SYSTEMS AND METHODS FOR CHARACTERIZING HYDROCARBON-CONTAINING FLUIDS

A system includes processing circuitry and a non-transitory, computer-readable medium that includes instructions that cause processing circuitry to receive logging data regarding a fluid. The logging data is indicative of a plurality of isotope ratios of a plurality of alkanes of the fluid. The instructions also cause the processing circuitry to determine, based on at least a first isotope ratio of the plurality of isotope ratios of the logging data corresponding to a first alkane of the plurality of alkanes, a thermal maturity of the fluid. Additionally, the instructions cause the processing circuitry to determine, based on at least a second isotope ratio of the plurality of isotope ratios corresponding to a second alkane of the plurality of alkanes, a gas-oil ratio (GOR) of the fluid. Furthermore, the instructions cause the processing circuitry to cause display of the thermal maturity of the fluid and the GOR of the fluid.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of European Patent Application No. 23306003.7, titled “SYSTEMS AND METHODS FOR CHARACTERIZING HYDROCARBON-CONTAINING FLUIDS,” filed Jun. 23, 2023, the entire disclosure of which is hereby incorporated herein by reference.

BACKGROUND

The present disclosure relates generally to techniques for analyzing logging data, such as advanced mud gas logging data to generate data or determinations regarding fluid type, fluid properties, and fluid alterations of petrochemical samples.

During the drilling process of an oil well or of a well of another effluent—in particular gas, vapor or water—drilling mud may be brought to the surface. An analysis may be performed on the mud gas to generate data regarding properties of gas in the formation, for example, at various depths of the formation. However, such analyses may be inaccurate, for not taking into account conditions or other considerations that may alter the results of such analyses, such as potential fluid alterations, fluid maturity, and mixing. Therefore, it is desirable to have an improved method to analyze hydrocarbon-containing fluids such as mud gas.

SUMMARY

Certain embodiments of the present disclosure include a system that may include processing circuitry and a non-transitory, computer-readable medium that includes instructions that cause processing circuitry to receive logging data regarding a fluid. The logging data is indicative of a plurality of isotope ratios of a plurality of alkanes of the fluid. The instructions may also cause the processing circuitry to determine, based on at least a first isotope ratio of the plurality of isotope ratios of the logging data corresponding to a first alkane of the plurality of alkanes, a thermal maturity of the fluid. Additionally, the instructions may cause the processing circuitry to determine, based on at least a second isotope ratio of the plurality of isotope ratios corresponding to a second alkane of the plurality of alkanes, a gas-oil ratio (GOR) of the fluid. Furthermore, the instructions may cause the processing circuitry to cause display of the thermal maturity of the fluid and the GOR of the fluid.

Certain embodiments of the present disclosure include a non-transitory, computer-readable medium that may include instructions that cause processing circuitry to receive logging data regarding a fluid. The logging data is indicative of a plurality of isotope ratios of a plurality of alkanes of the fluid. Also, the logging data includes a plurality of amounts. Each respective amount of the plurality of amounts corresponds to a respective alkane of the plurality of alkanes. The instructions also cause the processing circuitry to determine, based on at least a first isotope ratio of the plurality of isotope ratios of the logging data corresponding to a first alkane of the plurality of alkanes, a thermal maturity of the fluid. Additionally, the instructions cause the processing circuitry to determine, based on at least a second isotope ratio of the plurality of isotope ratios corresponding to a second alkane of the plurality of alkanes, a gas-oil ratio (GOR) of the fluid. Furthermore, the instructions cause the processing circuitry to determine a gas wetness ratio of the fluid based on at least a portion of the plurality of amounts, determine a fluid typing of the fluid based on the gas wetness ratio and the first isotope ratio, and cause display of the thermal maturity of the fluid, the fluid typing of the fluid, and the GOR of the fluid.

Certain embodiments of the present disclosure include a computer-implemented method that may include receiving, via processing circuitry, logging data regarding a fluid. The logging data is indicative of a plurality of isotope ratios of a plurality of alkanes of the fluid. The computer-implemented method may also include determining, via the processing circuitry and based on at least a first isotope ratio of the plurality of isotope ratios of the logging data corresponding to a first alkane of the plurality of alkanes, a thermal maturity of the fluid. Additionally, the computer-implemented method may include determining, via the processing circuitry and based on at least a second isotope ratio of the plurality of isotope ratios corresponding to a second alkane of the plurality of alkanes, a gas-oil ratio (GOR) of the fluid. Furthermore, the computer-implemented method may include causing, via the processing circuitry, display of the thermal maturity of the fluid and the GOR of the fluid.

DETAILED DESCRIPTION

As used herein, the terms “connect,” “connection,” “connected,” “in connection with,” and “connecting” are used to mean “in direct connection with” or “in connection with via one or more elements”; and the term “set” is used to mean “one element” or “more than one element.” Further, the terms “couple,” “coupling,” “coupled,” “coupled together,” and “coupled with” are used to mean “directly coupled together” or “coupled together via one or more elements.”

In addition, as used herein, the terms “real-time”, “real-time”, or “substantially real-time” may be used interchangeably and are intended to described operations (e.g., computing operations) that are performed without any human-perceivable interruption between operations. For example, data relating to the systems described herein may be collected, transmitted, and/or used in control computations in “substantially real-time”, such that data readings, data transfers, and/or data processing steps may occur once every second, once every 0.1 second, once every 0.01 second, or even more frequent, during operations of the systems (e.g., while the systems are operating). In addition, as used herein, the terms “automatic” and “automated” are intended to describe operations that are performed are caused to be performed, for example, solely by analysis system without human intervention.

The present disclosure relates to the characterization of fluid or gas (e.g., mud gas) samples. In particular, the presently described techniques enable the automated conversion of advanced mud gas logging data into information about fluid type, fluid properties, and fluid alterations. Specifically, δ13C values of gaseous alkanes can be utilized to determine thermal maturity (e.g., scaled to vitrinite reflectance equivalent (VRE)) and gas-oil ratio (GOR).

In stable isotope geochemistry, stable carbon isotopes may be expressed as ratio of13C to12C in an analyte with respect to international reference material. For example, such an isotope ratio (e.g., a δ13C value) for methane (C1) can be expressed as δ13C-C1. δ13C values of gaseous alkanes may be used in interpretation of fluid maturity and fluid typing and fluid alterations. Expected ranges of δ13C values in nature typically range from −80 to −20‰ or permille for methane, −45 to −20‰ for ethane (C2), and for −45 to 0‰ for propane (C3). While in general, the δ13C values increase with increasing thermal maturity, alteration processes can impact values. For example, biodegradation generates biogenic methane, which has negative values, typically below −60‰, while thermogenic methane covers range of approximately −55 to −20‰. Additionally, biodegradation may cause increases in δ13C of residual non-biodegraded propane. In many cases, in sedimentary basins, there is background biogenic methane from early sedimentary stage. At a later stage, thermogenic hydrocarbons may incorporate or mix with the biogenic methane. Hence, isotopic analysis of alkanes (e.g., methane, ethane, and propane) may be useful when determining maturity of charge and alteration scenarios, while analysis of methane alone can be insufficient. More specifically, a mature gas recharge can occur into a reservoir with less mature fluid (e.g., black oil), which may impact gas-based interpretation of the reservoir. As discussed herein, logging of δ13C of alkanes such as methane, ethane, and propane may enable characterization of active hydrocarbon systems, for example, to determine fluid maturity distribution, gradients, and mixing, differentiating thin bed tanks, and predicting fluid properties.

However, mud gas molecular composition, as opposed to isotopic composition, can be vulnerable to mud gas degassing techniques. Specifically, factors for this vulnerability are: 1) mud gas extraction efficiency (depending on mud type, temperature, and density, which control gas solubility and extractability); 2) mud gas recycling in reused drilling mud; and 3) degasser technology. These factors impact molecular gas ratios, as within the C1-C5range (i.e., methane, ethane, propane, butane, and pentane), alkanes have different properties and can be fractionated. As discussed below, isotope logging data may be utilized, for example, as a proxy to determine properties and characteristics of subterranean fluids (e.g., mud gas). While the presently disclosed techniques may be described below as being performed in conjunction with advanced mud gas logging (AMGL), the presently disclosed techniques may be utilized with any isotope logging systems or techniques.

FIG.1is a schematic view of an example implementation of a rotary drilling rig system5. Downhole measurements can be conducted by instruments disposed in a drill collar20. Such measurements may be stored in memory apparatus of the downhole instruments, or may be telemetered to the surface via conventional measuring-while-drilling (MWD) telemetering apparatus and techniques. For that purpose, an MWD tool sub, schematically illustrated as a tool29, may receive signals from instruments of the collar20, and may transmit them via a mud path8of a drill string6for receipt, e.g., ultimately via a pressure sensor14in a stand pipe15and/or to other surface instrumentation7.

The drilling rig system5may include a motor2that may turn a kelly3through the use of a rotary table4. The drill string6may include sections of drill pipe connected end-to-end to the kelly3and may be turned thereby. For example, drill collars and/or tools20,26,28, and29may be attached to the drilling string6. Such collars and tools may collectively form a bottom hole assembly (BHA)50extending from the drill string6to a drilling bit30. As the drill string6and the BHA50turn, the drill bit30can bore a wellbore9. An annulus10is thus defined between the outside of the drill string6(including the BHA50) and the wellbore9through one or more subterranean geological formations32.

A pump11may pump drilling fluid (drilling “mud”) from a source, e.g., from a mud pit13, via a stand pipe15, a revolving injector head17, and the mud path8of the kelly3and the drill string6to the drill bit30. The mud, which may be a water-based or oil-based drilling mud, may lubricate the drill bit30and may carry wellbore cuttings upward to the surface via the annulus10. If desired, the mud may be returned, for example, to the mud pit13or to an appropriate mud regeneration site, where it may be separated from cuttings and the like, degassed, and returned for application again to the drill string6.

Shale shakers52may separate the cuttings from the mud. Once the mud and the cuttings have been separated, the mud and the cutting may be collected and analyzed. Cuttings samples54may be collected manually and analyzed in a mud logging cabin with one or more instruments (such as microscope, X-ray fluorescence (XRF), X-Ray Diffraction (XRD), and the like). Alternatively, the cuttings sample may be collected and analyzed automatically at the well site.

The drilling fluid (e.g., drilling mud), may generally sampled at an outlet of the shakers52by a sampling device56and directed to an extractor58that extracts gas from the drilling mud. The gas is then directed to an analyzer60in order to detect the content of the gas. In other words, the analyzer60may generate logging data (e.g., a data stream) regarding the (mud) gas. The analyzer60may include a Thermal Conductivity Detector (TCD), a Flame Ionization Detector (FID), and/or mass spectrometer. The analyzer may also include a gas chromatograph (GC). The analyzer60may also be equipment capable of generating log data indicative of the isotopes of carbon (and (relative) amounts of the isotopes) in the mud gas. For example, the analyzer60may perform GC-oxidation reactor-isotope ratio mass spectrometry (GC-ox-IRMS). The analyzer60may also include a GC-ox-Hollow Wave Guide-Quantum Cascade (HWG-QC) IR laser (GC-ox-HWG-IR) or any other GC-ox-analyzer system. The analyzer60may also be or include equipment capable of performing advanced mud gas logging (AMGL), which may involve control on extraction thermodynamics and corrections of extraction efficiency and mud gas recycling biases. As such, the analyzer60may be or include any equipment capable of generating isotope logging data regarding gas extracted from the drilling fluid.

It should be noted that the techniques of the present application may be utilized on samples that are otherwise analyzed. For example, isotope lab analysis of mud gas spot samples (e.g., IsoTubes, Vacutainers, gas vials or gas bags) or even bottom hole samples (BHS) solution gas using GC-ox-IRMS may also be employed. Gas molecular composition data can come from any source of alkanes (e.g., C1-C5) at the same or similar frequency, such as, mud logging gas chromatography (GC), or performing gas chromatography on the isotope analyzer gas chain, or lab analysis of spot samples. Accordingly, the presently described techniques may be utilized on logging data regardless of whether the logging data is generated onsite (e.g., above ground or down-hole) or offsite (e.g., in a laboratory setting).

The downhole tool (collar)20may be any type of downhole tool taking measurement, such as an ultrasonic tool, an electromagnetic or resistivity tool, a sampling tool. For example, the ultrasonic tool may include at least one or more sensors45,46, e.g., such as for measuring characteristics of the wellbore9and/or fluid, including pressure, standoff, composition, etc. therein during drilling operations. Such measurements may be conducted while the wellbore9is being drilled and/or with the drill string6and the BHA50in the wellbore9while the drill bit30, the BHA50, and the drill string6are not rotating. Such measurements may be conducted while the drill string6, the BHA50, and the drill bit30are being tripped to and from the bottom of the wellbore9. The measurements (or data based at least partially thereon) may be transmitted to the surface via the MWD telemetry tool29and the internal mud passage8of the drill string6(or the annulus10), or they may be recorded and stored downhole and for retrieval at the surface after the drill string6and BHA50have been removed from the wellbore9.

The sensors45,46may be mounted on stabilizer fins27of the downhole tool20, as depicted inFIG.1, or may be mounted in a cylindrical wall23of the downhole tool20. An electronics module22may contain electronic circuits, microprocessors, memories, and/or the like, operable to control, and/or to receive, process, and/or store data from the sensors45,46, which may be mounted on a sleeve, an inner tube, and/or other section21secured around or within the collar of the downhole tool20. The section21and other components of the BHA50may include a path40by which drilling mud may pass through the interior passage8of the drill string6to the drill bit30.

A portion of the drilling rig system5, such as surface instrumentation7, may include other sensors for measurement parameters at the surface, such as flow, pressure, weight on bit, torque on bit, etc. and verify that the drilling rig system5works properly. As an example, a sensor61may be connected to the pump11to count the number of strokes of the pump, and a sensor62may be present at the kelly3or motor to assess the rotations per minute (RPM) or in the weight and torque on bit.

The surface instrumentation7may also include data processing system64that can encompass one or more, or portions thereof, of the following: control devices and electronics in one or more modules of the BHA50(such as a downhole controller), a remote computer system (not shown), communication equipment, and other equipment. The data processing system may include one or more computer systems or devices and/or may be a distributed computer system. For example, collected data or information may be stored, distributed, communicated to a human wellsite operator, and/or processed locally or remotely.

The data processing system64may, individually or in combination with other system components, also be linked to all or part of the sensors, downhole or at the surface, to process the measurements and may perform the methods and/or processes described below, or portions thereof. For example, the data processing system64may include processor capability for collecting data obtained from the sensors at the surface or downhole. Methods and/or processes within the scope of the present disclosure may be implemented by one or more computer programs that run in a processor located, e.g., in one or more modules of the BHA50and/or surface equipment of the drilling rig system5and/or a remotely located processing system that is communicatively coupled to the BHA50of the drilling rig system5(e.g., via a wired or wireless connection). Such programs may utilize data received from the BHA50via mud-pulse telemetry and/or other telemetry means, and/or may transmit control signals to operative elements of the BHA50. The programs may be stored on a tangible, non-transitory, computer-usable storage medium associated with the one or more processors of the BHA50and/or surface equipment, such as the surface instrumentation7, of the drilling rig system5, or may be stored on an external, tangible, non-transitory, computer-usable storage medium electronically coupled to such processor(s). The storage medium may be one or more known or future-developed storage media, such as a magnetic disk, an optically readable disk, flash memory, or a readable device of another kind, including a remote storage device coupled over a communication link, among other examples. Accordingly, the data processing system64may include processing circuitry (e.g., one or more processors) that is communicatively coupled to one or more non-transitory computer-readable media capable of storing instructions (e.g., in the form of applications or programs) that the processing circuitry may execute. The one or more non-transitory computer-readable media may be included in the data processing system64or communicatively coupled to the processing circuitry of the data processing system. As such, the data processing system64may receive data (e.g., logging data such as isotope logging data) from the analyzer60regarding analyzed mud gas or other gasses, and, utilizing processing circuitry that executes instructions stored on the one or more non-transitory computer-readable media, analyze the logging data to make determinations regarding the mud gas and/or or control one or more components of the drilling rig system5, such as the analyzer60or the BHA50. With that said, it should be noted that the techniques described herein as being performed by the data processing system64may be performed, in other embodiments, by a remotely located computing device or system that receives data, for example, from the analyzer60. Furthermore, it should be noted that the data processing system64may include and/or be communicatively coupled to one or more displays. As such, the data processing system64may also cause data or representations of data to be displayed.

As noted above, the present disclosure relates to the characterization of fluid or gas (e.g., mud gas) samples. In particular, the presently described techniques enable the automated conversion of advanced mud gas logging data into information about fluid type, fluid properties, and fluid alterations. Specifically, δ13C values of gaseous alkanes can be utilized to determine thermal maturity (e.g., scaled to vitrinite reflectance equivalent (VRE)) and gas-oil ratio (GOR).

Continuing with the drawings,FIG.2is a flow diagram of a process100for characterizing subterranean formations and fluids of subterranean formations. The process100may be performed by processing circuitry, such as processing circuitry of the data processing system64, executing instructions stored on a computer-readable medium. However, while operations of the process100are discussed below as performed by the data processing system64, it should be noted that the process100may be performed by processing circuitry that is communicatively coupled to the analyzer60or configured to receive logging data, such as isotope logging data. Additionally, while the operations of the process100are discussed in one order below, it should be noted that, in other embodiments, operations of the process100may be performed in another order and/or omitted. Bearing this in mind, the process100generally includes receiving isotope logging data (process block102) of a sample (e.g., of mud gas), determining a thermal maturity of the sample using δ13C (which may also be written as “d13C”) values of the isotope logging data (process block104), determining a gas-oil ratio (GOR) for the sample based on δ13C-C2(process block106), characterizing the sample based on wetness and δ13C-C1(process block108), determining whether the sample is of purely thermogenic origin (decision block110), and upon determining that the sample is not of purely thermogenic origin, determining a fraction of biogenic methane using δ13C-C1and wetness (process block112) and modifying the GOR using the fraction of added biogenic methane and indicating a magnitude of mixing of the sample (process block114). The process100may also include evaluating biodegradation of the sample (process block116) and indicating a severity of biodegradation (process block118). Additionally, the process100may include evaluating differences between δ13C-C1, δ13C-C2, and δ13C-C3(process block120), determining whether the sample has thermogenic gas recharge (decision block122), and, upon determining the sample has thermogenic gas recharge, determining a thermogenic recharge modifier for the GOR (process block124) and adjusting the GOR for thermogenic gas recharge and indicating a severity of the thermogenic gas recharge (process block126). Additionally, the process100may include indicating the GOR and an associated fluid type (process block128), which may be done after process block106, after process block114, after process block126, upon determining the sample is of purely thermogenic origin (e.g., at decision block110), or upon determining there is no thermogenic gas recharge (e.g., at decision block122)

At process block102, processing circuitry of the data processing system102may receive logging data (which may also be referred to as “log data”) of a sample. For example, the logging data may be generated by the analyzer60regarding mud gas (e.g., after being extracted from drilling mug by the extractor58). More specifically, the logging data may be isotope logging data that includes δ13C values for one or more alkanes. For example, in one embodiment, the δ13C values may include values of δ13C-C1(for methane) and δ13C-C2(for ethane). In another embodiment, the δ13C values may additionally include δ13C-C3values (for propane). In yet another embodiment, the δ13C values may also include values for δ13C-C4(for butane) and δ13C-C5(for pentane). The logging data may also include data indicative of the (relative) amounts of different alkanes in a sample and/or a gas wetness ratio.

At process block104, the data processing system may determine a thermal maturity of the sample using the δ13C values of the logging data receiving at process block102. In particular, the data processing system64may determine a maturity for the sample based on several alkanes, such as methane, ethane, and propane. Thus, multiple thermal maturities may be determined at process block104. Thermal maturity may be reflected by a value (e.g., a percentage) of vitrinite reflectance equivalent (VRE). The data processing system64may determine values of VRE for alkanes based on the δ13C values. More specifically, the data processing system64may determine VRE values by converting δ13C values to CRE values based on alkane-specific polynomial equations. For example,FIG.3is a graph150of δ13C-C1(as indicated by axis152) versus VRE (indicated by axis154). The graph150includes a line156of a polynomial equation for converting δ13C-C1to a VRE for methane. As another example,FIG.4is a graph170of δ13C-C2(as indicated by axis172) versus VRE (indicated by axis174), and the graph170includes a line176of a polynomial equation for converting δ13C-C2to a VRE for ethane. The polynomial equations utilized to convert δ13C values to VRE values may orders of three, four, or five. In other words, the highest exponent included in the equations may be three, four, or five. Additionally, the polynomials equations may be derived from works that describe VRE modeling of δ13C for alkanes, such as “Empirical carbon isotope/maturity relationships for gases from algal kerogens and terrigenous organic matter, based on dry, open-system pyrolysis” by U. Berner and E. Faber published in volume 24, no. 10-11 in pages 947-955 of Organic Geochemistry. Additionally, the polynomial equations may be tuned to cover the full range of natural values of δ13C of alkanes. For instance, in the graph150and for methane, VRE may be calculated from −48‰ to −25‰, which respectively correspond to the lowest measurable vitrinite reflectance (Ro) about 0.3% (e.g., the most immature source rock at the very beginning of generation hydrocarbons) and a high maturity well (e.g., dry gas). For ethane, and as depicted by the line176in the graph170, the polynomial equation may return values of VRE ranging from 0.3% to 2.15% for values of δ13C-C2ranging from −40.5‰ to −25‰. Additionally, for propane, for values of δ13C-C3ranging from 0.3% to 2.25%, VRE values ranging from −36.5‰ to −24.45‰ may be determined.

At process block106, the data processing system64may determine a gas-oil ratio (GOR) for the sample based on the δ13C-C2value of the sample. To determine the GOR, the data processing system64may utilize an equation that converts δ13C-C2values to GOR. For instance,FIG.5is a graph180plotting δ13C-C2(as indicated by axis182) versus GOR (as indicated by axis184) in which line186is representative of an equation for converting δ13C-C2values to GOR values. The equation may be a natural exponential equation (e.g., an equation in which a constant is multiplied by e raised to an exponential value that may include, or be derived from, a δ13C-C2value). In the graph180, the axis184is logarithmic, and various portions188(collectively referring to portions188A-188F) of the line186correspond to fluid types. The portions188may include portion188A (corresponding to dry gas), portion188B (corresponding to wet gas), portion188C (corresponding to condensate), portion188D (corresponding to co-genetic to volatile oil), portion188E (corresponding to co-genetic to peak oil), and portion188F (corresponding to co-genetic to early oil). Accordingly, the data processing system64may determine a GOR for the sample based on a δ13C-C2value of the sample.

Referring back toFIG.2and continuing the discussion of the process100, at process block108, the data processing system64may characterize the sample based on wetness (e.g., gas wetness ratio) and δ13C-C1. The data processing system64may receive the gas wetness ratio as part of the logging data received at process block102or determine the gas wetness ratio from values of the received logging data indicative of the amounts (e.g., in parts per million (PPM)) of different alkanes in the sample. For example, the data processing system64may determine the gas wetness ratio by determining a first sum of the amounts of ethane, propane, butane, and pentane in a sample, determining a second sum of the amounts of methane, ethane, propane, butane, and pentane in the sample, and dividing the first sum by the second sum (and optionally multiplying the quotient by one-hundred to generate a value that is a percentage).

To characterize the sample based on the wetness and δ13C-C1, the data processing system64may determine a range or region (e.g., as defined by a graph such as graph200ofFIG.6into which a given wetness and δ13C-C1falls. In particular,FIG.6is a diagram or graph200plotting wetness (e.g., gas wetness ratio, as indicated by the axis202) versus δ13C-C1(as indicated by axis204). The graph200also includes regions206(collectively referring to regions206A-206I). The regions206, which correspond to fluid types, may include region206A (corresponding to pure biogenic methane), region206B (corresponding to a biogenic-dominated mix), region206C (corresponding to a thermogenic-dominated mix), region206D (corresponding to dry gas), region206E (corresponding to wet gas), region206F (corresponding to condensate), region206G (corresponding to co-genetic to volatile oil), region206H (corresponding to co-genetic to peak oil), and region206I (corresponding to co-genetic to early oil). Some of the regions206may correspond to the portions186of the graph180ofFIG.5. For example, region206D corresponds to portion188A, region206E corresponds to portion188B, region206F corresponds to portion188C, region206G corresponds to portion188D, region206H corresponds to portion188E, and region206I corresponds to portion188F. Thus, the regions206correspond to fluid types. The graph200or data representative or indicative of the graph200may be stored in a computer-readable medium and utilized by the data processing system64to perform the operations of process block206. Indeed, to characterize a sample, the data processing system64may determine into which of the regions206a sample falls based on the gas wetness ratio of the sample and the value of δ13C-C1of the sample.

Returning toFIG.2and the discussion of the process100, at decision block110, the data processing system64may determine whether the sample is of purely thermogenic origin, for example, based on the characterization performed at process block108. More particularly, and referring toFIG.6, the data processing system64may determine the sample is of purely thermogenic origin upon determining that the sample falls in the region206D, the region206E, the region206F, the region206G, the region206H, or the region206I of the graph200. In other words, if a point were added to the graph200based on the gas wetness ratio and the δ13C-C1of the sample, and the point were located in one of the regions206D-206I, the data processing system64may determine the sample is of purely thermogenic origin. Conversely, the data processing system64may determine the sample is not of purely thermogenic origin upon determining the point is located outside of one of the regions206D-206I. For example, if the point were to be located inside of the region206A, the region206B, or the region206C, the data processing system64may determine the sample is not of purely thermogenic origin. As discussed below, the GOR for the sample (e.g., as determined at process block106) may be modified based on determining that the sample is not of purely thermogenic origin.

Returning toFIG.2and the discussion of the process100, if at decision block110the data processing system64determines the sample is not of purely thermogenic origin, at process block112, the data processing system64may determine a fraction of the sample that is biogenic methane based on δ13C-C1as well as the gas wetness ratio value of the sample. To determine the fraction of biogenic methane, the data processing system64may determine a value that is correlated to a distance from a trend line230of the graph200that a point for the sample would be when plotted on the graph200. More specifically, the fraction of biogenic methane may be determined by determining a quotient of a difference divided by a sum in which the difference is determined by subtracting δ13C-C1from a value, and the sum is determined by adding the value to a constant. The constant may be an integer (e.g., a value between fifty and one-hundred, inclusive), and the value may be determined based on the gas wetness ratio of the sample. More specifically, the value may be determined using a second-degree polynomial equation in which a first constant (e.g., a value between 0.001 and 0.01, inclusive) is multiplied is square of the gas wetness ratio to obtain a first value. From the first value, a second value is subtracted to obtain a third value. The second value may be determined by multiplying the gas wetness ratio by a second constant (e.g., a value between zero and one, inclusive). Lastly, a third constant (e.g., a value between ten and fifty, inclusive) is subtracted from the third value to obtain the fraction of biogenic methane.

At process block114, the data processing system64may modify the GOR (e.g., as determined at process block106) based on the fraction of biogenic methane determined at process block112. In one embodiment, to modify the GOR, the data processing system64may multiply the GOR (as determined at process block106) by a value that is the reciprocal of the difference between one and fraction of biogenic methane (determined at process block112). As such, the data processing system64may modify the GOR determined to account for the sample not being purely thermogenic origin.

At process block114, the data processing system64may additionally indicate a magnitude of mixing based on the fraction of the biogenic methane determined at process block112. For example, the data processing system64may cause a visual representation indicative of the degree of mixing in the sample to be displayed. Such a visual representation may be a graph or included in a graph.

Referring back toFIG.2and continuing with the discussion of the process100, at process block116, the data processing system64may evaluate biodegradation of the sample, for example, based on the thermal maturity of the sample (e.g., as determined at process block104). Biodegradation can be a critical alteration of the original petroleum fluid, oil, or gas, leading to overall compositional changes that impact fluid properties, such as viscosity and mobility. Biodegradation also adds its product to the fluid, biogenic methane, which increases GOR. As such, the data processing system64may evaluate biodegradation to determine an extent or severity of biodegradation (if any). In one embodiment, the data processing system64may evaluate biodegradation of the sample by determining a number of conditions present. The conditions may include, for example: 1) a difference between the VRE determined based on δ13C-C3and the VRE determined based on δ13C-C2exceeding a threshold value (e.g., a value between 0 and 0.5, inclusive); 2) the ratio of the quantities of isobutane to butane in the sample (e.g., as indicated in the received logging data) is greater than one; 3) the ratio of the quantities of isopentane to pentane in the sample (e.g., as indicated in the received logging data) is greater than one; 4a) the value of δ13C-C3being less than −49 or 4b) if the value of δ13C-C3is greater than −49, the difference of the VRE determined based on δ13C-C2and VRE determined based on δ13C-C1exceeding a threshold value (which may be the same value as the threshold used in the first condition); 5) the value of δ13C-C3exceeding −22; and 6) a ratio exceeding another threshold value (e.g., between 0 and 0.5, inclusive), in which the ratio is a ratio of a first value (e.g., a ratio of the quantities of ethane to propane in the sample (e.g., as indicated by the received logging data)) to a second value, which may be a ratio of the quantities of ethane to isobutane (e.g., as indicated in the received logging data) in the sample.

At process block118, the data analysis system64may indicate a severity of biodegradation, for example, by causing a graphical representation of the number of conditions discussed above with respect to process block116that are present (and/or an indication as to which conditions are present). For example,FIG.7is a chart250that includes sections252(collectively referring to sections252A-252G). Section252A includes a graph254having axis256(indicative of a reference point, such as a point indicative of when mud gas was analyzed or from where (e.g., a depth) mud gas was collected and analyzed)) and axis258, which is indicative of the VRE determined at the reference point as determined based on δ13C-C1. Various portions of the graph254are indicative of the thermal maturity at various reference points along the axis256(e.g., for which samples were analyzed), as indicated by the various shading or hatching. Section252B includes a graph260having axis256and axis262(indicative of the VRE determined at the reference point as determined based on δ13C-C2) that, like the graph254, is indicative of the thermal maturity at various reference points along the axis256. Somewhat similarly, Section252C includes a graph264having the axis256and axis266(indicative of the VRE determined at the reference point as determined based on δ13C-C3) that, like the graph254, is indicative of the thermal maturity at various reference points along the axis256. Section252D includes a graph268indicative of a thermal maturity for a given reference points along the axis256as based on the sections206of the graph200ofFIG.6. In other words, the graph268is indicative of in which section206of the graph200a point corresponding to a given reference point (along the axis256) would be. Section252E includes a graph270indicative of GOR (as indicated by axis272) for a given reference point (as indicated by the axis256) as well as the thermal maturity for the given reference point. The GOR indicated in the graph270may be the GOR determined at process block110, process block112, or (as discussed below) process block124. Section252F includes a graph274that has the axis256and axis276, which is indicative of severity of biodegradation (which is also indicated by shading or hatching). Section252G includes a graph278that is indicative of thermogenic gas recharge (e.g., mature gas recharge) as indicated along axis280(and by shading or hatching) for particular reference points (indicated along the axis256). Thus, to indicate a severity of biodegradation, in process block118, the data analysis system64may generate and cause the graph274to be displayed. In some embodiments, the operations of process block116may be performed contemporaneously with the operations associated with process block122or process124, which are discussed below.

Returning toFIG.2and the discussion of the process100, at process block120, the data processing system64may evaluate differences between isotope ratios for methane, ethane, and propane, for example, to evaluate thermogenic gas recharge, which may also be referred to as “mature gas recharge.” In many reservoirs, less mature fluid (e.g., hydrocarbons) is followed by a more mature fluid, often gas, as a consequence of increased burial depth and hence temperature of underlying source rocks, due to accumulating sediments in a basin, (e.g., deltaic systems). Such late arriving more thermally mature gas bears “heavier”13C-enriched isotopic signatures. That will cause appearance of higher VRE of lighter gases, more concentrated in mature gas, when mixed with lower maturity fluid, such as black oil-associated gas. As a result, an expected pattern in the mixture is that the VRE determined based on δ13C-C1(VREC1) will be greater than the VRE determined based on δ13C-C2(VREC2), which will be greater than the VRE determined based on δ13C-C1(VREC3). The data processing system64may take into account the amount of excess VRE of all three pairs of C1, C2, and C3(e.g., caused due to thermogenic gas recharge).

Indeed, to evaluate thermogenic gas recharge, at process block120, the data processing system64may determine whether which of the following conditions are present: 1a) a difference between the VRE determined based on δ13C-C1and the VRE determined based on δ13C-C2exceeding or being equal to a first threshold value (e.g., a value between 0 and 0.5, inclusive), which may be indicative of a relatively higher degree of thermogenic gas recharge (compared to 1b); 1b) when the difference determined at 1a is less than the first threshold, the difference exceeding a second threshold that is less than the first threshold value; 2a) a difference between the VRE determined based on δ13C-C1and the VRE determined based on δ13C-C3exceeding or being equal to a first threshold value (e.g., the same threshold value used in 1a), which may be indicative of a relatively higher degree of thermogenic gas recharge (compared to 2b); 2b) when the difference determined at 2a is less than the first threshold, the difference exceeding a second threshold that is less than the first threshold value (e.g., the same second threshold used above in 1b); 3a) a difference between the VRE determined based on δ13C-C2and the VRE determined based on δ13C-C3exceeding or being equal to a first threshold value (e.g., the same threshold value used in 1a), which may be indicative of a relatively higher degree of thermogenic gas recharge (compared to 3b); and 3b) when the difference determined at 3a is less than the first threshold, the difference exceeding a second threshold that is less than the first threshold value (e.g., the same second threshold used above in 1b). Each time the first threshold is exceeded (or equaled) by a determined difference, the data processing system64may assign a degree or severity of mature gas recharge that is greater than (e.g., double) a degree or severity assigned when the first threshold is not exceed (or equaled) but the second threshold is exceeded. When either threshold is exceeded (or, in the case of the first threshold, equaled), the data processing system64may determine there is thermogenic gas recharge.

At decision block122, the data processing system64may determine whether the sample has thermogenic gas recharge based on the differences evaluated in process block120. For example, when the first threshold or the second threshold described in the preceding paragraph is exceeded (or, in the case of the first threshold, equaled), the data processing system64may determine there is thermogenic gas recharge.

If at decision block122the data processing system64determines to adjust the GOR (e.g., based on determining there is thermogenic gas recharge), at process block126, the data processing system64may determine a thermogenic gas recharge modifier to the GOR (e.g., as determined at process block106or at process block114) based on the degree or severity of thermogenic gas recharge. The thermogenic gas recharge modifier may be determined as a quotient of an integer value representative of the determined severity of thermogenic gas recharge divided by an integer representative of a maximum possible severity of thermogenic gas recharge. The integer representative of the maximum possible severity of thermogenic gas recharge, which may occur when the first threshold (described two paragraphs above) is exceeded or equaled by each of the three differences described two paragraphs above.

At process block126, the data processing system64may modify the GOR based on the thermogenic gas recharge modifier. For example, the data processing system64may multiply the determined GOR (e.g., the GOR determined at process block106or the GOR as modified at process block114, if applicable) by a sum of one and the thermogenic gas recharge modifier. As such, the GOR may be modified to account for the degree or severity of thermogenic gas recharge in the sample. Additionally, also at process block126, the data processing system64may indicate the severity of thermogenic gas recharge. More specifically, the data processing system64may cause the degree of thermogenic gas recharge (or a representation thereof) to be displayed, for example, as a value or in the form of the graph278.

At process block128, the data processing system128may indicate the GOR as well as an associated fluid type of the sample. For example, the data processing system64may cause the GOR (or a representation of the GOR (as determined at process block106or as modified at process block114or process block126), which may be included in the graph270) to be displayed, either alone or with an indication of a fluid type of the sample (e.g., a value or the graph268). It should also be noted that the data processing system64may cause the chart250or any portion252thereof to be displayed when performing the operations of process block128. The operations of process block128may be performed after determining the GOR (e.g., at process block106), in response to determining the sample is of purely thermogenic origin (e.g., at decision block110), determining the sample does not have thermogenic gas recharge (e.g., at decision block122), or after modifying the GOR (e.g., at process block114or process block126).

In other embodiments, the process100may include operations additional to those described above. For example, in response to characterizing a sample as being of purely thermogenic origin (or not of purely thermogenic origin), determining there is or may be biodegradation, determining there is or may be thermogenic gas recharge, or any combination thereof, the data processing system64may alter a rate of sampling, cause the position of the BHA50to be altered, cause a rate at which the BHA50(or a portion thereof) traverses the subterranean geological formation32to be altered, or any combination thereof. For instance, in one embodiment, the data processing system64may decrease a rate of sampling of mud gas in response to determining a sample is of purely thermogenic origin or in response to determining the sample is not of purely thermogenic origin. In another embodiment, the data processing system64may increase a rate of sampling of mud gas in response to determining a sample is of purely thermogenic origin or in response to determining the sample is not of purely thermogenic origin. As another example, the data processing system64may decrease a rate of sampling of mud gas in response to determining there is biodegradation and/or in response to determining there is thermogenic gas recharge. As yet another example, the data processing system64may increase a rate of sampling of mud gas in response to determining there is biodegradation and/or in response to determining there is thermogenic gas recharge.

In another embodiment, the data processing system64may cause the position of the BHA50to be raised or lowered, cause a rate at which the BHA50traverses the subterranean geological formation32to be increased or decreased, or both in response to determining a sample is of purely thermogenic origin or in response to determining the sample is not of purely thermogenic origin. In yet another embodiment, the data processing system64may cause the position of the BHA50to be raised or lowered, cause a rate at which the BHA50traverses the subterranean geological formation32to be increased or decreased, or both in response to determining there is biodegradation and/or in response to determining there is thermogenic gas recharge. As yet another example, the data processing system64may cause the position of the BHA50to be raised or lowered, cause a rate at which the BHA50traverses the subterranean geological formation32to be increased or decreased, or both in response to determining there is biodegradation and/or in response to determining there is thermogenic gas recharge.

As discussed below with respect toFIG.8andFIG.9, the techniques of the present application may also enable characterization (e.g., of fluid maturity) using δ13C-C2along with two different ratios as proxies for thermal maturity. In particular,FIG.8is a graph300plotting gas wetness ratio (as indicated by axis302) against δ13C-C2(as indicated by axis304). The graph300also includes regions306(collectively referring to regions306A-306F) corresponding to regions206D-206I of the graph200ofFIG.5. For example, portion306A corresponds to region206D, portion306B corresponds to region206E, portion306C corresponds to region206F, portion306D corresponds to region206G, portion306E corresponds to region206H, and portion306F corresponds to region206I.FIG.9is a graph320plotting gas character (as indicated by axis322) against δ13C-C2(as indicated by axis324). The data analysis system64may determine a value for gas character by dividing a sum of the amounts of butane and pentane in a sample by the amount of propane in the sample. The graph320also includes regions326(collectively referring to regions326A-326F) corresponding to regions206D-206I of the graph200ofFIG.5. For example, portion326A corresponds to region206D, portion326B corresponds to region206E, portion326C corresponds to region206F, portion326D corresponds to region206G, portion326E corresponds to region206H, and portion326F corresponds to region206I. The data processing system64may characterize samples based on in which of the regions306,236data points for the samples are located.

In one embodiment, a system includes processing circuitry and a non-transitory, computer-readable medium including instructions that, when executed by the processing circuitry, cause the processing circuitry to receive logging data regarding a fluid. The logging data is indicative of a plurality of isotope ratios of a plurality of alkanes of the fluid. The instructions, when executed, also cause the processing circuitry to determine a thermal maturity of the fluid, based on at least a first isotope ratio of the plurality of isotope ratios of the logging data corresponding to a first alkane of the plurality of alkanes. Additionally, when executed, the instructions cause the processing circuitry to determine a gas-oil ratio (GOR) of the fluid based on at least a second isotope ratio of the plurality of isotope ratios corresponding to a second alkane of the plurality of alkanes. Moreover, when executed, the instructions cause display of the thermal maturity of the fluid and the GOR of the fluid.

The logging data may include a plurality of amounts, wherein each respective amount of the plurality of amounts corresponds to a respective alkane of the plurality of alkanes. Furthermore, when executed, the instructions may further cause the processing circuitry to determine a gas wetness ratio of the fluid based on at least a portion of the plurality of amounts and determine the thermal maturity of the fluid based on the gas wetness ratio.

The instructions, when executed, may further cause the processing circuitry to determine the GOR based at least on the second isotope ratio and determine whether the fluid is of purely thermogenic origin based on the thermal maturity of the fluid and the gas wetness ratio. When executed, the instructions may also cause the processing circuitry to generate a modified GOR by modifying the GOR based at least on the first isotope ratio and the gas wetness ratio in response to determining the fluid is not of purely thermogenic origin.

When executed, the instructions may additionally cause the processing circuitry to determine a degree of thermogenic gas recharge of the fluid and modify the GOR or the modified GOR based on the degree of the thermogenic gas recharge of the fluid.

The first alkane may be methane, and the second alkane may be ethane. Additionally, the thermal maturity comprises a vitrinite reflectance equivalent (VRE) value. Furthermore, when executed, the instructions may further cause the processing circuitry to cause a rate of sampling of the fluid to be increased or decreased based on the thermal maturity of the fluid. Moreover, the system may include a drilling system that is communicatively coupled to the processing circuitry. When executed, the instructions may cause the processing circuitry to adjust the drilling system based on the thermal maturity of the fluid.

In another embodiment, a non-transitory, computer-readable medium includes instructions that, when executed by processing circuitry, cause the processing circuitry to receive logging data regarding a fluid. The logging data is indicative of a plurality of isotope ratios of a plurality of alkanes of the fluid, and the logging data includes a plurality of amounts. Each respective amount of the plurality of amounts corresponds to a respective alkane of the plurality of alkanes. When executed, the instructions also cause the processing circuitry to determine a thermal maturity of the fluid based on at least a first isotope ratio of the plurality of isotope ratios of the logging data corresponding to a first alkane of the plurality of alkanes. Additionally, the instructions, when executed, cause the processing circuitry to determine a gas-oil ratio (GOR) of the fluid based on at least a second isotope ratio of the plurality of isotope ratios corresponding to a second alkane of the plurality of alkanes. When executed, in the instructions also cause the processing circuitry to determine a gas wetness ratio of the fluid based on at least a portion of the plurality of amounts, determine a fluid typing of the fluid based on the gas wetness ratio and the first isotope ratio, and cause display of the thermal maturity of the fluid, the fluid typing of the fluid, and the GOR of the fluid.

The fluid typing may include one of pure biogenic methane, a biogenic-dominated mix, a thermogenic-dominated mix, dry gas, wet gas, condensate, volatile oil, peak oil, or early oil. Additionally, the thermal maturity of the fluid may include a plurality of vitrinite reflectance equivalent (VRE) values. The plurality of VRE values may include a first VRE determined based on at least on the first isotope ratio, a second VRE determined based on at least on the second isotope ratio, and a third VRE determined based on at least a third isotope ratio of the plurality of isotope ratios corresponding to a third alkane of the plurality of alkanes.

When executed, the instructions may cause the processing circuitry to determine a degree of biodegradation of the fluid based on the logging data, determine a degree of thermogenic gas recharge of the fluid, and modify the GOR based on the degree of the thermogenic gas recharge of the fluid. Additionally, the instructions, when executed may cause the processing circuitry to cause display of a first visual indication of the plurality of VRE values, a second visual indication of the degree of biodegradation, and a third visual indication of the degree of thermogenic gas recharge. When executed, the instructions may cause the processing circuitry to determine the degree of biodegradation of the fluid based on determining whether a difference between the second VRE and the third VRE exceeds a first threshold value, whether a first ratio exceeds a second threshold value (in which the first ratio is a ratio of a first amount of the plurality of amounts corresponding to a fourth alkane of the plurality of alkanes to a second amount of the plurality of amounts corresponding to a fifth alkane of the plurality of alkanes), whether a second ratio exceeds a third threshold value (in which the second ratio is a ratio of a third amount of the plurality of amounts corresponding to a sixth alkane of the plurality of alkanes to a fourth amount of the plurality of amounts corresponding to a seventh alkane of the plurality of alkanes), whether the third isotope ratio is less than a fourth threshold value (or, if the value of the third isotope ratio is greater than the fourth threshold, whether a second difference of the second VRE value and the first VRE value exceeds a fifth threshold value), whether the third isotope ratio exceeds a sixth threshold, whether a third ratio exceeds a seventh threshold value (in which the third ratio is a ratio of a fourth ratio to a fifth ratio, with the fourth ratio being a ratio of a fifth amount of the plurality of amounts corresponding to the second alkane to a sixth amount of the plurality of amounts corresponding to the third alkane, and the fifth ratio being a ratio of the fifth amount to the first amount), or a combination thereof.

In yet another embodiment, a computer-implemented method includes receiving, via processing circuitry, logging data regarding a fluid, wherein the logging data is indicative of a plurality of isotope ratios of a plurality of alkanes of the fluid. The computer-implemented method also includes determining a thermal maturity of the fluid via the processing circuitry and based on at least a first isotope ratio of the plurality of isotope ratios of the logging data corresponding to a first alkane of the plurality of alkanes. Additionally, the computer-implemented method includes determining a gas-oil ratio (GOR) of the fluid via the processing circuitry and based on at least a second isotope ratio of the plurality of isotope ratios corresponding to a second alkane of the plurality of alkanes. Moreover, the computer-implemented method includes causing, via processing circuitry, display of the thermal maturity of the fluid and the GOR of the fluid.

The computer-implemented method may include determining a plurality of vitrinite reflectance equivalent (VRE) values based on the plurality of isotope ratios. The plurality of VRE values includes a first VRE determined based on at least on the first isotope ratio, a second VRE determined based on at least on the second isotope ratio, and a third VRE determined based on at least a third isotope ratio of the plurality of isotope ratios corresponding to a third alkane of the plurality of alkanes. The first alkane may be methane, the second alkane may be ethane, and the third alkane may be propane.

The computer-implemented method may also include causing, via the processing circuitry, a drilling system to be adjusted based on the thermal maturity of the fluid, causing, via the processing circuitry, a rate of sampling of the fluid to be increased or decreased based on the thermal maturity of the fluid, or both.

Accordingly, the techniques disclosed herein enable hydrocarbon-containing fluids, such as mud gas, to be analyzed and characterized, for example, to log subterranean formations or as part of logging subterranean formations. Indeed, as described above, logging data (e.g., advanced mud gas logging data) including data regarding isotope ratios of hydrocarbons may be utilized to determine fluid properties (e.g., fluid maturity, gas-oil ratio), biodegradation, and recharge. Furthermore, although the examples described above are illustrated for wellbores on the land, similar method may be applied to any acquisition configuration.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and/or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principals of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated.