MANAGING TRAINING WELLS FOR TARGET WELLS IN MACHINE LEARNING

Systems, methods, and apparatus including computer-readable mediums for managing training wells for target wells in machine learning are provided. In one aspect, a method includes: for each training well of a plurality of training wells, building a training network for the training well based on well log data of the training well, predicting a target well log of a target well using the training network built for the training well, determining a relevancy level between the training well and the target well based on the predicted target well log of the target well and a measured target well log of the target well, and selecting relevant training wells among the plurality of training wells based on the relevancy levels associated with the plurality of training wells.

TECHNICAL FIELD

The present disclosure relates to hydrocarbon reservoir properties, particularly to managing training wells for predicting properties of target reservoirs or wells in machine learning.

BACKGROUND

In the oil and gas and mining industry, wells are drilled for both exploration and production purposes. Wells are commonly logged by lowering a combination of physical sensors downhole to acquire data that measures various rock and fluid properties, e.g., irradiation, density, electrical and acoustic properties. Well log data are commonly used to estimate reservoir properties including porosity, fluid saturation, and permeability, which are required for reservoir modeling, reservoir estimation, and production forecasting. Machine learning provides an alternative approach to estimate those reservoir properties based on multiple well data, where a training data set needs to be optimized to achieve a high prediction accuracy in the target wells or zones. However, there is no systematic and quantitative approach to optimize the training data set for achieving the high prediction accuracy.

SUMMARY

The present disclosure describes methods and systems for managing training wells for target wells, e.g., hydrocarbon reservoirs, in machine learning.

One aspect of the present disclosure features a method of managing training wells for a target well, the method including: for each training well of a plurality of training wells, building a training network for the training well based on well log data of the training well, predicting a target well log of the target well using the training network built for the training well, determining a relevancy level between the training well and the target well based on the predicted target well log of the target well and a measured target well log of the target well, and selecting relevant training wells among the plurality of training wells based on the relevancy levels associated with the plurality of training wells. The target well can be a well to be drilled, and the target well and the plurality of training wells can be within a same reservoir.

In some embodiments, determining the relevancy level between the training well and the target well includes: calculating a correlation coefficient between the predicted target well log of the target well and the measured target well log of the target well, and determining the calculated correlation coefficient to be the relevancy level between the training well and the target well.

In some embodiments, selecting the relevant training wells among the plurality of training wells based on the relevancy levels associated with the plurality of training wells includes: comparing the relevancy levels associated with the plurality of training wells with a predetermined relevancy threshold, and selecting, among the plurality of training wells, training wells having corresponding relevancy levels greater than or equal to the predetermined threshold to be the relevant training wells for the target well.

In some embodiments, building the training network for the training well based on the well log data of the training well includes: training the training network using multiple input well logs of the training well as input parameters and an output well log of the training well as an output parameter, the well log data of the training well including the multiple input well logs and the output well log.

In some embodiments, the multiple input well logs of the training well include two or more of a list of well logs including permeability, porosity, oil saturation, water saturation, lithology, matrix density, and clay content, and the output well log of the training well includes at least one of a list of well logs including bulk density, resistivity, velocity, gamma ray, deep induction, neutron porosity, and density porosity.

In some embodiments, predicting the target well log of the target well using the training network for the training well includes: providing multiple well logs of the target well as inputs of the training network, the multiple well logs of the target well corresponding to the multiple input well logs of the training well, and obtaining an output of the respective training network based on the multiple well logs of the target well as the predicted target well log of the target well.

In some embodiments, the training network includes a single layer neural network having an input layer, a hidden layer, and an output layer.

In some embodiments, the method further includes: obtaining the plurality of training wells by filtering a multi-well database storing well data of multiple wells. The well data of the multiple wells can include at least one of well attributes, well logs, or core data in a field where the multiple wells are located.

In some embodiments, filtering the multi-well database includes: selecting wells located within a predetermined proximity of the target well in a field, where the plurality of training wells are within the selected wells.

In some embodiments, filtering the multi-well database includes: selecting wells for the target well based on stratigraphic zonation, where the selected wells have a common set of geological properties with the target well in multiple zones of a field, and where the plurality of training wells are within the selected wells.

In some embodiments, filtering the multi-well database includes: selecting wells for the target well based on one or more operational settings, where the plurality of training wells are within the selected wells. The one or more operational settings can include well type, drilling mud properties, and logging survey types.

In some embodiments, the method further includes: determining whether the selected relevant training wells satisfy a coverage criteria for the target well. In some cases, determining whether the selected relevant training wells satisfy a coverage criteria for the target well includes: obtaining, for each of a plurality of pairs of well log samples in the target well and the selected relevant training wells, a specified norm of distance between a well log sample in the target well and corresponding well log samples in the selected relevant training wells, averaging the specified norms of distance of the plurality of pairs of well log samples in the target well and the selected relevant training wells to obtain an average coverage, and determining whether the average coverage exceeds a predetermined coverage threshold.

In some embodiments, the method further includes: in response to determining that the selected relevant training wells satisfies the coverage criteria for the target well, providing the selected relevant training wells as a training set of an artificial intelligence (AI) network. The AI network is configured to be trained using the training set based on at least one machine learning algorithm that is more complicated than that implemented in the training network. In some examples, the AI network includes a capsule convolutional neural network and a deep belief neural network that are interconnected with each other.

Implementations of the above techniques include methods, systems, computer program products and computer-readable media. In one example, a method can be performed by at least one processor coupled to at least one non-volatile memory and the methods can include the above-described actions. In another example, one such computer program product is suitably embodied in a non-transitory machine-readable medium that stores instructions executable by one or more processors. The instructions are configured to cause the one or more processors to perform the above-described actions. One such computer-readable medium stores instructions that, when executed by one or more processors, are configured to cause the one or more processors to perform the above-described actions.

The details of one or more implementations of the subject matter of this specification are set forth in the accompanying drawings and associated description. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.

DETAILED DESCRIPTION

Implementations of the present specification provide techniques for managing training wells for target wells in machine learning. The techniques develop a systematic workflow to select a training set including multiple training wells out of a larger number of wells for the target wells, so that an artificial intelligence (AI) network can be trained using the training set based on machine learning, and the trained AI network can be used to forecast (predict or estimate) reservoir properties of the target wells.

In some embodiments, the workflow includes a series of sequential steps: 1) building a multi-well database that includes all the well data; 2) for a target well, narrowing down the training well selection, e.g., by filtering the database with area map, stratigraphic zonation, and operational settings; 3) performing relevancy testing and ranking, which can include: a) building a respective neural network between well logs for each training well, b) applying the neural network to the target well to predict a target log; c) calculating a relevancy between the target well with each training well based on a correlation coefficient between a target measured log and a corresponding predicted log; d) ranking the selected training wells based on the relevancy; 4) if the relevancy is higher than a predetermined threshold, a training well is selected into the training set for the target well; 5) applying a coverage analysis to estimate if the training set is large enough to represent the target well; 6) once the training set is optimized, the data of all training wells in the training set can be used as the input to feed into the AI network that can be configured to implement more complex or advanced machine learning algorithms to train the AI network. Then, the trained AI network can be used to forecast or predict one or more reservoir properties of the target well with a high prediction accuracy.

In some embodiments, the training set can include depth and geological information and reservoir parameters of the training wells as input parameters and well logs of the training wells as the target parameters. The reservoir parameters can include permeability, porosity, oil saturation, water saturation, lithology, matrix density, and/or clay content. The well logs can include logs of bulk density, resistivity, velocity, gamma ray, deep induction, neutron porosity, and/or density porosity. The techniques can then subsequently train an artificial intelligence (AI) network, e.g., a coupled deep brief and capsule convolutional neural network, with respect to the training set, and return a trained network. The trained network can be then used together with the depth and geological information as well as assumed reservoir properties of a newly planned well to estimate jointly multiple well logs of the newly planned well at a new specified location based on the geological formation. The estimated well logs can be in consistency or reconciliation with the training well logs. Therefore, the framework can use both spatial, geological settings, stratigraphic layering, and temporal properties to adjust intelligently to accurately estimate the forecasted well logs in the newly planned well and to ensure consistency with previously collected well logs.

The techniques herein can avoid using all the well data in the multi-well database for training the AI network, which can greatly improve a training speed and avoid large consumption of computation and storage resources. Also, the techniques apply relevancy testing and ranking to select most relevant training wells for the target wells, which can efficiently and greatly improve the prediction accuracy of the trained AI network. Further, the techniques provide a systematic workflow to sequentially filter the multi-well database to select the most training wells for the target wells, which increase reliability of the selection and thus improve the prediction accuracy. The techniques herein can be applied to any systems, devices, and methods for selecting relevant training data for target data, e.g., in machine learning or any other suitable applications.

FIG.1is a schematic diagram illustrating an example process100of forecasting well logs for new wells based on training an artificial intelligence (AI) network, e.g., machine learning and inference. The process100can be implemented by a computing system that can include one or more computing devices and one or more machine-readable repositories or databases that are in communication with each other. The new wells can be target wells or candidate wells. The databases can store training well logs, well location data of drilled wells in one or more hydrocarbon reservoirs, geological reservoir data, reservoir properties, and/or measurement data of the drilled wells. The computing system can use both spatial, geological settings, stratigraphic layering, and temporal properties to adjust intelligently to accurately estimate well logs in the new wells.

FIG.2shows an example200of designating a new well220in a hydrocarbon reservoir202where multiple training wells210can be previously drilled or formed. Each well210can be a borehole extending from a terranean surface (or the Earth's surface) to a respective subterranean zone of interest in the reservoir202. The well210can be any suitable type of well, e.g., a well including a single wellbore or a well including multiple wellbores. The well210can be configured to produce hydrocarbon components, e.g., gas, oil, water, or any suitable combinations, from the respective subterranean zone in the reservoir202. The wells212can be made at different times, e.g., with a difference of 5 years, 10 years, 20 years, 30 years, or 50 years, and their well logs of the wells212can be temporally and spatially distributed among a wide time range.

The new well220can be a candidate well (or a target well) for drilling in the reservoir202. An operator can first forecast or estimate well log responses of one or more candidate wells, then derive petrophysical parameters from the forecasted well log responses, and finally select one of the one or more candidate wells with a desired (or suitable) performance (or object) (e.g., high hydrocarbon productivity) and/or better performance than other candidate wells. The well logs of each candidate well can be forecasted as described inFIG.1. Depending on the objective of the operator, the computing system may conduct certain well logs and can utilize the information of the well logs in retraining the AI network102.

With reference toFIG.1, well log data of training wells, including training well logs104, e.g., of the training wells210in the reservoir202ofFIG.2, depth and geological information, e.g., of the training wells210ofFIG.2, and reservoir parameters, e.g., of the training wells210ofFIG.2, can be first processed at a processing step105into a training set106. For example, the well log data can be filtered with a linear smoothing filter to remove outliers and artifacts in the data. The filter window size may either be chosen manually or be a fixed percentage of the overall data length. The training set106may also be visually inspected for erroneous artifacts. The processed data are then categorized, and may be reduced further if desired. Finally, the data are separated into input parameters and output (or target) parameters for the training. The training set106can include the depth and geological information and the reservoir parameters of the training wells as input parameters and the multiple well logs of the training wells jointly as output (or target) parameters. The reservoir parameters can include permeability, porosity, oil saturation, water saturation, lithology, matrix density, and/or clay content. The well logs can include logs of bulk density, resistivity, velocity, gamma ray, deep induction, neutron porosity, and/or density porosity. Instead of using just one well log as the output parameter, the computing system can train the AI network102to simultaneously output multiple joint well logs.

The computing system can train an artificial intelligence (AI) network102with respect to the training set106at a processing step107and return a trained network102′. In some examples, the AI network102can be an artificial neural network, e.g., an integrated (or interconnected) network coupling a capsule convolutional neural network (CNN) and a deep belief neural network (NN). The AI network102can use at least one machine learning (ML) algorithm. The at least one ML algorithm can include at least one of a linear regression, a support vector regression, or a deep learning algorithm including a convolutional neural network (CNN) algorithm or a Recurrent Neural Network (RNN) algorithm.

At a processing step109, the computing system can use the trained network102′ together with new well data108of a newly planned well to jointly forecast multiple well logs110in the newly planned well at a new specified location, e.g., the new well220ofFIG.2. The forecasted multiple well logs are consistent or reconciled with each other, because they are optimized at the same time. In this way, inconsistencies due to deriving reservoir parameters individually or superimposing a mathematical relationship with estimated or assumed parameters can be avoided.

The new well data108can be conformed with respect to information of the training wells as the training set106. For example, the new well data108can include depth and geological information of the newly planned well that can be obtained based on the spatial relationship between the training wells and the newly planned well, as illustrated inFIG.2. The new well data108can also include estimated (or assumed) reservoir properties or petrophysical parameters. For example, the estimated petrophysical parameters can be based on the known petrophysical parameters of the training wells and/or the reservoir. The known petrophysical parameters can be reconciled petrophysical parameters based on reconciled well logs of the training wells. In some cases, the estimated petrophysical parameters can be reconciled petrophysical parameters derived based on reconciled and estimated well logs of the newly planned well, e.g., the well logs110in a previous forecasting or estimation using the trained network102′.

The forecasted well logs110for the new well can be in consistency or reconciliation with the training well logs104. That is, the well logs110of the new well and the training well logs104are reconciled well logs. In some embodiments, the trained network102′ can be used to obtain reconciled well logs for training wells, e.g., by replacing the new well data108with well data of the training wells at the processing step109. The reconciled well logs of the training wells can be used for generating reconciled reservoir parameters.

To achieve a high prediction accuracy in target wells, the training set needs to be optimized to. The current industry practice tends to use all wells available to feed into machine leaning algorithms. Sometimes, geoscientists and engineers select the input wells by location, zonation, and operational settings. So far, there is no systematic and quantitative approach to optimize the training set for achieving the best prediction accuracy. In our invention, we designed a systematic workflow to select the optimal training set out of a large number of wells based on a novel concept of “relevancy testing.”

FIG.3shows an example process300of managing training wells for a target well in machine learning. The process300can be implemented in the processing step105ofFIG.1to generate a training set, e.g., the training set106ofFIG.1, for the target well. The process300can be performed by a computing system. The computing system can be the computing system for the process100ofFIG.1, another computing system externally coupled to the computing system for the process100, or a combination thereof.

A multi-well database is obtained at step302. The multi-well database can include well data in a field. The field can include a plurality of wells distributed in one or more zones. The well data can include well attributes, reservoir parameters, well logs, core data, or a combination thereof. The well attributes can include geological information (e.g., coordinates), well type, zonation, or drilling conditions (e.g., depth). The reservoir parameters can include permeability, porosity, oil saturation, water saturation, lithology, matrix density, clay content, or any combination thereof. The well logs can include logs of bulk density, resistivity, velocity, gamma ray, deep induction, neutron porosity, density porosity, or any combination thereof. The well data can be compiled in one or more files, e.g., in LAS or comma-separated values (CSV) file format. The well data can include a respective file for each well.

In some embodiments, the well data is saved on a local computer of the computing system. The computing system can build and maintain the multi-well database. In some embodiments, the well data is saved on a remote server. The multi-well database can be built by the remote server different from the computing system. The multi-well database can be a public database. The computing system can search the multi-well database to retrieve well data for the target well.

The computing system narrows down a selection of training wells for the target well by filtering the multi-well database at304. The computing system can filter the multi-well database based on one or more factors including location proximity, stratigraphic zonation, and one or more operational settings.

In some embodiments, the computing system selects wells that are located within a proximity of the target well, e.g., within a circle of 1 mile in radius. The location proximity can defined by a user or empirical data.FIG.4is a schematic diagram400illustrating selecting training wells for a target well based on location proximity in a field402. A number of wells are distributed in the field402, including multiple target wells410with remaining wells as training wells420. For a particular target well410′, the computing system can filter the number of wells by putting a circle430around the target well410′, e.g., with the target well410′ as the center of the circle430. Wells within the circle430are selected as training wells420′ for the target well410′ to be used in the following steps.

In some embodiments, the computing system selects training wells for the target well based on stratigraphic zonation. The selected training wells have a common set of geological tops or rock types with the target well, e.g., based on lithology, petrophysical properties, age, or mineralogy.FIG.5is a schematic diagram500illustrating selecting training wells for target wells based on stratigraphic zonation. Each well has a geological profile502,504,506,508varying along Zona A and Zone B. Wells, e.g., training wells1,2,3, that have a geological profile502,504,508similar to the geological profile506and vary between same boundaries512and514of Zone A and514and516of Zone B, are selected as training wells for the target well.

In some embodiments, the computing system selects training wells for the target well based on one or more operational settings including well type (e.g., vertical/deviated/horizontal), drilling mud (e.g., oil based or water based mud), and logging survey types (e.g., wireline, logging while drilling, coiled tubing for tough logging conditions, or logging tools).

The computing system can filter the multi-well database to select training wells in any suitable sequence. In some examples, the computing system filters the multi-well database to select training wells first based on the location proximity, second based on the stratigraphic zonation, and third based on the one or more operational settings. That is, the computing system select training wells based on stratigraphic zonation among training wells selected based on the location proximity, and then select training wells based on the one or more operational settings among the selected training wells based on the stratigraphic zonation.

After the computing system selects the training wells for the target well by filtering the multi-well database at step304, the computing system performs relevancy testing and ranking on the selected training wells at step306to determine relevant training wells for the target well.

The computing system first performs relevancy testing by building an artificial intelligence (AI) network for each selected training well. The AI network can be a single layer neural network (NN) that includes one input layer, one hidden layer, and one output layer between input well logs and a target output well log.

FIG.6is a schematic diagram illustrating a single layer NN600. The single layer NN600includes an input layer602, a hidden layer604, and an output layer606. The input layer602includes a number of input nodes configured to receive a number of inputs602-1,602-2, . . . ,602-X, . . . ,602-n, e.g., well log1, well log2, . . . , well log X, . . . , well log n, where n is an integer larger than 1. The hidden layer604includes a number of hidden nodes604-1,604-2, . . . ,604-mthat each are configured to receive the number of inputs602-1,602-2, . . . ,602-nand to output a respective output, where m is an integer smaller than n. The output layer606is configured to receive the respective outputs from the hidden nodes604-1,604-2, . . . ,604-min the hidden layer604and to output an output log608. A user can train the single layer NN600by choosing input well logs and a target output well log. The input well logs and the target output well log can be logs of the training well that can include logs of bulk density, resistivity, velocity, gamma ray, deep induction, neutron porosity, and/or density porosity.

After a single layer NN for a training well is built, the computing system can apply the single layer NN to the target well to predict a target well log of the target well. Well logs of the target well are inputted into the single layer NN for the training well. The well logs of the target well inputted into the single layer NN can be the same types of well logs of the training well, e.g.,602-1,602-2,602-nofFIG.6, for building the single layer NN. The single layer NN can output, based on the inputted well logs of the target well, an output log as the predicted target well log of the target well.

Then, the computing system calculates a correlation coefficient (R2) between the predicted target well log of the target well obtained by using the single layer NN of the training well and a measured target well log of the target well.FIG.7shows an example700of calculating a correlation coefficient between a predicted log and a measured log. The predicted log and the measured log form a correlation plot702, from which the correlation coefficient R2is calculated as a slope of a line704corresponding to the correlation plot702. The correlation coefficient can be used to represent a relevancy level between the training well and the target well.

After relevancy levels (e.g., correlation coefficients) between each of the selected training wells and the target well are obtained at step306, the computing system further selects training wells based on relevancy levels into a training set for the target well at step308.

In some embodiments, the computing system applies a relevancy cutoff (e.g., a relevancy threshold) to the relevance levels of the selected training wells. The relevancy cutoff can be predetermined, e.g., by a user or based on empirical data. If a training well has a relevancy level greater than or equal to the relevancy cutoff, the training well can be selected into the training set for the target well by the computing system. If a training well has a relevancy level smaller than the relevancy cutoff, the training well is disregarded or not selected into the training set by the computing system at step308.

FIG.8illustrates an example of selecting training wells for a target well based on relevancy levels. Wells, e.g., Well No. 1, Well No. 2, Well No. 3, Well No. 4, Well No. 5, . . . , Well No. m, . . . , Well No. n, are ranked according to relevancy levels (e.g., correlation coefficients). A relevancy cutoff, e.g., predetermined by a user, is set to select training wells among the n wells. For example, the relevancy cutoff is set to be 0.78. As Well No. 1, Well No. 2, Well No. 3, and Well No. 4 have relevancy levels, e.g.,0.95,0.90,0.85,0.80, greater than the relevancy cutoff, the four wells can be selected into a training set for the target well. The remaining wells, e.g., Well No. 5 to Well No. n, have relevancy levels, e.g., 0.5, 0.45, or 0.35, smaller than the relevancy cutoff and thus are not selected into the training set.

After the training wells are selected based on relevancy levels into the training set at step308, the computing system can further perform a coverage analysis at step310to estimate if the training set is large enough to represent the target well, for example, training an AI network (e.g., the AI network102ofFIG.1) for predicting reservoir properties of the target well.

In some embodiments, the coverage analysis is performed by evaluating a specified norm of distance between each well log sample in the target well and corresponding well log samples (or neighbors) in the training wells.

Given samples X in a well log, the samples X can be transformed to a representative vector {tilde over (X)}=rep(X). In some cases, the samples X can be transformed to be the original input, e.g., {tilde over (X)}=rep(X)=X. In some cases, the samples X can be transformed to a latent space, e.g., by a neutral network model or any known transformation. For example, {tilde over (X)}=rep(X)=Fourier[X].

Given a norm∥·∥ on, a point of samples representation {tilde over (X)} in training wells, and a point of samples representation {tilde over (Y)} in the target well, a distance d(X,Y) between {tilde over (X)} and {tilde over (Y)} is evaluated by

The distances d(X,Y) can be evaluated for all pairs of samples X in the training well and samples Y in the target well.

For any sample Y in the target well, a non-linear function can be used to constrain neighboring distances, e.g., a sigmoid function. A cutoff criterion6can be used. For example, only the nearest k neighbors are included in the distance evaluation, and the farther neighbors are discarded.

Then the coverage C of the training set is evaluated by averaging through all sample pairs (X,Y) in the training wells and the target well, as follows:

The computing system can determine whether the training set for the target well satisfies a coverage criteria based on the determined coverage C.

If the determined coverage C does not exceed the threshold y, the computing system can increase the number of training wells in the training set, e.g., by adjusting the threshold y, adjusting the relevancy cutoff at step308, adjusting filtering thresholds at step304, or updating well data in the multi-well database, or any suitable combination thereof.

If the determined coverage C exceed a threshold y, e.g., defined by a user, the computing system can determine that the training set satisfies the coverage criteria, and the training set can be provided to train the AI network. In some embodiments, the computing system further optimizes the training set for the target well at step312before providing the training set for training the AI network. For example, well log data of training wells in the training set can be filtered with a linear smoothing filter to remove outliers and artifacts in the data. The filter window size may either be chosen manually or be a fixed percentage of the overall data length. The training set may also be visually inspected for erroneous artifacts. The well log data can be categorized and can be reduced further if desired. The optimized training set can be used as the training set106ofFIG.1to be used for training the AI network102ofFIG.1.

FIG.9is a flowchart of an example process of managing training wells for a target well in machine learning. The process900can be performed by a computing system. The computing system can be the computing system for performing the process300ofFIG.3. The computing system can include one or more computing devices and one or more machine-readable repositories or databases that are in communication with each other. The process900can be implemented as the process step105ofFIG.1or the process300ofFIG.3. The target well can be a well of interest or a well to be drilled, e.g., the target well410ofFIG.4, in a field, e.g., the field402ofFIG.4.

At step910, the computing system performs a series of steps912,914,916for each training well of a plurality of training wells. The target well and the plurality of training wells can be within a same reservoir in the field.

In some embodiments, the computing system obtains the plurality of training wells by filtering a multi-well database storing well data of multiple wells, e.g., step304ofFIG.3. The well data of the multiple wells can include at least one of well attributes, well logs, or core data in one or more fields where the multiple wells are located. The one or more fields can include the field where the target well is located. The multi-well database can be maintained in a local computing device of the computing system or a remote server. The multi-well database can be updated, e.g., dynamically or periodically.

In some embodiments, the computing system filters the multi-well database based on at least one of factors including: location proximity, stratigraphic zonation, and operational settings. The computing system can filter the multi-well database based on the factors in a sequential order, e.g., first based on the location proximity, then the stratigraphic zonation, and finally on the operational settings.

In some embodiments, the computing system filters the multi-well database by selecting wells located within a predetermined proximity of the target well in a field, e.g., as illustrated inFIG.4. The predetermined proximity can be defined by a user or based on empirical data.

In some embodiments, the computing system filters the multi-well database by selecting wells for the target well based on stratigraphic zonation, e.g., as illustrated inFIG.5. The selected wells can have a common set of geological properties with the target well in multiple zones of the field.

In some embodiments, the computing system filters the multi-well database by selecting wells for the target well based on one or more operational settings. The one or more operational settings can include well type, drilling mud properties, and logging survey types.

With reference to step912, for each training well of a plurality of training wells, the computing system builds a training network for the training well based on well log data of the training well. In some embodiments, the training network includes a single layer neural network (NN), e.g., the single layer NN600ofFIG.6. The single layer NN can have an input layer (e.g., the input layer602ofFIG.6), a hidden layer (e.g., the hidden layer604ofFIG.6), and an output layer (e.g., the output layer606ofFIG.6).

In some embodiments, the computing system builds the training network for the training well based on the well log data of the training well by training the training network using multiple input well logs of the training well as input parameters and an output well log of the training well as an output parameter. The well log data of the training well including the multiple input well logs and the output well log. In some examples, the multiple input well logs of the training well include two or more of a list of well logs comprising permeability, porosity, oil saturation, water saturation, lithology, matrix density, and clay content. In some examples, the output well log of the training well includes at least one of a list of well logs comprising bulk density, resistivity, velocity, gamma ray, deep induction, neutron porosity, and density porosity.

With reference to step914, the computing system predicts a target well log of the target well using the training network built for the training well. In some embodiments, the computing system provides multiple well logs of the target well as inputs of the training network and obtains an output of the respective training network based on the multiple well logs of the target well as the predicted target well log of the target well. The multiple well logs of the target well correspond to the multiple input well logs of the training well.

With reference to step916, the computing system determines a relevancy level between the training well and the target well based on the predicted target well log of the target well and a measured target well log of the target well. In some embodiments, the computing system calculates a correlation coefficient between the predicted target well log of the target well and the measured target well log of the target well, e.g., as illustrated inFIG.7, and determines the calculated correlation coefficient to be the relevancy level between the training well and the target well.

With reference to step920, the computing system selects relevant training wells among the plurality of training wells based on the relevancy levels associated with the plurality of training wells. In some embodiments, e.g., as illustrated inFIG.8, the computing system compares the relevancy levels associated with the plurality of training wells with a predetermined relevancy threshold (e.g., the relevancy cutoff ofFIG.8), and selecting, among the plurality of training wells, training wells having corresponding relevancy levels greater than or equal to the predetermined relevancy threshold to be the relevant training wells for the target well. The computing system can disregard training wells having corresponding relevancy levels smaller than the predetermined relevancy threshold.

After step920, the computing system can further determine whether the selected relevant training wells satisfy a coverage criteria for the target well. In some embodiments, the computing system obtains, for each of a plurality of pairs of well log samples in the target well and the selected relevant training wells, a specified norm of distance between a well log sample in the target well and corresponding well log samples in the selected relevant training wells, averaging the specified norms of distance of the plurality of pairs of well log samples in the target well and the selected relevant training wells to obtain an average coverage, and determining whether the average coverage exceeds a predetermined coverage threshold.

In some embodiments, in response to determining that the selected relevant training well fails to satisfy the coverage criteria, the computing system can the computing system can increase the number of training wells in the training set, e.g., by adjusting the predetermined coverage threshold, adjusting the relevancy threshold, adjusting filtering thresholds, or updating well data in the multi-well database, or any suitable combination thereof.

In some embodiments, in response to determining that the selected relevant training wells satisfies the coverage criteria for the target well, providing the selected relevant training wells as a training set of an artificial intelligence (AI) network, e.g., the AI network102ofFIG.1. The AI network is configured to be trained using the training set based on at least one machine learning algorithm that can be more complicated than the training network built at step912.

In some embodiments, after the training set is determined, the AI network is trained with well data of the training wells in the training set. The well data can include multiple reservoir parameters jointly as input parameters of the AI network and one or more well logs as output parameters of the AI network. The multiple reservoir parameters can include two or more of a list of parameters including permeability, porosity, oil saturation, water saturation, lithology, matrix density, and clay content. The one or more well logs can include logs of bulk density, resistivity, velocity, gamma ray, deep induction, neutron porosity, and/or density porosity.

In some embodiments, the AI network is an artificial neural network. The AI network can be an integrated network including a capsule convolutional neural network and a deep belief neural network that are interconnected with each other. The deep belief neural network can be configured to process the well data of the training wells by performing temporal and spatial information normalization on the well data of the training wells.

In some embodiments, each of the input parameters can be assigned with a respective weight. The respective weights can be customized or determined based on experience data or initialized, e.g., randomly and/or automatically, at the beginning of the training. The respective weights can be continuously updated during the training process. For example, the capsule convolutional neural network can include a plurality of capsules independent from each other and can estimate jointly the multiple well logs of the training wells using the plurality of capsules. The deep belief neural network can reconstruct capsule output data of the plurality of capsules for estimating the one or more well logs of the training wells. Then the estimated well logs of the training wells can be compared with the one or more well logs of the training wells. The AI network, e.g., the respective weights of the input parameters, can be adjusted for optimization based on a result of the comparison.

Well data of the target well can include depth and geological information and initial reservoir parameters. The initial reservoir parameters can include one or more of a list of parameters including permeability, porosity, oil saturation, water saturation, lithology, matrix density, and clay content. In some examples, the initial reservoir parameters of the target well can be determined based on the reservoir parameters of the training wells and a geological relationship between the training wells and the target well.

In some embodiments, the well data of the target well is processed to be conformed with the well data of the training wells. Processing the well data of the target well can include performing temporal and spatial information normalization on the well data of the target well, e.g., by using the deep belief neural network in the AI network. One or more well logs of the target well can be estimated by utilizing the trained AI network with the well data of the target well. The estimated multiple well logs of the target well can be reconciled with each other.

The estimated well logs of the target well can include one or more of a list of well logs including logs of bulk density, resistivity, velocity, gamma ray, deep induction, neutron porosity, and density porosity. The estimated well logs of the target well can be reconciled with the well logs of the training wells and geographic formation associated with the target well and the training wells. One or more of the estimated well logs of the target well can be selected for actual measurement. In some embodiments, new reservoir parameters of the target well can be obtained based on the estimated well logs of the target well and evaluating hydrocarbon properties of the target well based on the new reservoir parameters of the target well and/or the estimated well logs of the target well.

In some embodiments, any or all of the components of the computing system, both hardware and software, may interface with each other or the interface using an application programming interface (API) or a service layer. The API may include specifications for routines, data structures, and object classes. The API may be either computer language-independent or -dependent and refer to a complete interface, a single function, or even a set of APIs. The service layer provides software services to the computing system. The functionality of the various components of the computing system may be accessible for all service consumers via this service layer. Software services provide reusable, defined business functionalities through a defined interface. For example, the interface may be software written in any suitable language providing data in any suitable format. The API and service layer may be an integral or a stand-alone component in relation to other components of the computing system. Moreover, any or all parts of the service layer may be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of this specification.

Accordingly, the earlier provided description of example implementations does not define or constrain this specification. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of this specification.