Patent ID: 12234725

DETAILED DESCRIPTION

Illustrative examples of the subject matter claimed below will now be disclosed. In the interest of clarity, not all features of an actual implementation are described in this specification. It will be appreciated that in the development of any such actual implementation, numerous implementation-specific decisions may be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which will vary from one implementation to another. Moreover, it will be appreciated that such a development effort, even if complex and time-consuming, would be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.

Further, as used herein, the article “a” is intended to have its ordinary meaning in the patent arts, namely “one or more.” Herein, the term “about” when applied to a value generally means within the tolerance range of the equipment used to produce the value, or in some examples, means plus or minus 10%, or plus or minus 5%, or plus or minus 1%, unless otherwise expressly specified. Further, herein the term “substantially” as used herein means a majority, or almost all, or all, or an amount with a range of about 51% to about 100%, for example. Moreover, examples herein are intended to be illustrative only and are presented for discussion purposes and not by way of limitation.

The system and method described herein may be used to predict fluid properties in a subterranean formation based at least partially upon mud gas data. More particularly, the system and method may predict and/or estimate reservoir fluid properties from advanced mud gas analysis data, and these properties may be used for well placement and/or geosteering.

FIG.1illustrates a schematic side view of a wellsite100, according to an embodiment. The wellsite100may include a drilling rig102positioned over a subterranean formation. A downhole tool104may extend downward from the drilling rig102and drill a wellbore106in the subterranean formation. As shown, the wellbore106may include a first (e.g., vertical) portion108and a second (e.g., non-vertical) portion110. The second portion110may be referred to as deviated or horizontal. In at least one embodiment, the wellsite100may also include a second wellbore112, which may be or include a pilot wellbore. The second wellbore112may be drilled before, simultaneously with, or after the (first) wellbore106.

The subterranean formation may include a plurality of different layers. In the example shown inFIG.1, the subterranean formation may include a cap rock layer120, a free gas cap122, a gas-oil contact124, an oil layer126, an oil-water contact128, a brine layer130, and a reservoir rock layer132. The downhole tool104may be geosteered along a drilling trajectory105through the cap rock layer120into the oil layer126.

FIG.2illustrates an enlarged schematic side view of the wellsite100including the drilling rig102, the downhole tool104, and the wellbore106, according to an embodiment. Drilling fluid (also referred to as mud or drilling mud)202may be stored in a pit204at the wellsite100. A pump (e.g., a mud pump)206may deliver the drilling fluid202to an interior of a drill string208in the wellbore106, which causes the drilling fluid202to flow downwardly through the drill string208. The drilling fluid202exits the drill string208via ports in a drill bit210of the downhole tool104, and then circulates upwardly through an annulus region between the outside of the drill string208and a wall of the wellbore106. The drilling fluid202may carry formation cuttings and/or formation fluids up to the surface as it is returned to the pit204for recirculation. The formation fluids may include liquid and/or gas. The gas may be referred to as mud gas.

The drilling fluid202(e.g., the mud gas) may be sampled and analyzed by a drilling fluid analyzer214at the surface. The surface mud gas measurements may provide information about the subterranean formation (e.g., the reservoir fluids), which may be used to geosteer the downhole tool104. More particularly, the surface mud gas data may be used to estimate the reservoir fluid properties in the wellbore106in substantially real-time (e.g., within 1 hour or less, 30 minutes or less, 10 minutes or less, etc.). As described below, the surface mud gas data may be used to generate a continuous fluid log along the well trajectory105that can provide actionable insight into fluid variability and help to minimize uncertainties in understanding fluid distribution resulting from lateral heterogeneities that influence production success. The fluid log can be helpful mature fields where migration of fluids due to depletion and advancement of an injected gas front increases complexity in making geosteering decisions.

Advanced Mud Gas Analysis

Mud gas analysis during drilling produces early information about the hydrocarbons (HC) in the reservoir. Variation in gas concentrations in the drilling mud may be controlled by numerous factors and can be broadly attributed to drilling parameters and environment changes, or formation fluid and drilling fluid changes. Due to these limiting factors, conventional methodology was previously focused on establishing qualitative results. Hence, identifying the effect of these variables, applying corrections, and removing artifacts may be used during the quality control process before attempting mud gas interpretation. Reliable and quantitative mud gas data may be used to make real-time decisions about the asset in the early stage of exploration and development.

Advanced mud gas (AMG) analysis can provide accurate gas composition (e.g., lab quality) of the reservoir fluids during drilling. AMG data may be used for HC and fluid contacts identification, as well as inter- and intra-well fluid facies mapping. Gas composition from AMG can guide sampling and downhole fluid analysis, as well as early detection of reservoir complexities (e.g., tight, thin layered, etc).

FIGS.3A and3Billustrate schematic views of a mud gas analysis process, according to an embodiment. More particularly,FIG.3Aillustrates a schematic side view of a circulation path of the drilling fluid (e.g., mud)202between the surface and the downhole environment. The drilling fluid202may flow from the pit204through a hose302, a swivel304, and/or a kelly306down into the drill string208. As mentioned above, the drilling fluid202then flows out of the drill bit210and then circulates upwardly through an annulus region between the outside of the drill string208and the wall of the wellbore106. When a hydrocarbon-bearing section is encountered during drilling, the drilling fluid202contacts and mixes with the reservoir fluid. The drilling fluid202, carrying the cuttings and reservoir fluid (e.g., HC), then flows up the annulus region to the surface. A shaker (e.g., a shale shaker)308at the surface may separate the solids (e.g., cuttings) from the drilling fluid202, and the (now filtered) drilling fluid202may be transferred back to the pit204.

FIG.3Billustrates a schematic view of the drilling fluid analyzer214and results produced therefrom, according to an embodiment. The analyzer214at the surface may sample and analyze the drilling fluid202. The analyzer214may capture one or more first samples of the drilling fluid202that flows up and out of the wellbore106at the flex out310(e.g., before the drilling fluid202is filtered by the shale shaker308). The analyzer214may also or instead capture one or more second samples of the drilling fluid202that is being pumped into the wellbore106at the flex in312(e.g., after the drilling fluid202is filtered by the shale shaker308). The analyzer214may be or include a gas chromatograph and/or a mass spectrometer.

The results produced by the analyzer214may be or include a fluid data interpretation, a facies determination, a fluid composition, or a combination thereof. More particularly, the analyzer214may extract and analyze the gas components associated with HC in the reservoir. The gas may be analyzed to quantify the presence, amount, and/or concentration of compounds such as Methane (C1), Ethane (C2), Propane (C3), n-Butane (nC4), iso-Butane (iC4), n-Pentane (nC5), iso-Pentane (iC5), or a combination thereof. The molar gas composition may be matched with the depth from which the HC originated during drilling. The molar gas composition of C1-C5(in mol %) from AMG may be comparable to the normalized gas composition acquired from fluid analysis laboratory technique.

Fluid Prediction Workflow (FPW)

FIG.4illustrates a schematic view of a fluid prediction workflow (FPW), according to an embodiment. The FPW may use the results from the mud gas analysis (inFIGS.3A and3B) as inputs. The FPW may process these inputs using one or more machine learning (ML) models to estimate fluid type, fluid composition (e.g., C6plus), gas-oil ratio (GOR), or a combination thereof. More particularly, the models may estimate the heavy fractions of the reservoir fluid and other fluid properties from the normalized gas data. For example, the FPW may estimate the relative amounts of heavier molecules present in the reservoir fluid directly during drilling. The heavier molecules may be considered as one pseudo-group, including organic molecules with six carbons (C6), such as hexane and benzene, and/or more (C6plus) in the molecule. This group of the fluid composition may be referred to as C6plus fraction (C6+) hereafter. The C6+ may be contained in the liquid phase of the reservoir fluid. Such real-time estimation of the heavy fractions of the fluid may be useful in making informed drilling decisions and/or in planning sampling points in the vertical portion108of the wellbore106.

The relative concentration of C6+ (in mole %) in a HC sample is defined as:

C⁢6⁢plus[mol⁢%]=number⁢of⁢moles⁢of⁢components⁢in⁢the⁢C⁢6⁢plus⁢fractiontotal⁢number⁢of⁢moles⁢of⁢all⁢components⁢in⁢th⁢e⁢HC×1⁢0⁢0(Eq.1)

The output of the FPW may be a continuous fluid properties log, which may be generated while drilling.FIG.5illustrates a continuous fluid properties log500, according to an embodiment. In an oil-based mud environment where contamination is challenging, this log500can be a valuable reference for future calibration and analysis. The fluid properties log500may include one or more tracks. As shown, the fluid properties log500may include a first track510that shows the composition of the drilling fluid202, a second track520that shows the GOR of the drilling fluid202, a third track530that shows the C6+ of the drilling fluid202, a fourth track540that shows the fluid type(s) of the drilling fluid202, a fifth track550that shows a pressure of the drilling fluid202, a sixth track560that shows a resistivity of the drilling fluid202, a seventh track570that shows flair information, and an eighth track580that shows Methane (C1mole %) in reservoir fluid.

Advanced statistical learning tools can be used to build models to predict relative molecular concentration of fluid properties with a given set of input parameters, such as molar gas composition (C1-C5mole %). Statistical learning refers to a wide range of tools for exploring and understanding data through statistical models. Models may be used for estimating and/or predicting an output based on one or more inputs. A reliable model may be developed by training the model with historical reservoir fluid data. A database containing fluid properties of reservoir fluids can be used to build, train, and validate the statistical models.

The FPW can be used to generate the continuous fluid properties log500(e.g., fluid type, C6+ mole %, GOR) along the well trajectory105. The fluid properties log500may provide actionable insight into fluid variability and help to minimize the uncertainties related to fluid distribution resulting from lateral heterogeneities. The fluid properties log500may depict the normalized C1 mole % from mud gas measurement to indicate that a C1 concentration increased or decreased in a fluid in a section between the points on the well trajectory105. However, in an embodiment, such information alone may not be sufficient to understand the fluid variation. Fluid prediction data may show small fluid variation through the well profile, for example, with an average C6+ of 40 mole % and GOR of 700 scf/stb. By monitoring predicted fluid composition (e.g., C1 to C6+ mole %) and/or GOR, the reservoir fluid may be visualized as becoming heavier in that section. The track540may show the fluid type probability. This track540may show that the fluid is predominantly oil, for example. Trends in fluid properties along the well profile can be used to identify situations when the trajectory105approaches a gas cap122or to identify movement of gas-oil-contacts124. The application of such a FPW can be used to predict C6+ and/or GOR. The application may also or instead be used to interpret the fluid variation during geosteering.

FIG.6illustrates a schematic side view of the wellsite100showing a fluid variation across a fault610, according to an embodiment. The fluid properties log500along with MWD, LWD, and/or other measurements (e.g., measured by the downhole tool104and/or at the surface), can indicate fluid variation across faults or barriers610, which can have an impact on completion at a later stage. For example, it may be helpful to know that the reservoir fluids across the fault610are similar in nature while drilling the horizontal portion110of the wellbore106. The fluid properties log500can be helpful in mature fields where migration of fluids due to depletion and advancement of injected gas front increases complexity in making geosteering decisions.

In some FPWs, the models may be trained on a dataset containing a limited number of reservoir fluid samples. In the training data, the sample properties may vary over a wide range of fluid types. However, reservoir fluid properties are not known a priori while drilling. Hence, in exploration wells, it may be difficult to know ahead of time whether a fluid sample encountered in a new reservoir will have characteristics similar to samples in the training dataset. In cases where the new fluid composition is markedly different from the training data, predicted fluid properties may have a degree of uncertainty.

Geosteering workflows may have access to more reliable fluid data from nearby wellbores and pilot wells112. Such local fluid information can be used to reduce uncertainty in the fluid prediction method using the FPW. The workflow may include the following to update the FPW. First, a fluid prediction may be made from normalized gas data using an existing FPW. The fluid properties may be compared with local fluid data for accuracy. If the predicted properties agree with the local fluid data, the continuous fluid log500(e.g., generated by current FPW) may continue to be used to monitor the reservoir fluid in the wellbore106.

However, if the fluid prediction from the existing FPW is not within a predetermined threshold, the local fluid data may be used to calibrate the fluid predictions. The local fluid data may be used in conjunction with the previously-used training data to train the ML models embedded in the FPW. The number of local fluid samples may be less than the number of samples in the existing database. To increase the influence of the local data in model training, higher weights can be assigned to sparse local data. Prediction accuracy of the retrained models can then be tested. If satisfactory, the FPW can be updated with the new models, and the updated FPW can be used to generate an updated continuous fluid log500for the wellbore106.

FIG.7illustrates a flowchart of a method700for predicting fluid properties in the subterranean formation, according to an embodiment. In one embodiment, the subterranean formation may include fluid composition changes in the target (e.g., oil layer126) due to faults and/or barriers610in the reservoir that create fluid compartmentalization, as shown inFIG.6. The method700may predict and/or estimate reservoir fluid properties from advanced mud gas analysis data, which may be used for well placement and/or geosteering.

An illustrative order of the method700is provided below; however, one or more portions of the method700may be performed in a different order, combined, split into sub-portions, repeated, or omitted without departing from the scope of the disclosure. One or more portions of the method700may be performed by the computing system800described below with respect toFIG.8.

The method700may include measuring one or more measured fluid properties of a mud gas at a surface of a wellbore106, as at702. As mentioned above, the mud gas may be part of the drilling fluid202(e.g., after the drilling fluid202is pumped up and out of the wellbore106). The one or more measured fluid properties may be measured simultaneously with the downhole tool104drilling the wellbore106in the subterranean formation. The one or more measured fluid properties may include a fluid type, a fluid composition, a gas-oil ratio (GOR), or a combination thereof.

The method700may also include comparing the one or more measured fluid properties with one or more corresponding measured fluid properties that are measured in another (e.g., pilot) wellbore112, as at704. The wellbore112may be drilled before, simultaneously with, or after the wellbore106. The wellbore112may be drilled within 1000 m, within 500 m, within 100 m, or within 50 m of the wellbore106.

The method700may also include converting the one or more measured fluid properties, the one or more corresponding measured fluid properties, or a combination thereof into a converted data format, as at706. This may be done to match the data format of additional data described below.

The method700may also include measuring one or more mud gas properties of the mud gas at the surface of the wellbore106, as at708. The one or more mud gas properties may be measured simultaneously with the downhole tool104drilling the wellbore106in the subterranean formation. The one or more mud gas properties may also or instead be measured simultaneously with the measuring of the one or more measured fluid properties. The one or more mud gas properties may include a composition of molecules in the mud gas, a pressure of the mud gas, a temperature of the mud gas, or a combination thereof.

The method700may also include normalizing the one or more mud gas properties to produce one or more normalized mud gas properties, as at710.

The method700may also include predicting one or more first predicted fluid properties, as at712. The one or more predicted fluid properties may be predicted using one or more pre-trained machine-learning (ML) models. The one or more first predicted fluid properties may be determined based at least partially upon the one or more (e.g., normalized) mud gas properties. The one or more first predicted fluid properties may include the fluid type, the fluid composition, the GOR, or a combination thereof. The one or more pre-trained ML models may include a first pre-trained ML model for the fluid type, a second pre-trained ML model for the fluid composition, a third pre-trained ML model for the GOR, or a combination thereof.

The method700may also include comparing the one or more measured fluid properties (from702) to the one or more first predicted fluid properties (from712), as at714. The one or more measured fluid properties may be in the converted data format to match the format of the one or more first predicted fluid properties.

The method700may also include re-training the one or more pre-trained ML models to produce one or more re-trained ML models, as at716. The re-training may be in response to the comparison (at714). More particularly, the re-training may be in response to the comparison being greater than a first predetermined threshold. In an example, the first predetermined threshold may be a difference between the fluid types (at714) being greater than a fluid type threshold, a difference between the fluid compositions (at714) being greater than a fluid composition threshold, a difference between GORs (at714) being greater than a GOR threshold, or a combination thereof. The re-training may be performed using a first portion (e.g., 80%) of a combined dataset. The combined dataset may include the one or more measured fluid properties (from702) (e.g., in the converted data format) and/or one or more training fluid properties that were used to train the one or more pre-trained ML models.

The method700may also include testing the one or more re-trained ML models, as at718. The testing may be performed using a second portion (e.g., 20%) of the combined dataset. In an example, testing the one or more re-trained models may include generating one or more outputs using the one or more re-trained ML models based upon one or more inputs. The one or more inputs may include the second portion of the combined data. Testing may also include comparing the one or more outputs with a ground truth of the second portion of the combined dataset.

The method700may also include predicting one or more second predicted fluid properties, as at720. The one or more second predicted fluid properties may include the fluid type, the fluid composition, the GOR, or a combination thereof. The one or more second predicted fluid properties may be predicted using the one or more re-trained ML models. The one or more second predicted fluid properties may be predicted in response to the comparison of the one or more outputs with the ground truth being within a second predetermined threshold. The second predetermined threshold may be different than the first predetermined threshold.

The method700may also include comparing the one or more measured fluid properties (from702) to the one or more second predicted fluid properties (from720), as at722. The one or more measured fluid properties may be the converted data format to match the format of the one or more second predicted fluid properties.

The method700may also include determining a geosteering response for the downhole tool104, as at724. The geosteering response may be determined based at least partially upon the comparison (at722). The geosteering response may include modifying the drilling trajectory105of the downhole tool104based at least partially upon the comparison being greater than or less than the first predetermined threshold (from716).

The method700may also include geosteering the downhole tool104, as at726. The downhole tool104may be geosteered in response to the comparison (from722), the geosteering response (from724), or a combination thereof. In one embodiment, geosteering the downhole tool104may also or instead include generating a signal (e.g., using the computing system800) and/or transmitting the signal to the downhole tool104. The signal may instruct the downhole tool104to maintain or modify the drilling trajectory105(e.g., to target and/or stay within the oil layer126).

In some embodiments, the methods of the present disclosure may be executed by a computing system.FIG.8illustrates an example of such a computing system800, in accordance with some embodiments. The computing system800may include a computer or computer system801A, which may be an individual computer system801A or an arrangement of distributed computer systems. The computer system801A includes one or more analysis modules802that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module802executes independently, or in coordination with, one or more processors804, which is (or are) connected to one or more storage media806. The processor(s)804is (or are) also connected to a network interface807to allow the computer system801A to communicate over a data network809with one or more additional computer systems and/or computing systems, such as801B,801C, and/or801D (note that computer systems801B,801C and/or801D may or may not share the same architecture as computer system801A, and may be located in different physical locations, e.g., computer systems801A and801B may be located in a processing facility, while in communication with one or more computer systems such as801C and/or801D that are located in one or more data centers, and/or located in varying countries on different continents).

A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

The storage media806may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment ofFIG.8storage media806is depicted as within computer system801A, in some embodiments, storage media806may be distributed within and/or across multiple internal and/or external enclosures of computing system801A and/or additional computing systems. Storage media806may 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 may be provided on one computer-readable or machine-readable storage medium, or may 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). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

In some embodiments, computing system800contains one or more fluid property module(s)808configured to perform at least a portion of the method700. It should be appreciated that computing system800is merely one example of a computing system, and that computing system800may have more or fewer components than shown, may combine additional components not depicted in the example embodiment ofFIG.8, and/or computing system800may have a different configuration or arrangement of the components depicted inFIG.8. The various components shown inFIG.8may 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.

Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general-purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are included within the scope of the present disclosure.

Computational interpretations, models, and/or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system800,FIG.8), 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.

As used herein, the terms “inner” and “outer”; “up” and “down”; “upper” and “lower”; “upward” and “downward”; “upstream” and “downstream”; “above” and “below”; “inward” and “outward”; and other like terms as used herein refer to relative positions to one another and are not intended to denote a particular direction or spatial orientation. The terms “couple,” “coupled,” “connect,” “connection,” “connected,” “in connection with,” and “connecting” refer to “in direct connection with” or “in connection with via one or more intermediate elements or members.”

The foregoing description, for purposes of explanation, used specific nomenclature to provide a thorough understanding of the disclosure. However, it will be apparent to one skilled in the art that the specific details are not required in order to practice the systems and methods described herein. The foregoing descriptions of specific examples are presented for purposes of illustration and description. They are not intended to be exhaustive of or to limit this disclosure to the precise forms described. Many modifications and variations are possible in view of the above teachings. The examples are shown and described in order to best explain the principles of this disclosure and practical applications, to thereby enable others skilled in the art to best utilize this disclosure and various examples with various modifications as are suited to the particular use contemplated. It is intended that the scope of this disclosure be defined by the claims and their equivalents below.