and verification data, training the static embedded initial bias module, the dynamic-static complementary cross fusion module and the sequence-to-sequence model by using the training data, including:
as shown in FIG. 4, the input time segments and the predicted time segments corresponding to each sample are divided, and the normalized corresponding features and labels are combined and sent into each module according to the parameters required by each module, wherein the time segment step size input by each sample time sequence feature extractor is as follows The corresponding iterative prediction time slice step length of the iterative predictor is +.>；
And (3) carrying out loss calculation on the predicted tag value corresponding to the output of the sequence-to-sequence model and the training data, wherein the loss function selection RMSLE is shown in a formula (9):
(9)
wherein, the liquid crystal display device comprises a liquid crystal display device,sample data amount representing one batch, < ->Representing pre-emphasisMeasure->And updating the model parameters according to the gradient back-propagation values of the model parameters by a time step.
The visual observation shows that the input and output of the model are in non-one-to-one correspondence, and after training, the model can realizeAnd outputting any step length in the range.
According to the embodiment of the invention, the trained model is verified by using verification data, the generalization and the accuracy are weighed, and the optimal model is reserved. Based on the reserved optimal model, efficient and accurate time sequence dynamic yield iterative prediction of shale oil well yield is realized.
As shown in fig. 5, the embodiment of the invention further provides a shale oil well yield prediction device, which comprises:
the data collection preprocessing module 510 is used for collecting historical production data of the shale oil well, preprocessing the data, wherein the data comprises static parameter data, dynamic production scheduling parameter data and daily oil production data;
The static embedding initial bias module 520 is configured to input the static parameter data into the static embedding initial bias module, extract static embedding information as an initialization state of a sequence-to-sequence model, and provide an initial state bias of reaction oil reserve information for the sequence-to-sequence model;
the dynamic-static complementary cross fusion module 530 is configured to input the static parameter data, the dynamic production scheduling parameter data and the daily oil production data into the dynamic-static complementary cross fusion module, fully explore the mutual influence of the dynamic-static parameter data and the superposition influence of the dynamic-static parameter data on the yield, and obtain complementary fusion information of the dynamic daily oil production scheduling parameter data and the static parameter data as input from the sequence to the sequence model;
a sequence-to-sequence model 540 for iterative prediction of shale well production using the sequence-to-sequence model.
The functional structure of the shale oil well yield prediction device provided by the embodiment of the invention corresponds to the shale oil well yield prediction method provided by the embodiment of the invention, and is not repeated here.
Fig. 6 is a schematic structural diagram of an electronic device 600 according to an embodiment of the present invention, where the electronic device 600 may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 601 and one or more memories 602, where at least one instruction is stored in the memories 602, and the at least one instruction is loaded and executed by the processors 601 to implement the steps of the shale oil well yield prediction method described above.
In an exemplary embodiment, a computer readable storage medium, such as a memory including instructions executable by a processor in a terminal to perform the shale well production prediction method described above, is also provided. For example, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.

Claims (8)

1. A shale oil well production prediction method, comprising:
collecting historical production data of shale oil wells, and preprocessing the data, wherein the data comprises static parameter data, dynamic production scheduling parameter data and daily oil production data;
Inputting the static parameter data into a static embedding initial bias module, extracting static embedding information, and taking the static embedding information as initial state bias from a sequence to a sequence model;
inputting the static parameter data, the dynamic production scheduling parameter data and the daily oil production data into a dynamic-static complementary cross fusion module to obtain complementary fusion information of the dynamic daily oil production scheduling parameter data and the static parameter data, and taking the complementary fusion information as input of the sequence to a sequence model;
performing iterative prediction of shale oil well yield by using the sequence-to-sequence model;
the dynamic and static complementary cross fusion module comprises: two major parts, namely a cross attention module and a gate control fusion module;
the cross attention module is divided into a static passage and a dynamic passage, wherein the static passage firstly determines priori influence information of the dynamic production arrangement parameter data on daily oil production through the dynamic yield interaction moduleDetermining superposition influence information of the static parameter data on daily oil production by means of static channel attention>As an output of the static path; the dynamic path firstly determines priori influence information of the static parameter data on daily oil production through a static yield interaction module>Determining superposition influence information of the dynamic production schedule parameter data on daily oil production through dynamic channel attention >As an output of the dynamic path;
the said、/>、/>、/>And daily oil production data are input into the gating fusion module to obtain complementary fusion information +.>Said->As input to a sequential feature extractor of the sequence-to-sequence model along with daily oil production data;
the sequence-to-sequence model main body framework consists of a time sequence feature extractor and an iteration predictor, wherein the time sequence feature extractor and the iteration predictor both adopt a long-short period memory network LSTM, the time sequence feature extractor takes static embedded information extracted by the static embedded initial bias module as initial state bias, takes complementary fusion information output by the dynamic-static complementary cross fusion module and daily oil production data as input, and extracts autocorrelation of dynamic daily oil production data according to the characteristic that the output of the current time state of the LSTM is influenced by the previous time state, as shown in a formula (7):
（7）；
time input segment of the time sequence feature extractorOutput +.>And historical daily oil production data->Stacked together as input to the timing feature extractor, the output of the static embedded initial bias module>And->As the initial state bias of the time sequence feature extractor, the time sequence feature extractor is utilized to extract the autocorrelation of the dynamic daily output data, and finally the output state of the time sequence feature extractor is obtained >And->；
The saidAnd->Feeding into said iterative predictor, said iterative predictor being based on said +.>And->Dynamic iterative prediction of oil production is performed in combination with dynamic production schedule parameter data, as shown in equation (8):
（8）；
the iterative predictor inputs dynamic production schedule parameter data corresponding to the current time step at each time stepAnd the daily oil production prediction value of the iteration predictor of the previous time step +.>And input the output hidden state of the previous time step +.>And cell status->When being the first starting state of the iterative predictor, the +.>，/>For the output state of the timing feature extractor +.>，/>Then based on this, the daily oil production of the current time step is predicted +.>；
The process described by equation (8) is repeated until the desired prediction step size is reached.
2. The method of claim 1, wherein the body of the static embedded initial bias module is a multi-layer perceptron MLP and employs a tanh activation function, and the inputs of the static embedded initial bias module are the static parameter data and days of miningThe static parameter data includes: geology and flowPhysical property parameter data->Construction parameter data- >The output is expressed as +.>Subsequently->Hidden state of the sequential feature extractor as the sequence-to-sequence model, +.>The cell state as the time sequence feature extractor is fed into the model as an initial state bias, and a specific formula is shown in (1):
（1）。
3. the method of claim 1, wherein the specific operation of the static path comprises:
introducing a priori influence by the dynamic yield interaction module as shown in a formula (2), wherein the formula (2) is as follows:
（2）；
daily oil production dataBy means of a linear layer->Mapping to query vector +.>Dynamic production of scheduling parameter data->By two different linear layers +.>And->Mapped as key vectors +.>Sum vector->Then->Andis subjected to a dot multiplication operation and divided by the channel dimension +.>Then generating importance weights in the time sequence dimension by using softmax function, and using the generated importance weight pairs +.>Re-weighting to obtain the data of the introduced dynamic production arrangement parameters->Daily oil production->Priori influence information after a priori influence +.>；
The saidAnd the main body of the static path is input with static parameter data +.>Into the static channel attention, determining superposition influence information of static parameter data on daily oil production +. >As shown in formula (3):
（3）；
first through a linear layerWill->Mapping as +.>Will->By two linear layers->And->Respectively mapped as +.>And->Then->Transpose and +.>Performing a dot multiplication operation and dividing by the channel dimension +.>Then generating importance weights in channel dimensions using a softmax function, using the generated importance weight pairs +.>Re-weighting to obtain the output +.>；
The specific operation of the dynamic path comprises the following steps:
introducing a priori influence by the static yield interaction module as shown in formula (4), wherein formula (4) is as follows:
（4）；
will beBy means of a linear layer->Mapping to->Static parameter data->By two linear layers->And->Respectively mapped as +.>And->Then->And->Is subjected to a dot multiplication operation and divided by the channel dimension +.>Then generating importance weights in the time sequence dimension by using softmax function, and using the generated importance weight pairs +.>Re-weighting to obtain the introduced static parameter data +.>Daily oil production->Is->；
Will beAnd the main body of the dynamic path inputs dynamic production schedule parameter data +.>Into the dynamic channel attention, determining the superposition influence information of dynamic production schedule parameter data on daily oil production +. >As shown in formula (5):
（5）；
first through a linear layerWill->Mapping as +.>Will->By two linear layers->Andrespectively mapped as +.>And->Then->Transpose and +.>Performing a dot product operation and dividing by the channel dimensionThen generating importance weights in channel dimensions by using softmax function, re-weighting by using the generated importance weights to obtain output of static channel->。
4. The method of claim 1, wherein said combining said、/>、/>、/>And daily oil production data are input into the gating fusion module to obtain complementary fusion information +.>Specifically, as shown in formula (6):
（6）；
prior influence information of static pathAnd a priori influence information of dynamic pathways +.>Daily oil production data +.>Stacked together, fed into a linear layer fusion for dimension reduction and using +.>Activating function to generate gating information->；
Utilizing the gating informationControlling the information retention degree after the superposition of the static information path, the operations comprise:
output of static pathThrough a linear layer->And gating information->By doing Hadamard product, the gating information element tends to be 1 with corresponding value +.>The information of (2) is preserved and the information corresponding to the value tending to 0 is discarded;
Output of dynamic pathThrough a linear layer->And->By doing Hadamard product, the gating information element tends to be 1 with corresponding value +.>The information of (2) is preserved and the information corresponding to the value tending to 0 is discarded;
for static channel information after gate screeningAnd dynamic path information->Performing element addition operation to obtain complementary fusion information after dynamic and static complementary cross fusion>。
5. The method of claim 1, further comprising separating data into training data and validation data, training the static embedded initial bias module, the dynamic-static complementary cross-fusion module, and the sequence-to-sequence model using the training data, comprising:
dividing the input time segment and the predicted time segment corresponding to each sample, combining the normalized corresponding characteristics and labels, and sending the normalized corresponding characteristics and labels into each module according to the parameters required by each module, wherein the time segment step length input by each sample time sequence characteristic extractor is as followsThe corresponding iterative prediction time slice step length of the iterative predictor is +.>；
And (3) carrying out loss calculation on the predicted tag value corresponding to the output of the sequence-to-sequence model and the training data, wherein the loss function selection RMSLE is shown in a formula (9):
（9）；
Wherein, the liquid crystal display device comprises a liquid crystal display device,sample data amount representing one batch, < ->Representing prediction->And updating the model parameters according to the gradient back-propagation values of the model parameters by a time step.
6. A shale oil well production prediction apparatus, comprising:
the data collection preprocessing module is used for collecting historical production data of the shale oil well and preprocessing the data, wherein the data comprises static parameter data, dynamic production arrangement parameter data and daily oil production data;
the static embedding initial bias module is used for inputting the static parameter data into the static embedding initial bias module, extracting static embedding information as an initialization state of the sequence-to-sequence model, and providing initial state bias of the reaction oil reserve information for the sequence-to-sequence model;
the dynamic-static complementary cross fusion module is used for inputting the static parameter data, the dynamic production scheduling parameter data and the daily oil production data into the dynamic-static complementary cross fusion module, fully exploring the mutual influence of the dynamic-static parameter data and the superposition influence of the dynamic-static parameter data on the yield, and obtaining the complementary fusion information of the dynamic daily oil production scheduling parameter data and the static parameter data as the input of the sequence to the sequence model;
A sequence-to-sequence model for performing iterative predictions of shale well production using the sequence-to-sequence model;
the dynamic and static complementary cross fusion module comprises: two major parts, namely a cross attention module and a gate control fusion module;
the cross injectionThe power module is divided into a static passage and a dynamic passage, wherein the static passage firstly determines priori influence information of the dynamic production arrangement parameter data on daily oil production through the dynamic yield interaction moduleThe method comprises the steps of carrying out a first treatment on the surface of the Determining superposition influence information of the static parameter data on daily oil production by static channel attention>As an output of the static path; the dynamic path firstly determines priori influence information of the static parameter data on daily oil production through a static yield interaction module>Determining superposition influence information of the dynamic production schedule parameter data on daily oil production through dynamic channel attention>As an output of the dynamic path;
the said、/>、/>、/>And daily oil production data are input into the gating fusion module to obtain complementary fusion information +.>Said->As input to a sequential feature extractor of the sequence-to-sequence model along with daily oil production data;
the sequence-to-sequence model main body framework consists of a time sequence feature extractor and an iteration predictor, wherein the time sequence feature extractor and the iteration predictor both adopt a long-short period memory network LSTM, the time sequence feature extractor takes static embedded information extracted by the static embedded initial bias module as initial state bias, takes complementary fusion information output by the dynamic-static complementary cross fusion module and daily oil production data as input, and extracts autocorrelation of dynamic daily oil production data according to the characteristic that the output of the current time state of the LSTM is influenced by the previous time state, as shown in a formula (7):
（7）；
Time input segment of the time sequence feature extractorOutput +.>And historical daily oil production data->Stacked together as input to the timing feature extractor, the output of the static embedded initial bias module>And->As the initial state bias of the time sequence feature extractor, the time sequence feature extractor is utilized to extract the autocorrelation of the dynamic daily output data, and finally the output state of the time sequence feature extractor is obtained>And->；
The saidAnd->Feeding into said iterative predictor, said iterative predictor being based on said +.>And->Dynamic iterative prediction of oil production is performed in combination with dynamic production schedule parameter data, as shown in equation (8):
（8）；
the iterative predictor inputs dynamic production schedule parameter data corresponding to the current time step at each time stepAnd the daily oil production prediction value of the iteration predictor of the previous time step +.>And input the output hidden state of the previous time step +.>And cell status->When being the first starting state of the iterative predictor, the +.>For the output state of the timing feature extractor +.>Then based on this, the daily oil production of the current time step is predicted +. >；
The process described by equation (8) is repeated until the desired prediction step size is reached.
7. An electronic device comprising a processor and a memory having at least one instruction stored therein, wherein the at least one instruction is loaded and executed by the processor to implement the shale well production prediction method of any of claims 1-5.
8. A computer readable storage medium having stored therein at least one instruction, wherein the at least one instruction is loaded and executed by a processor to implement the shale well production prediction method of any of claims 1-5.
CN202310350371.1A 2023-04-04 2023-04-04 Shale oil well yield prediction method and device Active CN116151480B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310350371.1A CN116151480B (en) 2023-04-04 2023-04-04 Shale oil well yield prediction method and device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310350371.1A CN116151480B (en) 2023-04-04 2023-04-04 Shale oil well yield prediction method and device

Publications (2)

Publication Number Publication Date
CN116151480A CN116151480A (en) 2023-05-23
CN116151480B true CN116151480B (en) 2023-07-18

Family

ID=86340966

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310350371.1A Active CN116151480B (en) 2023-04-04 2023-04-04 Shale oil well yield prediction method and device

Country Status (1)

Country Link
CN (1) CN116151480B (en)

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9022140B2 (en) * 2012-10-31 2015-05-05 Resource Energy Solutions Inc. Methods and systems for improved drilling operations using real-time and historical drilling data
CN113236228B (en) * 2021-06-24 2023-07-25 中海石油(中国)有限公司 Method and system for rapidly predicting single well yield
CN113722997A (en) * 2021-09-01 2021-11-30 北京中地金石科技有限公司 New well dynamic yield prediction method based on static oil and gas field data
CN113962148B (en) * 2021-10-20 2022-09-13 中国石油大学(北京) Yield prediction method, device and equipment based on convolutional coding dynamic sequence network
CN114021922A (en) * 2021-10-27 2022-02-08 中海石油(中国)有限公司 Oil well productivity main control factor analysis method, system, equipment and storage medium
CN113988479A (en) * 2021-12-07 2022-01-28 扬州江苏油田瑞达石油工程技术开发有限公司 Pumping well multi-well dynamic liquid level depth prediction method based on dynamic and static information feature fusion neural network
CN114925623B (en) * 2022-07-22 2022-09-23 中国地质大学（北京） Oil and gas reservoir yield prediction method and system

Also Published As

Publication number Publication date
CN116151480A (en) 2023-05-23

Similar Documents

Publication Publication Date Title
Tang et al. A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
Anand et al. Fractional-Iterative BiLSTM Classifier: A Novel Approach to Predicting Student Attrition in Digital Academia
Bao et al. Data-driven end-to-end production prediction of oil reservoirs by enkf-enhanced recurrent neural networks
Zhang et al. Fourier neural operator for solving subsurface oil/water two-phase flow partial differential equation
CN111368870A (en) Video time sequence positioning method based on intra-modal collaborative multi-linear pooling
He Reduced-order modeling for oil-water and compositional systems, with application to data assimilation and production optimization
Yuyang et al. Shale gas well flowback rate prediction for Weiyuan field based on a deep learning algorithm
CN109918649A (en) A kind of suicide Risk Identification Method based on microblogging text
CN111832227A (en) Shale gas saturation determination method, device and equipment based on deep learning
Wang et al. A novel shale gas production prediction model based on machine learning and its application in optimization of multistage fractured horizontal wells
Jiang et al. Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models
Park et al. Hybrid physics and data-driven modeling for unconventional field development and its application to US onshore basin
Kumar Pandey et al. Employing deep learning neural networks for characterizing dual-porosity reservoirs based on pressure transient tests
WO2020219057A1 (en) Systems and methods for determining grid cell count for reservoir simulation
Jiang et al. Metnet: a mutual enhanced transformation network for aspect-based sentiment analysis
Peng et al. A proxy model to predict reservoir dynamic pressure profile of fracture network based on deep convolutional generative adversarial networks (DCGAN)
Omosebi et al. Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage
Liu et al. Predicting gas flow rate in fractured shale reservoirs using discrete fracture model and GA-BP neural network method
Xie et al. Intelligent modeling with physics-informed machine learning for petroleum engineering problems.
Yang et al. Using one-dimensional convolutional neural networks and data augmentation to predict thermal production in geothermal fields
Yang et al. A steam injection distribution optimization method for SAGD oil field using LSTM and dynamic programming
Shan et al. Physics-informed machine learning for solving partial differential equations in porous media.
CN116151480B (en) Shale oil well yield prediction method and device
Huang et al. A deep-learning-based graph neural network-long-short-term memory model for reservoir simulation and optimization with varying well controls
Razak et al. Embedding physical flow functions into deep learning predictive models for improved production forecasting

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant