Patent Description:
In the oil and gas industry, well logs are collected using logging equipment, either during or after drilling, to determine characteristics of the formation around the well. Well logs can include gamma ray logs, resistivity logs, neutron logs, density logs, porosity logs, and others.

However, well logs may be incomplete, e.g., due to sensor failure and/or human error. Recently, machine learning models have been used to automatically reconstruct the missing elements of the well logs. Generally, the machine learning models are trained using a corpus of labeled high-quality well logs from a specific area, which the machine learning models use to establish predictive links between input, incomplete well logs and output or "reconstructed" well logs, in which the incomplete data is filled in.

However, acquisition of such labeled high-quality well log training data can present challenges. For example, the section for high-quality well logs are determined manually by a human operator, generally a domain expert, which can be expensive and time-consuming. Further, due to difference in underground geochemical properties, the labeled high-quality well log may not be of general applicability, that is, labels for well logs from one field may not be useful to train machine learning models for reconstructing well logs for another field. Thus, in relatively new fields, there may not be any or may not be a sufficient amount of labeled training data available. <NPL>, discloses a a method for estimating synthetic Vp and Vs transit-time curves for well logs based on Gamma ray, Neutron-porosity, Density, DtP and DtS logs. A neural network is trained to perform the estimation, and the well logs that have known transit time curves are used as the training data set. Before training, outliers and null-data are removed form well log data used for training. <NPL>, discloses a method for estimating synthetic well logs of one data type based on existing well logs of other data types. Training is performed with a set of well logs comprising both input and output data types, and anomalous data in the training set have been detected and removed before training. DEBJANI <NPL>), discloses a method for estimation and reconstruction of missing and noisy well log data. Training set consists of possible values of data within ranges that are realistic for the surveyed area. After the training, the autoencoder is tested on real well log data. <NPL>" refers to a method for estimation of permeability values based on well log data comprising different measured parameters. Input data used for training is well log data, and output data comprises permeability values measured on the core of the same wells. During the training, a large training error indicates the existence of outlier data. Outliers are removed and the training is resumed with the remaining data.

The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and/or" as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms "includes," "including," "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, as used herein, the term "if" may be construed to mean "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context.

The aspects of the invention refer to a method as defined in claim <NUM>, a computer-readable medium as defined in claim <NUM>, and a computing system as defined in claim <NUM>. Some operations in the processing procedures, methods, techniques and workflows disclosed herein may be combined and/or the order of some operations may be changed, as long as they don't depart from the scope of the invention as defined in claims <NUM>, <NUM> and <NUM>.

<FIG> illustrate simplified, schematic views of oilfield <NUM> having subterranean formation <NUM> containing reservoir <NUM> therein in accordance with implementations of various technologies and techniques described herein. <FIG> illustrates a survey operation being performed by a survey tool, such as seismic truck <NUM>, to measure properties of the subterranean formation. The survey operation is a seismic survey operation for producing sound vibrations. In <FIG>, one such sound vibration, e.g., sound vibration <NUM> generated by source <NUM>, reflects off horizons <NUM> in earth formation <NUM>. A set of sound vibrations is received by sensors, such as geophone-receivers <NUM>, situated on the earth's surface. The data received <NUM> is provided as input data to a computer <NUM> of a seismic truck <NUM>, and responsive to the input data, computer <NUM> generates seismic data output <NUM>. This seismic data output may be stored, transmitted or further processed as desired, for example, by data reduction.

<FIG> illustrates a drilling operation being performed by drilling tools <NUM> suspended by rig <NUM> and advanced into subterranean formations <NUM> to form wellbore <NUM>. Mud pit <NUM> is used to draw drilling mud into the drilling tools via flow line <NUM> for circulating drilling mud down through the drilling tools, then up wellbore <NUM> and back to the surface. The drilling mud is typically filtered and returned to the mud pit. A circulating system may be used for storing, controlling, or filtering the flowing drilling mud. The drilling tools are advanced into subterranean formations <NUM> to reach reservoir <NUM>. Each well may target one or more reservoirs. The drilling tools are adapted for measuring downhole properties using logging while drilling tools. The logging while drilling tools may also be adapted for taking core sample <NUM> as shown.

Typically, the wellbore is drilled according to a drilling plan that is established prior to drilling. The drilling plan typically sets forth equipment, pressures, trajectories and/or other parameters that define the drilling process for the wellsite. The drilling operation may then be performed according to the drilling plan. However, as information is gathered, the drilling operation may need to deviate from the drilling plan. Additionally, as drilling or other operations are performed, the subsurface conditions may change. The earth model may also need adjustment as new information is collected.

Surface unit <NUM> may include transceiver <NUM> to allow communications between surface unit <NUM> and various portions of the oilfield <NUM> or other locations. Surface unit <NUM> may also be provided with or functionally connected to one or more controllers (not shown) for actuating mechanisms at oilfield <NUM>. Surface unit <NUM> may then send command signals to oilfield <NUM> in response to data received. Surface unit <NUM> may receive commands via transceiver <NUM> or may itself execute commands to the controller. A processor may be provided to analyze the data (locally or remotely), make the decisions and/or actuate the controller. In this manner, oilfield <NUM> may be selectively adjusted based on the data collected. This technique may be used to optimize (or improve) portions of the field operation, such as controlling drilling, weight on bit, pump rates, or other parameters. These adjustments may be made automatically based on computer protocol, and/or manually by an operator. In some cases, well plans may be adjusted to select optimum (or improved) operating conditions, or to avoid problems.

As shown, the sensor (S) may be positioned in production tool <NUM> or associated equipment, such as Christmas tree <NUM>, gathering network <NUM>, surface facility <NUM>, and/or the production facility, to measure fluid parameters, such as fluid composition, flow rates, pressures, temperatures, and/or other parameters of the production operation.

Data plots <NUM>-<NUM> are examples of static data plots that may be generated by data acquisition tools <NUM>-<NUM>, respectively; however, it should be understood that data plots <NUM>-<NUM> may also be data plots that are updated in real time. These measurements may be analyzed to better define the properties of the formation(s) and/or determine the accuracy of the measurements and/or for checking for errors. The plots of each of the respective measurements may be aligned and scaled for comparison and verification of the properties.

Static data plot <NUM> is a seismic two-way response over a period of time. Static plot <NUM> is core sample data measured from a core sample of the formation <NUM>. The core sample may be used to provide data, such as a graph of the density, porosity, permeability, or some other physical property of the core sample over the length of the core. Tests for density and viscosity may be performed on the fluids in the core at varying pressures and temperatures. Static data plot <NUM> is a logging trace that typically provides a resistivity or other measurement of the formation at various depths.

A production decline curve or graph <NUM> is a dynamic data plot of the fluid flow rate over time. The production decline curve typically provides the production rate as a function of time. As the fluid flows through the wellbore, measurements are taken of fluid properties, such as flow rates, pressures, composition, etc..

While a specific subterranean formation with specific geological structures is depicted, it will be appreciated that oilfield <NUM> may contain a variety of geological structures and/or formations, sometimes having extreme complexity. In some locations, typically below the water line, fluid may occupy pore spaces of the formations. Each of the measurement devices may be used to measure properties of the formations and/or its geological features. While each acquisition tool is shown as being in specific locations in oilfield <NUM>, it will be appreciated that one or more types of measurement may be taken at one or more locations across one or more fields or other locations for comparison and/or analysis.

The data collected from various sources, such as the data acquisition tools of <FIG>, may then be processed and/or evaluated. Typically, seismic data displayed in static data plot <NUM> from data acquisition tool <NUM> is used by a geophysicist to determine characteristics of the subterranean formations and features. The core data shown in static plot <NUM> and/or log data from well log <NUM> are typically used by a geologist to determine various characteristics of the subterranean formation. The production data from graph <NUM> is typically used by the reservoir engineer to determine fluid flow reservoir characteristics. The data analyzed by the geologist, geophysicist and the reservoir engineer may be analyzed using modeling techniques.

<FIG> illustrates an oilfield <NUM> for performing production operations in accordance with implementations of various technologies and techniques described herein. As shown, the oilfield has a plurality of wellsites <NUM> operatively connected to central processing facility <NUM>. The oilfield configuration of <FIG> is not intended to limit the scope of the oilfield application system. Part, or all, of the oilfield may be on land and/or sea. Also, while a single oilfield with a single processing facility and a plurality of wellsites is depicted, any combination of one or more oilfields, one or more processing facilities and one or more wellsites may be present.

Attention is now directed to <FIG>, which illustrates a side view of a marine-based survey <NUM> of a subterranean subsurface <NUM> in accordance with one or more implementations of various techniques described herein. Subsurface <NUM> includes seafloor surface <NUM>. Seismic sources <NUM> may include marine sources such as vibroseis or airguns, which may propagate seismic waves <NUM> (e.g., energy signals) into the Earth over an extended period of time or at a nearly instantaneous energy provided by impulsive sources. The seismic waves may be propagated by marine sources as a frequency sweep signal. For example, marine sources of the vibroseis type may initially emit a seismic wave at a low frequency (e.g., <NUM>) and increase the seismic wave to a high frequency (e.g., <NUM>-<NUM>) over time.

The component(s) of the seismic waves <NUM> may be reflected and converted by seafloor surface <NUM> (i.e., reflector), and seismic wave reflections <NUM> may be received by a plurality of seismic receivers <NUM>. Seismic receivers <NUM> may be disposed on a plurality of streamers (i.e., streamer array <NUM>). The seismic receivers <NUM> may generate electrical signals representative of the received seismic wave reflections <NUM>. The electrical signals may be embedded with information regarding the subsurface <NUM> and captured as a record of seismic data.

In one implementation, seismic wave reflections <NUM> may travel upward and reach the water/air interface at the water surface <NUM>, a portion of reflections <NUM> may then reflect downward again (i.e., sea-surface ghost waves <NUM>) and be received by the plurality of seismic receivers <NUM>. The sea-surface ghost waves <NUM> may be referred to as surface multiples. The point on the water surface <NUM> at which the wave is reflected downward is generally referred to as the downward reflection point.

The electrical signals may be transmitted to a vessel <NUM> via transmission cables, wireless communication or the like. The vessel <NUM> may then transmit the electrical signals to a data processing center. Alternatively, the vessel <NUM> may include an onboard computer capable of processing the electrical signals (i.e., seismic data). Those skilled in the art having the benefit of this disclosure will appreciate that this illustration is highly idealized. For instance, surveys may be of formations deep beneath the surface. The formations may typically include multiple reflectors, some of which may include dipping events, and may generate multiple reflections (including wave conversion) for receipt by the seismic receivers <NUM>. In one implementation, the seismic data may be processed to generate a seismic image of the subsurface <NUM>.

Marine seismic acquisition systems tow each streamer in streamer array <NUM> at the same depth (e.g., <NUM>-<NUM>). However, marine based survey <NUM> may tow each streamer in streamer array <NUM> at different depths such that seismic data may be acquired and processed in a manner that avoids the effects of destructive interference due to sea-surface ghost waves. For instance, marine-based survey <NUM> of <FIG> illustrates eight streamers towed by vessel <NUM> at eight different depths. The depth of each streamer may be controlled and maintained using the birds disposed on each streamer.

Embodiments of the disclosure may provide systems and methods for automating well log quality control analysis and log reconstruction processes, contributing to the competency and efficiency in management of high-quality field data. In some embodiments, the systems and methods may employ a workflow that takes advantage of well logs being correlated and exploits this correlation via one or more neural networks, e.g., autoencoders. The autoencoder(s) may be particularly useful in data denoising and reconstruction.

The workflow <NUM>, illustrated in <FIG>, begins by acquiring well log data which include a plurality of well logs, as at <NUM>. The well logs may be a priori unlabeled, or may be partially labeled, or entirely labeled. In this context, "labeled" means that a human user has reviewed the well log and indicated incomplete or erroneous sections of the data, which may provide the basis for supervised learning, as a machine learning model may make predictions based on the human user's conclusions. Because the workflow <NUM> may not rely on supervised learning, however, the workflow <NUM> may operate whether or not the well logs are labeled. This may avoid time-consuming and potentially non-repeatable (e.g., subjective, prone to error and mislabeling) human-user interaction with the raw well log data. The workflow <NUM> proceeds through two general stages: an outlier detection stage 403A and log reconstruction stage 403B.

In the outlier detection stage 403A, the sections of the well for which the well log data is substantially complete are identified, as at <NUM>. By way of example, a measurement may be conducted between depths of <NUM> feet and <NUM> feet. Between depth <NUM> feet and <NUM> feet, the density log is filled with -<NUM>, or otherwise indicates that values are missing. These may be incomplete sections; in contrast, those sections not filled with -<NUM> may be identified as complete. A variety of other ways to identify missing well logs or segments thereof may also be used.

"Complete" sections may thus be those sections with zero missing logs. "Substantially complete" sections are those with few missing sections, such that the data is sufficient to employ these sections in further analyses. In some embodiments, the workflow <NUM> identifies those sections that are entirely complete (no missing logs) or those that have, for example, fewer than a threshold number of missing entries. For example, well log data may include a combination of logs (e.g., two or more types of logs selected from the group of density, neutron, gamma, resistivity, and acoustic Δt (sonic)). A larger number of different types of logs for a same depth interval generally corresponds to greater accuracy, although some types of logs are considered more descriptive than others (e.g., a density log). Accordingly, if one or more two types of logs are missing for a depth interval, but the density log is present, the section may still be considered substantially complete, as results may still be achieved using the input. At some number of missing logs (e.g., three), however, the missing logs may be considered incomplete, even if the density log is present.

These identified well sections with substantially complete (or entirely complete) data are extracted, as at <NUM> and used to train an outlier detection neural network, e.g., a first or "outlier detection" autoencoder, as at <NUM>. Once trained, the outlier detection autoencoder is configured to detect outliers in the remaining (substantially) complete well log data. In general, outliers are defined as sections of a well log that deviate from neighboring sections to a degree that would generally indicate a spurious or erroneous signal or data point. As will be described below, this determination may be made statistically. Outliers are removed from the data that is used to train a second autoencoder (as described below). Removing the outliers may serve to increase the accuracy of the second autoencoder by avoiding training using anomalous or otherwise unrepresentative training data. The result of the outlier detection stage thus is "estimated" well logs that exclude incomplete well log sections and outlier well log sections.

<FIG> illustrates an example of a neural network/autoencoder, and reference thereto may assist in a more complete understanding of the outlier detection and log reconstruction stages 403A, 403B. The neural network may have an input layer, which may receive well logs (e.g., gamma ray, neutron, resistivity, etc.). A trained neural network may then apply one or more series of layers, with weights in the connections that have been updated through back-propagation to minimize predictive error. The result may be an output layer, which may provide the estimation of well log data.

Accordingly, an autoencoder may be trained to learn the internal correlation between input logs. Thus, a cost function for the autoencoder may be set, which measures the difference between the estimated logs and the original logs. A back-propagation process is then used to minimize the cost function during the training process. Back-propagation may include calculating a gradient of the cost function and updating the weights of the autoencoder in the direction to reduce the cost function. After several hundred epochs of training, the cost function may reach a stable state and may not further decrease.

In some embodiments, there may be a risk that the autoencoder is simply mapping the input to the output layer, and the internal correlation is not learned. To avoid this situation, the bottleneck layer may be narrower that the input layer. By squeezing the bottleneck, the autoencoder breaks the identical mapping relation and is forced to learn the internal correlation. With back-propagation and a narrower bottleneck layer, the autoencoder is trained to learn the correlation between well logs.

Returning to <FIG>, the workflow <NUM> proceeds to the log reconstruction stage 403B. This stage 403B is conducted by a second autoencoder, also referred to herein as a "log reconstruction" or "reconstruction" autoencoder. The second autoencoder is trained, as at <NUM>, using the result of the outlier detection stage 403A, namely, the estimated well logs, e.g., the input well logs from which the incomplete sections and outlier section shave been extracted. Next, the incomplete well logs received at <NUM> (and extracted from consideration during the initial, outlier-detection stage 403A) are introduced into the trained second autoencoder and reconstructed. For example, individual logs may be treated as including missing data and may be reconstructed by the second autoencoder.

The autoencoder-based log reconstruction workflow is thus an unsupervised modeling workflow, which may avoid the drawbacks inherent to supervised modeling discussed above. That is, for example, the workflow may not use labeled data when reconstructing the log. This workflow can be applied to a wide range of log dataset and improving the efficiency in log quality control analysis and log reconstruction.

<FIG> illustrates a flowchart of another workflow <NUM> for well log reconstruction, according to the invention.

The workflow <NUM> includes the worksteps <NUM>, <NUM>, <NUM>, and <NUM>, as shown in <FIG>, e.g., provided as part of the outlier detection stage 403A. In addition, the outlier stage 403A includes generating estimated well logs using the trained outlier detection autoencoder and the well log data, as at <NUM>. The estimated well logs are produced based on the original well log data, e.g., by the outlier detection autoencoder attempting to complete or replace the extracted incomplete sections.

The workflow <NUM> then includes determining estimated errors between the original well logs and the estimated well logs, as at <NUM>, e.g., by comparing the estimated data to the original data. The estimated errors may be stored as a histogram, as shown in <FIG>, for example. From the estimated errors, an outlier error threshold may be determined. In some embodiments, the outlier error threshold may be determined as the largest value between a hard-cutoff (e.g., static and/or predetermined) and soft (e.g., dynamic) percentile value. In some embodiments, the hard-cutoff value is a heuristic value from experience. The soft percentile value is determined by the error distribution in the well log training data. For example, soft percentile value may be the value corresponding to 90th, 95th, or 98th percentile of the estimated error distribution. The workflow <NUM> may then use the outlier error threshold to identify outlier sections, as at <NUM>, which are those sections that differ from the original by greater than a threshold amount.

In some embodiments, an uncertainty analysis may be conducted on the results of the outlier detection stage 403A, e.g., the well logs excluding the incomplete sections and the identified outliers. For example, a Monte Carlo dropout method may be implemented for the uncertainty analysis. During Monte Carlo dropout, a random number of neural nodes may be deactivated, and then one model output sample may be generated. After repeating the Monte Carlo dropout multiple times, a series of output samples are generated. The confidence interval of the results can be calculated as [µ - <NUM> * σ, µ + <NUM> * σ], where µ is the mean, and σ is the standard deviation.

The workflow <NUM> then proceeds to the implementation/reconstruction stage 403B. As with the similar stage 403B of <FIG>, this stage 403B of <FIG> includes training the second, reconstruction autoencoder to reconstruct well logs based on the results of the outlier detection stage 403A (e.g., the well logs with incomplete and outlier sections removed), as at <NUM>. Thereafter, second autoencoder reconstructs the well logs, as at <NUM>. In at least some embodiments, to "reconstruct" a well log means to predict the information that would have been included in the incomplete section of the well log, if the well log were not incomplete, and then add the predicted portions into the well log. In some embodiments, an uncertainty analysis may be conducted on the results of the reconstruction stage 403B, in either of workflows <NUM> and/or <NUM>, i.e., the reconstructed well logs. Such uncertainty analysis may be performed using a Monte Carlo dropout technique, as discussed above.

Accordingly, the present disclosure provides an integrated, autoencoder-based, log data outlier detection workflow, which may facilitate an unsupervised training and implementation of a reconstruction autoencoder (or any other type of neural network). As such, the present embodiment provides data outlier detection and log reconstruction, e.g., at the same time. This workflow can be applied to a wide range of log datasets and may improve the efficiency in log quality control and log reconstruction. As a result, well logs that are reconstructed may be displayed or otherwise communicated (visualized) to a user. Well logs have many practical applications in many different petrotechnical workflows, such as, among others, determining location, techniques, and otherwise informing hydrocarbon extraction processes such as exploration, drilling, treatment, intervention, production, etc. Accordingly, the present disclosure provides a more accurate, reconstruction of well logs which can ameliorate the effects of gaps in the well log data. Moreover, this amelioration workflow may be orders of magnitude faster than previous attempts, even with the same processing power, taking processing times down from days to minutes.

The method of the present invention is executed by a computing system. <FIG> illustrates an example of such a computing system <NUM>, in accordance with some embodiments. The computing system <NUM> may include a computer or computer system 801A, which may be an individual computer system 801A or an arrangement of distributed computer systems. The computer system 801A includes one or more analysis module(s) <NUM> configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module <NUM> executes independently, or in coordination with, one or more processors <NUM>, which is (or are) connected to one or more storage media <NUM>. The processor(s) <NUM> is (or are) also connected to a network interface <NUM> to allow the computer system 801A to communicate over a data network <NUM> with one or more additional computer systems and/or computing systems, such as 801B, 801C, and/or 801D (note that computer systems 801B, 801C and/or 801D may or may not share the same architecture as computer system 801A, and may be located in different physical locations, e.g., computer systems 801A and 801B may be located in a processing facility, while in communication with one or more computer systems such as 801C and/or 801D that are located in one or more data centers, and/or located in varying countries on different continents).

The storage media <NUM> can be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of <FIG> storage media <NUM> is depicted as within computer system 801A, in some embodiments, storage media <NUM> may be distributed within and/or across multiple internal and/or external enclosures of computing system 801A and/or additional computing systems. Storage media <NUM> may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above can be provided on one computer-readable or machine-readable storage medium, or alternatively, can be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). The storage medium or media can be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions can be downloaded over a network for execution.

In some embodiments, computing system <NUM> contains one or more neural network module(s) <NUM>. In the example of computing system <NUM>, computer system 801A includes the neural network module <NUM>. In some embodiments, a neural network module may be used to perform some or all aspects of one or more embodiments of the methods. In alternate embodiments, a plurality of neural network modules may be used to perform some or all aspects of methods.

It should be appreciated that computing system <NUM> is only one example of a computing system, and that computing system <NUM> may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of <FIG>, and/or computing system <NUM> may have a different configuration or arrangement of the components depicted in <FIG>. The various components shown in <FIG> may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits.

Geologic interpretations, models and/or other interpretation aids may be refined in an iterative fashion; this concept is applicable to embodiments of the present methods discussed herein. This can include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system <NUM>, <FIG>), and/or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

Claim 1:
A computer-implemented method comprising:
receiving (<NUM>) well log data comprising a plurality of well logs;
identifying (<NUM>) one or more sections of one or more well logs of the plurality of well logs that have substantially complete data;
further comprising extracting (<NUM>) one or more incomplete sections of the one or more incomplete well logs of the plurality of well logs, prior to training (<NUM>) a reconstruction neural network, such that the reconstruction neural network is not trained using the one or more incomplete sections;
training (<NUM>) the reconstruction neural network to reconstruct incomplete well logs based on the one or more sections of the one or more well logs that have substantially complete data;
detecting (<NUM>) one or more outlier sections of the one or more well logs that have substantially complete data;
wherein detecting the one or more outlier sections comprises:
training (<NUM>) an outlier detection neural network using the one or more sections that have substantially complete data;
generating (<NUM>) one or more estimated well logs based on original well log data using the outlier detection neural network; and
identifying (<NUM>) the one or more outlier sections at least partially by comparing the one or more estimated well logs with the original well log data; and
extracting the one or more outlier sections prior to training (<NUM>) the reconstruction neural network, such that the reconstruction neural network is not trained using the one or more outlier sections; and
reconstructing (<NUM>) one or more incomplete well logs of the plurality of well logs using the reconstruction neural network.