Method and system for grading reactivity for kinetic partitioning in basin modeling

Abstract

A method of grading thermal reactivity for kinetic allocation in basin modeling can include obtaining information related to various hydrocarbon source rock samples; and determining thermal reactivity of the hydrocarbon source rock corresponding to the various hydrocarbon source rock samples. The hydrocarbon source rock is at the same thermal maturity level in the area of interest. The method can include grading thermal reactivity at different thermal maturity. The method includes comparing published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of the hydrocarbon source rock in the area of interest. The method can include classifying kinetic parameters in the organic phase of the hydrocarbon source rock layer according to reactivity and maturity. The method can include allocating kinetic parameters derived from immature hydrocarbon source rock units in the hydrocarbon source rock layer in a sedimentary basin to mature hydrocarbon source rock units. The method can include evaluating reactivity to improve selection and allocation of kinetic parameters in basin modeling.

Description

Background Technology

[0001] Kinetic parameters are inputs in basin modeling to determine the initiation and rate of hydrocarbon generation, as well as the depth or temperature of the hydrocarbon generation window. Kinetic parameters describing chemical reaction rates include activation energy and frequency factors. For this purpose, most petroleum system modeling assumes that hydrocarbon generation can be described by a series of parallel first-order kinetics, where the rate of each reaction depends only on the concentration of one reactant. In basin models, the generation of different subsets of the parent material is stimulated individually (i.e., each subset has a different set of frequency factors and activation energy distributions). Summary of the Invention

[0002] In general, in one aspect, the embodiments disclosed in this specification relate to a method for classifying thermal reactivity for kinetic allocation in basin modeling. The method includes obtaining information associated with various source rock samples (e.g., multiple source rock samples). The method includes determining the thermal reactivity of source rocks corresponding to the various source rock samples. Within the region of interest, the source rocks are at the same level of thermal maturity. The method includes classifying thermal reactivity at different thermal maturity levels. The method includes comparing published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of source rocks in the region of interest. The method includes classifying kinetic parameters in the organic phase of source rock layers according to reactivity and maturity. The method includes assigning kinetic parameters derived from immature source rock units in source rock layers of a sedimentary basin to mature source rock units. The method includes evaluating reactivity to improve the selection and allocation of kinetic parameters in basin modeling.

[0003] In general, in one aspect, the embodiments disclosed in this specification relate to a system for classifying thermal reactivity for kinetic allocation in basin modeling. The system includes a receiver that receives information associated with various source rock samples (e.g., multiple source rock samples). The system includes a processor that determines the thermal reactivity of source rocks corresponding to the various source rock samples. Within the region of interest, the source rocks are at the same level of thermal maturity. The processor classifies thermal reactivity at different thermal maturity levels. The method includes comparing published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of source rocks in the region of interest. The processor classifies kinetic parameters in the organic phase of the source rock layers based on reactivity and maturity. The processor assigns kinetic parameters derived from immature source rock units in the source rock layers of a sedimentary basin to mature source rock units. The processor evaluates reactivity to improve the selection and allocation of kinetic parameters in basin modeling.

[0004] In general, in one aspect, the embodiments disclosed in this specification relate to a non-transitory computer-readable medium storing instructions executable by a computer processor. The instructions include functionality for obtaining information related to various source rock samples (e.g., multiple source rock samples). The instructions include functionality for determining the thermal reactivity of source rocks corresponding to the various source rock samples. In the region of interest, the source rocks are at the same level of thermal maturity. The instructions include functionality for classifying thermal reactivity at different thermal maturity levels. The method includes comparing published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of source rocks in the region of interest. The instructions include functionality for classifying kinetic parameters in the organic phase of source rock layers according to reactivity and maturity. The instructions include functionality for assigning kinetic parameters derived from immature source rock units in a sedimentary basin to mature source rock units. The instructions include functionality for evaluating reactivity to improve the selection and assignment of kinetic parameters in basin modeling.

[0005] Other aspects of this disclosure will become apparent from the following description and the appended claims. Attached Figure Description

[0006] Specific embodiments of the disclosed technology will now be described in detail with reference to the accompanying drawings. For consistency, similar elements are indicated by similar reference numerals in the drawings.

[0007] Figure 1 A schematic diagram showing a cross-sectional view illustrating a source rock sampling process according to one or more embodiments is presented.

[0008] Figure 2 A schematic diagram of a collection tool according to one or more embodiments is shown.

[0009] Figure 3 A process for sampling source rocks according to one or more embodiments is illustrated.

[0010] Figure 4 A graph is shown illustrating the determination of the kinetic parameters Ea and A of the Arrhenius equation using measured reaction rate k and temperature according to one or more embodiments.

[0011] Figure 5 A schematic diagram of a system according to one or more embodiments is shown.

[0012] Figure 6 A schematic diagram of a system according to one or more embodiments is shown.

[0013] Figure 7A A graph showing the reaction rates in pyrolysis experiments using different heating rates according to one or more embodiments is presented.

[0014] Figure 7B A graph is shown illustrating a set of dynamic parameters according to one or more embodiments.

[0015] Figure 7C A graph showing the conversion curves generated by applying kinetic parameters in a hypothetical thermal history according to one or more embodiments is shown.

[0016] Figure 8 A graph is shown that includes one or more dynamic parameters that have been reprocessed according to one or more embodiments.

[0017] Figures 9 to 10B A graph showing source rock pyrolysis and kinetic data according to one or more embodiments is presented.

[0018] Figures 11A to 11D A diagram showing examples of common dynamic parameters according to one or more embodiments is provided.

[0019] Figure 12A A graph showing the conversion curves generated by applying kinetic parameters in a hypothetical thermal history according to one or more embodiments is shown.

[0020] Figure 12B A graph showing the conversion curves generated by applying kinetic parameters in a hypothetical thermal history according to one or more embodiments is shown.

[0021] Figure 13 A graph is shown showing kinetic parameters including those obtained by reprocessing according to one or more embodiments of a method for evaluating the reactivity of source rocks.

[0022] Figure 14 and Figure 15 A graph showing the type of oil source rock and conversion curves tested using a method according to one or more embodiments is presented.

[0023] Figure 16 A graph is shown showing the kinetic parameters of a source rock tested using a method according to one or more embodiments, after being reprocessed by the method.

[0024] Figure 17 A flowchart according to one or more embodiments is shown.

[0025] Figure 18 A flowchart according to one or more embodiments is shown.

[0026] Figure 19 A schematic diagram of a system according to one or more embodiments is shown. Detailed Implementation

[0027] Specific embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. For consistency, similar elements are indicated by similar reference numerals in the drawings.

[0028] Numerous specific details are set forth in the following detailed description of embodiments of the present disclosure in order to provide a more thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.

[0029] Throughout the application, ordinal numbers (e.g., first, second, third, etc.) may be used as adjectives for elements (i.e., any noun in this application). Unless explicitly disclosed, such as by using the terms “before,” “after,” “single,” and other such terms, the use of ordinal numbers does not imply or create any particular order of elements, nor does it limit any element to a single element. Rather, the use of ordinal numbers is intended to distinguish between elements. As an example, a first element is distinct from a second element, and a first element may contain more than one element and be placed after (or before) the second element in the order of elements.

[0030] In general, embodiments of this disclosure include methods and systems for evaluating reactivity in source rock assessment. In some embodiments, the methods and systems provide simplified formats for kinetic parameters and graphical methods for evaluating the reactivity of source rocks. In some embodiments, the methods and systems facilitate the use of kinetic parameters as single variables (e.g., reactivity) for the assessment and characterization of source rocks. To this end, the methods and systems provide a novel approach to improve the selection and assignment of kinetic parameters in basin modeling.

[0031] In one or more embodiments, the source rock kinetics underlying the implementation of the method and system include the Arrhenius equation and kinetic parameters, the derivation of kinetic parameters from pyrolysis experiments, and the use of kinetic parameters in basin modeling and source rock evaluation. To this end, the method and system are based on the principles of converting parent material into petroleum in source rocks, which are considered as a series of irreversible reactions controlled by first-order chemical kinetics. These chemical kinetics can be described by the Arrhenius equation shown in Equation (1). Equation (1) determines the rate of conversion of parent material to hydrocarbons under thermal stress during the burial history of the source rock and illustrates the effects of temperature and time on petroleum formation. The kinetic parameters (activation energy Ea and frequency factor A) are key inputs to the source rock in basin modeling, used to quantify petroleum formation, retention, and discharge, and to determine the timing of any relevant processes.

[0032] k = Ae - E a / RT (1)

[0033] In equation (1), k is the reaction rate, which describes the change in the molar mass of the reactants with respect to time; A is the frequency factor (i.e., the pre-exponential factor), which describes the number of potential elementary reactions per unit time; Ea is the activation energy, which is defined as the energy barrier that must be overcome for the reaction to occur; R is the gas constant, which is equal to 8.31447 (i.e., in Ws / mol / K); and T is the reaction temperature (i.e., in Kelvin or K).

[0034] In one or more embodiments, the thermal reactivity of the source rock (i.e., the thermal stability of the source rock) is a key variable in determining the degree of source rock transformation and the timing of hydrocarbon generation in geological history. In some embodiments, reactivity is compared with other parameters such as TOC (i.e., quantity), source rock type (i.e., mass), and T... max Or vitrinite reflectance (i.e., maturity) should be considered together to fully characterize the source rock.

[0035] Advantageously, the methods and systems described in this specification provide a streamlined solution for evaluating the reactivity of source rocks. These methods and systems allow for direct comparison and interpretation of commonly used kinetic parameter formats. In this context, the reactivity of source rocks may not need to be evaluated in kinetic analysis or basin modeling software by a geochemist or basin modeler expected to be familiar with kinetic analysis and maturity modeling. These methods and systems expand the use of reactivity in source rock evaluation and shale resource assessment by preventing limitations imposed by specialized knowledge and / or access to appropriate software.

[0036] Furthermore, the methods and systems described in this specification improve basin modeling practices by addressing the heterogeneity of source rocks (i.e., the kinetics of source rocks may vary longitudinally and laterally within them) and by providing well-founded methods for assigning measured kinetic parameters derived from immature source rock units in sedimentary basins to mature source rock units.

[0037] In one or more embodiments, the method and system include a weighted average of Ea (i.e., also known as WA-Ea) to simplify the format of the discretely distributed Ea. Furthermore, the method and system may include a cross plot of WA-Ea and log(A) to evaluate and classify reactivity for source rocks without requiring kinetic or basin modeling software. The method and system may include a process for converting kinetic parameters into reactivity as a single variable for the evaluation and characterization of source rocks. The method and system may include developing a scheme to assign kinetic parameters in basin modeling based on reactivity classification.

[0038] In some embodiments, evaluating reactivity according to the method and system for source rock evaluation may include determining the thermal reactivity (i.e., chemical reactivity under thermal stress) of source rocks at the same level of thermal maturity. The method and system may include interpreting kinetic parameters derived from thermally mature source rock samples, which is an improvement over methods and / or systems that only process kinetic parameters derived from immature source rock samples. The method and system can compare published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of source rock samples. The method and system can convert the complex format of kinetic parameters into reactivity as a single variable for source rock evaluation and characterization.

[0039] In some embodiments, classifying reactivity according to the method and system for kinetic allocation in basin modeling may include classifying the thermal reactivity of source rock samples with different thermal maturity. The method and system may include assigning kinetic parameters derived from immature source rock units to mature source rock units within source rock layers during basin modeling. The method and system can evaluate reactivity to improve the selection and allocation of kinetic parameters in basin modeling.

[0040] In one or more embodiments, the method and system treat thermal reactivity as a key variable because the reactivity of source rock samples can be evaluated in kinetic analysis or basin modeling software by generating and comparing oil-source material transformation curves. The method and system may include optimization methods relative to those relying solely on parameters such as sedimentary environment, stratigraphy, and oil-source material type. The method and system may include optimization methods relative to schemes such as total sedimentary and stratigraphic ages, which define five organic facies and assign a predetermined average kinetic from a set of source rocks within each organic facies. The method and system may include optimization methods relative to measuring as many possible kinetics as possible about immature samples to constrain uncertainty by using the mean of the Ea distribution, the mean of A, and the standard deviation of Ea in numerical simulations. The method and system can provide insight into and quantify uncertainties in kinetic modeling while providing guidance for kinetic assignment.

[0041] In one or more embodiments, the method and system may include an optimization approach, rather than applying kinetics determined at each location to a limited portion of the study area because there are no specific guidelines to constrain the study area and stratigraphic units to which kinetics can be applied. Rather than using only a weighted averaging approach, where kinetic parameters from samples at different locations and depths within a source rock interval are combined by assigning weights proportional to their Rock-Eval S2 yields to the Ea distribution of each sample, the method and system may include an optimization approach. In particular, while a single combined kinetic captures the average conversion and total potential of the source rock, this approach loses the temporal, potential, and compositional characteristics of different source rock units (i.e., the heterogeneity of the source rock) (e.g., if it is multi-component kinetics).

[0042] In one or more embodiments, the method and system provide kinetic parameters from kinetic measurements of source rock samples or kinetic parameters collected from publications for evaluation. Specifically, the reactivity of source rocks can be compared using conversion curves generated by extrapolating kinetic parameters using geoheating rates in kinetic analysis or basin modeling software. In this respect, the method and system do not require commercially available modeling software for evaluating source rock reactivity because they simplify the complex format of the kinetic parameters (i.e., WA-Ea and A) and graphically display these parameters on cross-plots, facilitating the use of kinetic parameters by geologists and prospectors to evaluate source rocks.

[0043] In one or more embodiments, the method and system provide a scheme for allocating measurement kinetics based on reactivity gradation, which can serve as a basis for developing new methods and / or systems for organic phase mapping and stratigraphic association.

[0044] Oil companies and technology service providers can use this method and system to enhance the evaluation (e.g., characterization) of source rocks in conventional oil systems and source rock reservoirs (e.g., unconventional oil systems). This method and system can compare kinetic parameters and provide assessments of source rock reactivity, which can generate greater value in kinetic analysis. To this end, this method and system can improve the classification of kinetic parameters and the selection and assignment of kinetics in basin modeling software.

[0045] Figure 1A schematic diagram illustrating a collection tool 160 for retrieving a source rock sample 150 from a subsurface sedimentary section 130 at a collection site 100 is shown. The collection tool 160 includes a central chamber 140 configured to collect, contain, and transport the source rock sample 150. The collection tool 160 may have a cylindrical shell extending along a central axis 180 throughout its entire length. The collection tool 160 can descend and ascend along the subsurface sedimentary section 130 to sample the source rock. The collection tool 160 can descend to a depth between an upper sedimentary section 110 and a lower sedimentary section 170 using a conveying mechanism 120. In some embodiments, the collection tool 160 includes a top operatively connected to the conveying mechanism 120, which allows the collection tool 160 to descend and ascend along the upper sedimentary section 110, the subsurface sedimentary section 130, and the lower sedimentary section 170. In some embodiments, the upper sedimentary section 110, the subsurface sedimentary section 130, and the lower sedimentary section 170 may produce depths of equal or different lengths. In some embodiments, the upper sedimentary section 110, the subsurface sedimentary section 130, and the lower sedimentary section 170 may be the same or different types of source rock samples with the same or similar maturity.

[0046] In some embodiments, the collection tool 160 may exchange information with the control system 360 (i.e., the ground surface panel). In some embodiments, the collection tool 160 may include sensors and systems for collecting data related to the area of ​​interest. In some embodiments, the collection tool 160 may include hardware and / or software for establishing a secure wireless connection (i.e., a communication link) with the ground surface panel to ensure real-time data exchange and compliance with data protection requirements.

[0047] Figure 2 The schematic diagram illustrates various systems that can be incorporated into the collection tool 160. In some embodiments, the collection tool 160 includes electronic components that enable it to perform communication, data collection, and / or processing functions. In some embodiments, the collection tool 160 includes a communication system 210, a processing system 220, a sensing system 230, and a sampling system 240 coupled to a central chamber 140 containing a source rock sample 150. The communication system 210 may include communication devices, such as a transmitter 212 and a receiver 214. The transmitter 212 and receiver 214 may transmit and receive communication signals, respectively. Specifically, the transmitter 212 and receiver 214 may communicate with one or more control systems located at a remote location via a wired connection. In some embodiments, the communication system 210 may wirelessly communicate with a control system 360 located at a surface 370. The surface 370 may be an underwater surface.

[0048] Processing system 220 may include processor 222 and memory 224. Processor 222 may execute computational processes simultaneously and / or sequentially. Processor 222 may use received or collected information to determine the information to be sent and the processes to be executed. Similarly, processor 222 may control the collection and exchange of geospatial information from collection tool 160.

[0049] The sensing system 230 may include external sensors 232. External sensors 232 may be sensors that collect physical data from the environment surrounding the collection tool 160. External sensors 232 may be lightweight sensors requiring a small footprint. These sensors can exchange information with each other and provide it to the processor 222 for analysis. External sensors 232 may be electrical, nuclear, acoustic, or another type of logging tool. External sensors 232 may emit signals (i.e., electrical, nuclear, or acoustic signals) via a signal generator at the sensing section.

[0050] The sampling system 240 may include a collection controller 242 that coordinates the collection of source rock sample 150 through a central hole (not shown) at the bottom of the collection tool 160. Coordinating the collection of source rock sample 150 may include determining the filling of the central chamber 140 at a predetermined depth or determining parameters of the source rock sample 150 collected in a subsurface sedimentary section 130.

[0051] Figure 3 An example of a collection tool 160 used for collecting source rock sample 150 according to one or more embodiments is shown. The collection system 300 may include a surface device 310, which includes an actuator 350, a sensor 340, and a control system 360 connected to each other using a hardware and / or software interface 320. Furthermore, the collection system 300 may be supported from the surface 370 by a structure 330. The collection system 300 includes an upper sedimentary section 110, a subsurface sedimentary section 130, and a lower sedimentary section 170 extending from the surface 370 to a subsurface stratum 390. The subsurface stratum 390 may have a porous region including a hydrocarbon pool. In some embodiments, the collection tool 160 is translated longitudinally 380 using the surface device.

[0052] The collection system 300 may include a control system (“control system”) 360. In some embodiments, during operation of the collection system 300, the control system 360 may collect and record wellhead data for the collection system 300. In some embodiments, the control system 360 may regulate the movement of the delivery mechanism 120 by modifying the power supplied to the actuating device 350. The delivery mechanism 120 may be a tool that couples the collection tool 160 to the structure 330. In some embodiments, the control system 360 includes a reference... Figure 1 The described ground surface panel.

[0053] The control system 360 may include a laboratory equipment room (not shown). The laboratory equipment room may include hardware and / or software with capabilities for generating one or more basin models of formation 390 and / or performing one or more reservoir simulations. The laboratory equipment room may be used to conduct experiments related to identifying kinetic parameters in source rock samples associated with subsurface sedimentary section 130. Furthermore, the laboratory equipment room may include storage devices for storing formation logs and data about the source rock samples for modeling or simulation. While the laboratory equipment room may be coupled to the control system 360, it may be located remotely from the field. In some embodiments, the laboratory equipment room may include a computer system configured to estimate the depth of collection tool 160 at any given time. The laboratory equipment room may use memory to compile and store historical data about subsurface sedimentary section 130.

[0054] In some embodiments, the actuation device 350 may be a motor or pump connected to the delivery mechanism 120 and the control system 360. In some embodiments, measurements are recorded in real time and are available for viewing or use within seconds, minutes, or hours after a condition is sensed (e.g., measurements are available within one hour of a condition being sensed). In such embodiments, wellhead data may be referred to as “real-time” wellhead data. Real-time data enables the operator of the collection system 300 to assess the relative current state of the collection system 300 and make real-time decisions regarding the development of the collection system 300 and the reservoir.

[0055] Figure 4 Examples of graphs according to one or more embodiments are shown. The relationship between the reaction rate k and temperature T during the conversion of the oil parent material can be described by the Arrhenius equation (1). The reaction rate k is positively linearly related to temperature on a logarithmic scale. Higher temperatures and lower Ea favor a faster conversion rate k. Figure 4 As shown, the graph illustrates the measured reaction rate k, which can be reversed by the regression line in the graph shown as "1 / T vs. ln(k)" to represent the pairing of Ea and A. Based on this relationship, one or more embodiments obtained kinetic parameters from pyrolysis experiments. The determination of kinetic parameters for oil-based material conversion is a two-step process, involving artificial maturation experiments followed by fitting the calculated kinetic parameters to laboratory data.

[0056] Figure 5 Schematic diagrams of examples according to one or more embodiments are shown. In one or more embodiments, the method and system include novel schemes for processing kinetic parameters and for interpreting kinetic parameters on cross-plots. The method and system can process measured kinetic parameters or reprocess published / archived kinetic parameters without using kinetic analysis or basin modeling software, while providing a rapid assessment of the reactivity of source rocks.

[0057] In some embodiments, open-system pyrolysis 510 using standard heating rates relies on source rock samples to evaluate 520 the organic matter (abundance, quality, and maturity) of the source rocks for source rock evaluation 512 and shale reservoir characterization 514. In some embodiments, open-system pyrolysis 510 at standard heating rates is a scheme for using pyrolysis experiments to obtain TOC and Rock-Eval parameters, as well as overall kinetic parameters, for evaluating source rock samples to improve source rock evaluation and basin modeling.

[0058] In some embodiments, temperature-programmed open-system pyrolysis (e.g., Rock-Eval, HAWK, SR Analyzer, POPI-TOC, and Pyromat) can be used for source rock evaluation 512 and shale reservoir characterization 514. Pyrolysis experiments conducted at a standard heating rate (i.e., 25 °C / min) provide essential parameters (including TOC, S1, S2, S3, T). max HI, OI, and PI are used to quantify the abundance, quality, and maturity of organic matter in source rocks. Pyrolysis experiments can be conducted at various heating rates (typically ranging from 0.5 °C / min to 50 °C / min) to generate pyrolysis data recording pyrolysis yield, temperature, and time. This data can then be analyzed using kinetic analysis software (e.g., Kinetics 2000, Kinetics 05, Kinetics 2015) or manually through regression and parameter fitting (e.g., PI). Figure 4 The pyrolysis data is processed in the diagram to derive the kinetic parameters.

[0059] Specifically, in pyrolysis experiments used for kinetic analysis, finely ground source rocks or isolated parent material samples with good organic matter abundance (i.e., TOC > 1%) can be used. Standard pyrolysis for kinetics may require samples that are not thermally mature to slightly mature (i.e., vitrinite reflectance (Ro) < 0.6%), which allows the obtained kinetic parameters to be used for basin modeling to simulate hydrocarbon generation from the start of parent material conversion to the depletion of all parent material potential. In some embodiments, the method and system can be applied to mature samples to evaluate and compare their reactivity, provided that mature samples can produce a hydrocarbon response (e.g., an S2 peak) during pyrolysis.

[0060] In some embodiments, different mathematical models can be used in kinetic analysis to derive kinetic parameters for different chemical reactions. Various mathematical models can be implemented using software, including discrete models, Gaussian models, nucleation models, first- or Nth-order models, Weibull models, alternate pathway models, and isoconversional models. In some embodiments, the model and system can be configured to handle commonly used models for the decomposition of oil masterbatch, i.e., discrete models—an Ea distribution with an energy interval of 1 kcal / mol and an optimized A. Other models with a continuous Ea distribution and an A can also be handled. In this regard, the method and system may include calculating a weighted average WA-Ea and evaluating the kinetic parameters reprocessed on a WA-Ea vs. A cross plot.

[0061] In some embodiments, open-system pyrolysis 530 for kinetic analysis includes a multi-heating-rate experiment 532 and the application of a mathematical model of kinetics 534 to derive kinetic parameters 540. Source rock evaluation via standard open-system pyrolysis may include performing an open-system pyrolysis 510 with a standard heating rate and oxidizing whole-rock powder samples to determine TOC and Rock-Eval parameters. In some embodiments, kinetic analysis via multi-heating-rate pyrolysis may include performing open-system pyrolysis experiments on source rock samples using multiple heating rates 532 (i.e., at least two heating rates that may differ by one or two orders of magnitude, such as 3 °C / min and 30 °C / min). Source rock samples may be whole-rock powder (i.e., TOC > 1%) or separated parent material. Source rock samples may have different maturities. In some embodiments, using the same pyrolysis apparatus may allow the use of different heating rates and sample types in the evaluation project to minimize the influence of laboratory and sample conditions on the determination of kinetic parameters.

[0062] In one or more embodiments, kinetic parameters 540 are derived using pyrolysis data obtained from open system pyrolysis 510 at standard heating rates. In some embodiments, the pyrolysis data can be used to derive kinetic parameters 540 based on a mathematical model having an Ea distribution and a common A. In some embodiments, using the same kinetic analysis software (or manual calculation method) and the same mathematical model (e.g., a discrete model) in the evaluation project ensures the comparability of the obtained kinetic parameters.

[0063] In some embodiments, characterization of kinetic parameters 550 includes calculating weighted kinetic parameters 552 and plotting the weighted kinetic parameters 554 to determine maturity lines and reactivity grades 560. This method and system can evaluate and compare published kinetic parameters, archived kinetic parameters, and measured kinetic parameters. Comparisons of published / archived / measured kinetics may require obtaining kinetic parameters using the same mathematical model (e.g., a discrete model) and very similar laboratory techniques (e.g., open-system pyrolysis with similar minimum and maximum heating rates).

[0064] For very thick source rock formations in the well or any area of ​​interest, kinetic data can be subgrouped based on depth and rock properties to ensure that variations in Ro (vitrinite reflectance) or VRE (Ro equivalent) are sufficiently small (e.g., less than 0.1%) in each subgroup. In this regard, T from open-system pyrolysis 510... max The results can be used to estimate VRE and create trend lines for each subgroup.

[0065] In one or more embodiments, for datasets that cannot be grouped by wells (e.g., scattered kinetic parameters from publications), the data can be divided into maturity groups (e.g., <0.5%, 0.5%–0.6%, 0.6%–0.7%, ...), and then a trend line can be created for each maturity group.

[0066] In one or more embodiments, maturity lines can be determined on the cross plot to show, as according to Figure 8 The examples discussed show maturity increasing clockwise, with a systematic shift in the Ea distribution as maturity increases.

[0067] In some embodiments, the kinetic evaluation 570 of source rocks includes evaluating the maturity of the source rocks 572 and evaluating and classifying the reactivity of the source rocks 574 to classify the kinetic parameters 580. When evaluating the maturity of source rocks, gradient-colored arc-shaped arrows can indicate the direction of increasing maturity. Therefore, the maturity of new source rock samples can be qualitatively estimated based on their respective WA-Ea and A values. When evaluating and classifying the reactivity of source rock samples, arrows can indicate the direction of decreasing reactivity. The reactivity of source rock samples with similar maturity (e.g., Ro change < 0.1%) or from a single well source rock formation can be determined based on specific trend lines.

[0068] Achieving improved source rock characterization through thermal reactivity 590 can include providing new variables or dimensions beyond the three parameters (i.e., quantity, quality, and thermal maturity) measured for source rock evaluation. In this regard, even if two source rock samples have the same source rock type, TOC, HI, and thermal maturity, a more reactive source rock sample may begin hydrocarbon generation and reach peak generation before a less reactive one. In another example, a shallower, more reactive source rock may generate hydrocarbons before a deeper, less reactive one.

[0069] In one or more embodiments, the graphical method provides a rapid assessment of reactivity without the need for complex calculations and basin modeling. In this regard, independent assessments of reactivity can be added to source rock evaluation procedures to better characterize source rocks and evaluate hydrocarbon potential in a dynamic geological history.

[0070] In one or more embodiments, the thermal reactivity classification 595 used to optimize the rock dynamics model may include optimizing the kinetic model of source rock samples in basin modeling. Kinetic parameters derived from immature source rock samples can be used as a kinetic representation of the entire source rock layer in the basin, and then used to simulate hydrocarbon generation and expulsion from source rocks in hydrocarbon systems. Simplified kinetic models may assume that the kinetic parameters of immature source rocks do not vary significantly. In some embodiments, the kinetics of immature source rocks represent the kinetics of more mature and deeper source rocks.

[0071] Figure 6 An example of processing a source rock sample according to one or more embodiments is shown. Figure 6 The parallel processing stages, occurring simultaneously or sequentially, are shown because, as a result of 600 source rock samples, more than one source rock sample 150 can be processed for modeling. References can be used... Figure 1 , Figure 2 and Figure 3 The described apparatus performs source rock sampling 600. In some embodiments, source rock sampling 600 may result in source rock static evaluation 610, basin modeling 630, source rock dynamic evaluation 620, and further optimized basin modeling 640. Specifically, source rock sampling 600 may include obtaining samples of immature, early-mature, and late-mature source rocks, respectively represented by samples 140a-140c.

[0072] In some embodiments, during the static evaluation 610 of source rocks using a standard open system, standard pyrolysis 612 involving TOC and Rock-Eval parameters can be used to obtain the abundance, quality, and maturity 614a, 614b, and 614c of organic matter in various source rock samples. Specifically, whole-rock powder samples can be subjected to open-system pyrolysis (standard heating rate, typically 25 °C / min) and oxidation to determine TOC and Rock-Eval parameters (e.g., S1, S2, T). max (HI, OI, etc.). In this regard, these data can be used to evaluate the abundance, quality, and maturity of organic matter in source rocks.

[0073] In some embodiments, for basin modeling 630, the abundance, quality, and maturity 614a-614c of organic matter from various source rock samples are used to define the source rocks in the region of interest. Kinetic parameters from the source rock kinetic evaluation 620 are another key input for simulating the conversion of oil parent material to hydrocarbons in source rocks for basin modeling. Various source rock samples 140a-140c are processed by multi-rate pyrolysis 532 to obtain kinetic parameters corresponding to the Ea distribution / A 624a, 624b, and 624c. Specifically, open-system pyrolysis experiments can be performed on source rock samples using multiple heating rates (at least two heating rates differing by one or two orders of magnitude, e.g., 3 °C / min and 30 °C / min). Samples can be whole-rock powder (TOC > 1%) or separated oil parent material. Samples can have different maturities. Throughout the evaluation project, the same pyrolysis apparatus, heating rates, and sample types are maintained to minimize the influence of laboratory and sample conditions on the determination of kinetic parameters. At this point, pyrolysis data can be used to derive kinetic parameters based on a mathematical model with an Ea distribution and a common A. The same kinetic analysis software (or manual calculation method) and the same mathematical model (e.g., a discrete model) should be used in the evaluation project to ensure the comparability of the obtained kinetic parameters. In some embodiments, published / archived parameters can be accessed and compared. Comparison of published / archived / measured kinetics may require obtaining kinetic parameters using the same mathematical model (e.g., a discrete model) and very similar laboratory techniques (e.g., open-system pyrolysis with similar minimum and maximum heating rates). In some embodiments, only kinetic parameters derived from immature source rock samples are used in conventional basin modeling.

[0074] In some embodiments, in order to perform source rock dynamic evaluation 620, various corresponding Ea distributions / A 624a-624c are processed by using Equation (2) to calculate weighted dynamic parameters 626 to obtain various WA-Ea / A values ​​626a, 626b and 626c.

[0075]

[0076] In equation (2), Ea i The distribution is the Ea value at each energy interval, W i Each Ea i The weights (i.e., normalized scores).

[0077] Furthermore, a weighted kinetic graph can be obtained, and this graph serves as the result for evaluating reactivity 628. In some embodiments, the source rock kinetic evaluation 620 involves using a new parameter. The new parameter can be a single reactivity variable used for source rock evaluation, other than the abundance, quality, and maturity of organic matter. In the graph, all pairings of WA-Ea and A can be paired along a logarithmic scale on the cross-plot. (See reference...) Figures 7A to 7C and Figure 13 Let's discuss an example of a cross plot. In the plot, you can adjust the scale and range of the X and Y axes to display all the data from the actual situation.

[0078] In some embodiments, the plotted weighted kinetics and evaluated reactivity 628 are used to classify reactivity and categorize kinetic parameters 642. At this point, kinetic parameters can be assigned 644 to specific plotted values ​​based on the reactivity classification. In this respect, the kinetic parameters assigned 644 based on the reactivity classification can be used to obtain optimized basin modeling 640. In some embodiments, optimized basin modeling 640 involves using multiple kinetics. These multiple kinetics can be multiple kinetic parameters assigned to different source rock units within the source rock based on the reactivity classification and the kinetic parameter categorization.

[0079] Kinetic data (WA-Ea and A) can be grouped based on the source rock formation from the well, and exponential trend lines can be generated for these data. The maturity of the same source rock formation in a well may be very similar unless the formation is very thick (e.g., >500 feet) or there is evidence of intrusion within the formation. This plot can be used to evaluate the maturity of samples with published / archived kinetics (when their maturity data are unavailable). In the plot, the reactivity of source rock samples with similar maturity (e.g., Ro variation <0.1%) or from a source rock formation in the same well can be determined based on trend lines generated from these data.

[0080] As mentioned above, reactivity, or a reactivity classification, can provide new variables or dimensions beyond the three parameters (quantity, quality, and thermal maturity) measured for source rock evaluation. For example, even if two source rocks have the same source material type, TOC, HI, and thermal maturity, a more reactive source rock may begin generating hydrocarbons and reach peak generation before a less reactive one. In another example, a shallower, more reactive source rock may generate hydrocarbons before a deeper, less reactive one. Therefore, reactivity can be rapidly assessed without complex calculations and basin modeling. Independent assessments of reactivity can be added to source rock evaluation procedures to better characterize source rocks and evaluate hydrocarbon potential in a dynamic geological history.

[0081] In some embodiments, the evaluation of source rock sampling 600 is divided into one or more acquisition periods (i.e., collection periods) and / or one or more processing periods (i.e., evaluation periods). During the acquisition period, data is acquired using samples 140a, 140b, and 140c through various processing devices. During the processing period, the processed data can be organized in real time into one or more aggregated data packages representing kinetic parameters and standard parameters, so that static evaluation 610, kinetic evaluation 620, basin modeling 630, and optimized basin modeling 640 can be continuously updated (i.e., source rock sampling evaluation is performed in real time over a period of time).

[0082] Figure 7A , Figure 7B and Figure 7C Graphs illustrating laboratory pyrolysis, kinetic optimization, and extrapolation to geological conditions according to one or more embodiments are shown. In some embodiments, the method and system focus on global kinetics. In this regard, artificial maturation techniques for obtaining global kinetic parameters may include... Figure 7A The methods shown utilize open-system pyrolysis at multiple constant heating rates (e.g., Rock-Eval, SR Analyzer, HAWK, POPI-TOC, Pyromat). Because the source material is heterogeneous, simplifications in kinetic analysis and basin modeling include a discrete distribution of Ea values ​​at 1 kcal / mol intervals and a common A, such as... Figure 7B As shown.

[0083] In some embodiments, the procedure for determining overall kinetic parameters may include pyrolyzing immature source rock samples in a multi-heating-rate experiment to generate reaction rate data. For example, the inverse kinetic parameters Ea and A can be fitted by calculating parameters using measured reaction rates. Figure 7A As shown in Figure 700, pairings of experimental and calculated values ​​are illustrated in 701, 702, 703, 704, and 705. These pairings of experimental and calculated values ​​can be distributed along discrete values ​​at various temperature ranges.

[0084] In some embodiments, the procedure for determining the overall kinetic parameters may include using a discrete distribution of Ea values ​​spaced at 1 kcal / mol intervals and a common A to obtain the kinetic parameters. For example... Figure 7B As shown, Figure 710 shows the percentages of activation energy values ​​(e.g., 711, 712, and 713), which indicate the number of activation energies occurring over time.

[0085] In some embodiments, the procedure for determining the overall kinetic parameters may include applying kinetics at a geoheating rate (i.e., 1 °C / Ma, where Ma is millions of years) to test the reasonableness of the parameters and estimate the onset and critical point of the oil-generating window. Figure 7C As shown, Figure 715 illustrates the fractional reactions relative to temperature values ​​(e.g., 716 and 717), which show the conversion points over time. In some embodiments, kinetic parameters can be directly input into basin modeling software to quantify the hydrocarbon generation process of the oil-to-source conversion and create thermal maturity and conversion maps. In this regard, all derived kinetics can be applied together with geoheating rates (based on thermal history) to test the plausibility and reactivity of the corresponding kinetics before implementing measurement kinetics in the basin model. Extrapolation can be performed in the kinetics module of kinetic software (e.g., Kinetics 2015) or basin modeling software (e.g., PetroMod) to provide conversion TR curves (e.g., ...). Figure 7C (As shown) to estimate the start temperature and time of the oil production window (i.e., TR = 10%) and the critical point (i.e., TR = 50%) for a given oil system.

[0086] Figure 8 A cross plot of the weighted average Ea versus A on a logarithmic scale is shown to evaluate the thermal reactivity and maturity of source rocks. The numbers on the X and Y axes and the positions of the dashed lines may vary depending on the specific context. Curved arrows indicate the direction of increasing maturity. This representation can be performed using a color gradient matching the dashed arrows corresponding to Wells 1, 2, and 3 at their respective intersections. Arrows indicate the direction of decreasing reactivity. In this case, points A, B, and C represent samples from immature source rock formations in Well 1; points D, E, and F represent samples from the same source rock formations within the oil window maturity of Well 2; and points G, H, and I represent samples from the same source rock formations within the gas window of Well 3.

[0087] As described above, the kinetic data (WA-Ea and A) from the source rock formations of the well are grouped, and an exponential trend line can be generated for the data. Under the assumption of similar source rock maturity in the well, the trend line on the WA-Ea and log A cross plot is a straight line, and the slope of this line is a function of the average maturity of the source rock formations in that well. The T values ​​from this group of samples can be used as a basis for further analysis. maxOr other maturity measurements can be used to label the average maturity. This process can be repeated to create more trend lines on the cross plot. For very thick source rock formations in the well, kinetic data can be subgrouped based on depth and rock properties to ensure that variations in Ro (i.e., vitrinite reflectance) or VRE (i.e., Ro equivalent) are sufficiently small (e.g., less than 0.1% in each subgroup). max This can be used to estimate VRE. Trend lines can then be created for each subgroup. For datasets that cannot be grouped by well (such as scattered kinetic parameters from publications), the data can be divided into maturity groups (e.g., <0.5%, 0.5%–0.6%, 0.6%–0.7%, ...), and then trend lines can be created for each maturity group. Finally, maturity lines can be determined on a cross-plot to show a clockwise increase in maturity, where the Ea distribution exhibits a systematic shift as maturity increases.

[0088] like Figure 5 and Figure 6 As shown, evaluating the maturity of source rock samples can explain Figure 8 The maturity gradient. In this respect, the maturity of new source rock samples can be qualitatively estimated based on their WA-Ea and A values. For example, Figure 8 The maturity level can be correlated using (A≈B≈C)<(D≈E≈F)<(G≈H≈I). Furthermore, as... Figure 5 and Figure 6 As shown, the evaluation and classification of the reactivity of source rock samples can explain variations in reactivity within the samples. For example, when source rock samples have similar maturity (e.g., Ro variation < 0.1%), or when the source rock layers in the well can be determined based on a reactivity trend line, Figure 8 The reactivity in these components can be correlated. For example, in... Figure 8 In this context, reactivity can be correlated using the order A > B > C, D > E > F, and G > H > I.

[0089] Figure 9 , Figure 10A and Figure 10B The examples shown illustrate reactivity evaluated using the methods and systems described in this specification. In some embodiments, curves perpendicular to the dashed lines can be plotted to classify the reactivity of source rocks. Figure 8As shown, two classification curves can be plotted arbitrarily to identify three reactivity categories: high, medium, and low. Based on the trend line for immature source rocks (e.g., Well 1), the corresponding WA-Ea values ​​for these reactivity categories can be less than 54 kcal / mol, between 54 kcal / mol and 58 kcal / mol, and less than 58 kcal / mol. In this case, the reactivity of source rocks with different maturity levels can be approximately assessed as (A≈D)>(B≈E≈G)>(C≈F≈I). The classification curves can be improved by measuring the kinetic parameters of a series of source rock samples that have undergone different degrees of artificial maturation. Further classification can be performed to establish a more detailed source rock kinetic model.

[0090] Reactivity grading can provide a solution for assigning kinetic parameters derived from immature source rock samples to mature source rock units. Within source rock layers, this method and system assume no significant changes in the organic phase, and that the kinetics of immature source rock units represent the optimal kinetics of those mature source rock units at the same reactivity grade. For example, Figure 8 The source rock units C, F, and I shown can fall into the same reactivity category (low). In this respect, C (rather than A or B) is likely the best kinetic representative of F and I. Furthermore, when modeling basins outlining regions defined by C, F, and I, it is best to use the kinetics derived from the immature source rock unit C (rather than the kinetic averages or weighted averages of A, B, and C). Directly using averaged or weighted average kinetics may result in the loss of detail regarding hydrocarbon generation due to the varying reactivity within the source rocks.

[0091] exist Figure 9 , Figure 10A , Figure 10B , Figure 11A , Figure 11B , Figure 11C , Figure 11D , Figure 12A , Figure 12B and Figure 13 The paper illustrates tests of the method and system with reference to two different datasets. The first case evaluates the measurement kinetics of source rock formations from different wells with different maturity levels. The second case evaluates the published and measurement kinetics derived from different source rock formations with the same maturity level (e.g., immature).

[0092] The first scenario illustrates the measurement kinetics from three wells. The source rocks are marine source rock formations (i.e., type II oil parent material) from four wells in Saudi Arabia, such as... Figure 9 As shown. Due to the lack of vitrinite granules, maturity is determined by T. maxThe graptolite reflectance was determined. In this case, wells T, S, M, and A correspond to immature wells, very early-oil maturity, late-oil maturity, and dry-grass maturity, respectively. Kinetic parameters were not determined from well A because the kinetic analysis of the sample from well A did not show an undefined S2 peak. In the tests, the pyrolysis instrument was an open system (e.g., HAWK). The laboratory heating rates for kinetic analysis were between 3 °C / min and 30 °C / min (inclusive). The mathematical model for the kinetic parameters included a common A and a discrete distribution of Ea spaced at 1 kcal / mol intervals, as shown in the figure. Figure 10A and Figure 10B As shown. The geological heating rate used for extrapolation (i.e., generating the transformation curve) is 1℃ / Ma.

[0093] In particular, Figure 9 A table is shown, including sample information, source rock pyrolysis data, and reprocessing kinetic parameters using the method of this invention. Figure 9 In this context, "depth" refers to the depth of the source rock formation in the well, where T < S < M < A. "Seq.#" refers to... Figure 11A , Figure 11B , Figure 11C , Figure 11D , Figure 12A , Figure 12B and Figure 13 The depth sequence number is shown. “VRE.t” refers to the vitrinite reflectance equivalent calculated by VRE = (0.01867*Tmax) - 7.306, and “GRo” refers to the graptolite reflectance. “VRE.g” refers to the vitrinite reflectance equivalent estimated based on Gro. “A” refers to the common frequency factor of the discrete Ea distribution. “WA-Ea” refers to the weighted average Ea of the discrete Ea distribution calculated using equation (2).

[0094] Figure 11A , Figure 11B , Figure 11C and Figure 11D Examples of kinetic parameters are shown, spanning Figures 1110, 1120, 1130, and 1140, illustrating the common A and discrete Ea distributions under case 1. In these... Figure 11A , Figure 11B , Figure 11C and Figure 11D In this study, pyrolysis data were generated using HAWK with open system pyrolysis at two heating rates (3 °C / min and 30 °C / min) for kinetic calculations in Kinetics 2015 software.

[0095] Figure 12A and Figure 12B This demonstrates how to use Kinetics 2015 for information about Figure 9 , Figure 10A and Figure 10B Evaluation of the transformation curves (1201, 1202, 1203, 1204 and 1205 or 1211, 1212, 1213, 1214 and 1215) generated by the wells under discussion. In this case, for well S, the thermal reactivity is curve 1201 > curve 1202 > curve 1203 > curve 1205 > curve 1204, and for well M, the thermal reactivity is curve 1213 > curve 1211 > curve 1215 > curve 1214 > curve 1216 > curve 1212. Figure 12A and Figure 12B The conversion rate curves generated using a discrete kinetic model (i.e., a common discrete distribution of A and Ea) with a geoheating rate of 1 °C / Ma in Kinetics 2015 software are shown. Furthermore, comparison of the conversion curves (i.e., conversion sequences) is a current solution for evaluating reactivity. In this case, from left to right, the conversion of source rock to hydrocarbons indicates that higher temperatures are required as the source rock samples become more difficult to convert, suggesting reduced reactivity. The numbers on the curves represent depth sequences, with curve 1201 being the shallowest sample.

[0096] Figure 13 An evaluation of the methods and systems described in this specification is shown. Figure 13 In this study, the method and system were used to evaluate thermal maturity and reactivity. Two exponential trend lines were plotted for data from wells S and M, respectively, showing the linear relationship between WA-Ea and Log(A). Thermal maturity consisted of two exponential trend lines plotted for data from wells S and M, respectively, showing the linear relationship between WA-Ea and Log(A). In this case, the maturity relationship was well T < well S < well M. For well S, the thermal reactivity was curve 1201 > curve 1202 > curve 1203 > curve 1205 > curve 1204, and for well M, curve 1213 > curve 1211 > curve 1215 > curve 1214 > curve 1216 > curve 1212. Figure 13 The thermal reactivity is classified as follows: low reactivity is located in well S; medium reactivity is located in curves 1202, 1203 and 1205 of wells T and S, and curves 1201 and 1213 of well M; high reactivity is located in curves 1212, 1214, 1215 and 1216 of wells S (4) and M.

[0097] In one or more embodiments, based on Figures 9 to 13 The results, the maturity sequence evaluated by this invention and the T maxThe assessment is consistent with that of graptolite reflectivity. The slope of the trend line can be representative of maturity. The thermal reactivity evaluated in this invention is consistent when assessed using conversion curves generated in Kinetics 2015 software. This method and system provide a gradation of the reactivity of source rocks with different maturities.

[0098] Figure 14 , Figure 15 and Figure 16 The method and system were tested using two different datasets. In this case, the measured kinetics were compared with published kinetics. The source rock samples measured were from a Saudi Arabian marine source rock formation (i.e., type II oil parent material). In this case, two samples from wells T and S were used. The published kinetics were for source rocks with reported kinetic parameters published by Tegelaar and Noble (1994). Figure 14 Based on other geochemical parameters (Tegelaar and Noble, 1994), the oil source material type was determined to be Type I (TI) / Type I-sulfur (TIS), Type II (TII) / Type II-sulfur (TII-S), and Type III (TIII). Its kinetic parameters are available in PetroMod's kinetic editor. Maturity indicates that, based on Rock-Eval HI and T... max Data, except for the Barnett Shale, are all immature for all samples, such as... Figure 14 As shown. Based on Figure 14 The estimated Ro of the Barnett Shale is greater than 0.70%, with the VRE of 0.96% indicated by empirical formulas for VRE and Tmax (Hackley and Cardott, 2016); although Tegelaar and Noble (1994) reported a relatively low Ro value (0.53%). The pyrolysis apparatus was similar to the open system measured in the HAWK published in Pyromat II. Laboratory heating rates for kinetic analysis were measured at 1 °C / min, 3 °C / min, 10 °C / min, 30 °C / min, and 50 °C / min. Kinetic parameters were published at 1 °C / min, 5 °C / min, 15 °C / min, and 50 °C / min. The mathematical model for the kinetic parameters was a common A and a discrete distribution of Ea at intervals of 1 kcal / mol. The geoheating rate used for extrapolation (i.e., generation transformation curves) was 1 °C / Ma.

[0099] In one or more embodiments, Figure 14The evaluation was performed using conversion curves generated in PetroMod software. In this case, the thermal reactivity was classified as follows: Type I and III: T.Akar (TI) > Mae Sot (TIS) > Ribesalbes (TI) > La Luna (TI) > Green River (TI) > Mannville (TI) > Tasmanites (TI); Type II: Monterey (II) > Bakken (II) > Kimmeridge (II) > Arabia Well S (II) > Arabia Well T (II) > Woodford (II) > Pematang (II) > Barnett (II). In this case, the Barnett shale used in the analysis is likely a mature sample, and therefore its kinetics and reactivity are not comparable to other source rocks (i.e., immature samples). Due to the influence of maturity on the Ea distribution, the conversion curve of the immature Barnett shale should be shifted to the left, and its reactivity should be in the middle of the Type II source rock sequence.

[0100] In one or more embodiments, the thermal reactivity classification can be arbitrary, as reactivity can be classified based on the transformation sequence. In this case, [1.0] is the most reactive source rock, while [7.0] is the most stable source rock in this case. For Type I and Type III: [1.0]T.Akar>[2.0]Mae Sot>[3.0]Ribesalbes>[4.0]La Luna>[5.0]Green River>[6.0]Mannville>[7.0]Tasmanites. In the reactivity classification of Type I and Type III, the classification of Type II is evaluated: [3.0]Monterey>[4.0]Bakken>[4.5]Kimmeridge>[4.8]Arabia Well S>[5.0]Arabia Well T>[5.1]Woodford>[6.0-TII]Pematang>[6.2-TII]Barnett

[0101] In one or more embodiments, Figure 15 An assessment indicating the level of maturity is presented, comprising an exponential trend line plotted over all published data, showing a clear linear relationship between WA-Ea and Log(A), which is generally consistent with maturity assessments. Figure 15In the study, the thermal reactivity of Type I and Type III shale rocks was as follows: T.Akar (TI) > Mae Sot (TI) > Ribesalbes (TIS) > La Luna (TI) > Green River (TI) > Mannville (TI) > Tasmanites (TI); and for Type II: Monterey (II) > Bakken (II) > Kimmeridge (II) > Arabia Well S (II) ≈ Arabia Well T (II) > Barnett (TII) > Woodford (TII) > Pematang (TII). The kinetic parameters and correlations of the Barnett Shale are not comparable due to their maturity levels.

[0102] exist Figure 15 In the classification of thermal reactivity, arbitrary numbers are used for the reactivity classification. In this case, [1.0] is the most reactive source rock, while [7.0] is the most stable source rock in this case. For Type I and Type III: [1.0]T.Akar > [2.0]Mae Sot > [3.0]Ribesalbes > [4.0]La Luna > [5.0]Green River > [6.0]Mannville > [7.0]Tasmanites. In the reactivity classification of Type I and Type III, the classification of Type II is evaluated as follows: [3.0]Monterey > [4.0]Bakken > [4.2]Kimmeridge > [4.8]Arabia Well S > [5.0]Arabia Well T > [5.3-TII]Barnett > [5.8]Woodford > [6.0-TII]Pematang. The kinetic parameters and correlations of the Barnett Shale are not comparable due to its maturity.

[0103] In this case, compared to evaluation through transformed sequences, Figure 14 and Figure 15 This approach provides very similar reactivity classifications for source rocks at the same maturity level (under the "immature" category). In some embodiments, the method and system use WA-Ea instead of the Ea distribution in geological extrapolation, which quantifies only the average kinetic behavior and reactivity of source rocks within the major hydrocarbon generation window. This simplification is advantageous for the use of kinetic parameters in reactivity assessments but may lose the initiation and / or later stages of the oil-source material transformation (e.g., Figure 14 As shown, details are provided when TR < 10% and TR > 90%.

[0104] Figure 14The Rock-Eval HI and T, modified from the article by Comford et al. (1998), are shown. max The cross-plot shows the type of oil source material and its maturation trend. Figure 14 and Figure 15 In the described tests, two samples of marine source rocks (Type II) from Saudi Arabia were measured, and 13 marine source rocks (Type I: green; Type II: light blue; Type III: purple) from Tegelaar and Noble (1994) were used as examples of published data. All samples were immature except for the Barnett Shale. Although the authors reported a relatively low Ro value (0.53%), the Ro of the Barnett Shale, estimated based on the graph, should be greater than 0.70%. Overall kinetic parameters for the 13 source rocks are available in PetroMod.

[0105] Figure 15 The conversion rate curves generated by applying a discrete dynamics model (with common discrete distributions of A and Ea) and a geothermal heating rate (1 °C / Ma) in PetroMod software are shown. Figure 15 As shown, the comparison of transformation curves (i.e., transformation sequences) is the current solution for evaluating reactivity. In this case, from left to right, the transformation of oil source material to hydrocarbons requires higher temperatures and becomes more difficult, indicating a decrease in the reactivity of the source rock. For Type I (solid curve) and Type III source rocks, the reactivity is: T. Akar (TI) > Mae Sot (TI) > Ribesalbes (TIS) > La Luna (TI) > Green River (TI) > Mannville (TIII) > Tasmanites (TI); for Type II source rocks (dashed curve): Monterey (TIIS) > Bakken (TII) > Kimmeridge (TII) > Arabia Well S (TII) > Arabia Well T (TII) > Woodford (TII) > Pematang (TII) > Barnett (TII). Furthermore, the Barnett Shale used in the analysis may be a mature sample. The numbers near the curves are arbitrary classifications of reactivity based on the transformation sequences, namely Type I & III and Type II. In this case, [1.0] is the most reactive source rock, while [7.0] is the most stable source rock.

[0106] Figure 16 An example is shown where the method and system are used to evaluate thermal maturity and reactivity. The exponential trend line shows a very good linear relationship between WA-Ea and Log(A), which is generally consistent with the relationship based on... Figure 13The maturity assessments (i.e., all except Barnett are immature) are consistent. For Type I (circular) and Type III (triangular) source rocks, the reactivity is: T.Akar (TI) > Mae Sot (TI) > Ribesalbes (TIS) > La Luna (TI) > Green River (TI) > Mannville (TIII) > Tasmanites (TI); and for Type II (rhomboid) source rocks: Monterey (TIIS) > Bakken (TII) > Kimmeridge (TII) > Arabia Well S (TII) ≈ Arabia Well T (TII) > Barnett (TII) > Woodford (TII) > Pematang (TII).

[0107] Figure 17 A flowchart according to one or more embodiments is shown. Specifically, Figure 17 A method for evaluating reactivity in source rock evaluation is described. In some embodiments, the method may use reference... Figure 3 The collection system 300 described is implemented by the control system 360. Furthermore, Figure 17 One or more boxes in the middle can be accessed through, for example Figures 1 to 3 It is performed by one or more components as described herein. Although Figure 17 The boxes in the document are presented and described in sequence, but those skilled in the art will understand that some or all of these boxes may be executed in a different order, may be combined or omitted, and may be executed in parallel. Furthermore, these boxes may be executed actively or passively.

[0108] In box 1710, information related to various source rock samples is obtained from the area of ​​interest (AOI). This can be achieved using methods such as... Figures 1 to 3 The collection tool 160 described herein is used to obtain this information. Source rock samples can be collected and tested at the same location using an in-situ laboratory facility, which can be similar to the reference facility. Figure 3 The laboratory equipment room under discussion. Samples can be tested additionally or alternatively at locations remote from the source rock sample collection site.

[0109] In box 1720, the abundance, quality, and maturity of organic matter in various source rock samples are evaluated using TOC and Rock-Eval data from the AOI. In laboratory testing procedures, the collected source rock samples and their associated information can be used to test and evaluate abundance, quality, and maturity. As mentioned above, open-system pyrolysis 510 can be used to determine thermal maturity while evaluating the organic matter in the source rocks.

[0110] In box 1730, common kinetic parameters are derived from multi-heating-rate pyrolysis of various source rock samples in the AOI. Under common kinetic models, the discrete distribution of Ea values ​​can be paired with a common A to obtain the kinetic parameters.

[0111] In box 1740, create a dynamics dataset including all measured dynamic parameters, published dynamic parameters, and archived dynamic parameters. Various dynamic parameters correspond to AOIs. For example... Figures 4 to 13 As shown, various kinetic parameters are collected, edited, and compared to determine changes in the evaluation process based on the obtained information. (See reference...) Figure 5 As described, after the open system pyrolysis 510, a heating rate experiment 532 can be performed to generate pyrolysis data for deriving kinetic parameters 540, which are used for kinetic parameter characterization 550 and kinetic evaluation 570. The pyrolysis data can be processed in kinetic analysis software (e.g., Kinetics 2000, Kinetics 05, Kinetics 2015) or in manual regression and parameter fitting to derive the kinetic parameters 540.

[0112] In box 1750, the weighted average Ea is calculated using equation (2), and all pairs of WA-Ea and logA are plotted on the cross plot. A weighted average is applied to the kinetic parameters 540 / 624a-624c according to the formula... Figures 8 to 1 Cross-plots are generated using the method discussed in section 0. As mentioned above, cross-plots of the weighted averages of kinetic parameters 552 or 626a-626c can be plotted on a logarithmic scale to evaluate the thermal reactivity and maturity of the source rocks. Figure 8 Since the curved arrows indicate the direction of increasing maturity, the maturity of new source rock samples can be qualitatively estimated based on the derived kinetic parameters 552 or 626a-626c.

[0113] In box 1760, maturity lines are identified in the cross-plot. Kinetic data (WA-Ea and A) are grouped by well or maturity range. For data from a single well or a specific maturity range, an exponential trend line (maturity line) representing the dynamic changes of source rocks at the same maturity level can be generated in the cross-plot. Because the Ea distribution exhibits a systematic shift with increasing maturity, the trend line may show a clockwise increase in maturity. In this case, multiple source rock samples are plotted to visually identify their maturity relative to each other.

[0114] In box 1770, responsiveness is evaluated at the same maturity level, and responsiveness is graded according to different maturity levels. For example... Figure 8As shown, the arrows on the maturity lines indicate a decrease in reactivity at the same maturity level. For source rocks of different maturity levels, reactivity can be graded across maturity lines. The graphical representation of the source rock kinetic data provides a rapid assessment of reactivity without complex calculations and basin modeling.

[0115] In box 1780, the complex format of the kinetic parameters is transformed into a single reactive variable for the source rock. This variable is easier to use than common kinetic parameters in the evaluation and characterization of source rocks in the region of interest.

[0116] Figure 18 A flowchart according to one or more embodiments is shown. Specifically, Figure 18 A method is described for evaluating reactivity in source rock assessment to improve the selection and assignment of kinetic parameters in basin modeling. In some embodiments, the method can use a reference... Figure 3 The collection system 300 described is implemented by the control system 360. Furthermore, Figure 18 One or more boxes in the middle can be accessed through, for example Figures 1 to 3 It is performed by one or more components as described herein. Although Figure 18 The boxes in the document are presented and described in sequence, but those skilled in the art will understand that some or all of these boxes may be executed in a different order, may be combined or omitted, and may be executed in parallel. Furthermore, these boxes may be executed actively or passively.

[0117] In box 1805, information related to various source rock samples is obtained for AOI. As described with reference to box 1710, source rock samples can be collected and tested at the same location using an in-situ laboratory facility, which can be similar to the one described in the reference box. Figure 3 The laboratory equipment room under discussion. Samples can be tested additionally or alternatively at locations remote from the source rock sample collection site.

[0118] In box 1810, as described with reference to box 1720, the abundance, quality, and maturity of organic matter in various source rock samples are evaluated using TOC and Rock-Eval from the AOI. As mentioned above, parameters can be determined using open-system pyrolysis 510 while evaluating the organic matter in the source rocks.

[0119] In box 1815, as described with reference to box 1730, common kinetic parameters are derived from multi-heating-rate pyrolysis of various source rock samples in the AOI. Under common kinetic models, the discrete distribution of Ea values ​​can be paired with a common A to obtain the kinetic parameters.

[0120] In box 1820, a dynamics dataset is created, including all measured dynamic parameters, published dynamic parameters, and archived dynamic parameters. Specifically, as described with reference to box 1740 and as referred to... Figure 5 As described, after the open system pyrolysis 510, a heating rate experiment 532 can be performed to generate pyrolysis data for deriving kinetic parameters 540, which are used for kinetic parameter characterization 550 and kinetic evaluation 570. The pyrolysis data can be processed in kinetic analysis software (e.g., Kinetics 2000, Kinetics 05, Kinetics 2015) or in manual regression and parameter fitting to derive the kinetic parameters 540.

[0121] In box 1825, as described with reference to box 1750, the weighted average Ea is calculated using equation (2), and all pairs of WA-Ea and logA are plotted on the cross plot. A weighted average is applied to the kinetic parameters 540 or 624a-624c, according to the formula regarding... Figures 8 to 1 Cross-plots are generated using the method discussed in section 0. As mentioned above, cross-plots of the weighted averages of kinetic parameters 552 or 626a-626c can be plotted on a logarithmic scale to evaluate the thermal reactivity and maturity of the source rocks. Figure 8 Since the curved arrows indicate the direction of increasing maturity, the maturity of new source rock samples can be qualitatively estimated based on the derived kinetic parameters 552 or 626a-626c.

[0122] In box 1830, as described with reference to box 1760, maturity lines are determined in the cross-plot. Kinetic data (WA-Ea and A) are grouped by well or maturity range. For data from a single well or a specific maturity range, an exponential trend line (maturity line) representing the dynamic changes of source rocks at the same maturity level can be generated in the cross-plot. Because the Ea distribution exhibits a systematic shift with increasing maturity, the trend line may show a clockwise increase in maturity. In this case, multiple source rock samples are plotted to visually identify the maturity of these samples relative to each other.

[0123] In box 1835, as described with reference to box 1770, responsiveness is evaluated at the same maturity level, and responsiveness is graded for different maturity levels. Figure 8 As shown, the arrows on the maturity lines indicate a decrease in reactivity at the same maturity level. For source rocks of different maturity levels, reactivity can be graded across maturity lines. The graphical representation of the source rock kinetic data provides a rapid assessment of reactivity without complex calculations and basin modeling.

[0124] Furthermore, in box 1835, the thermal reactivity of source rocks is graded at different thermal maturity levels. The grading curves can be improved by measuring the kinetic parameters 540 or 622 of a series of source rock samples that have undergone different degrees of artificial maturation. As mentioned above, further grading can be performed to establish a more detailed source rock kinetic model. Reactivity or grading can provide a new variable or dimension beyond the parameters conventionally measured for source rock evaluation (i.e., quantity, quality, and thermal maturity). For example, a source rock sample with higher reactivity may begin generating hydrocarbons and reach peak generation before a source rock with lower reactivity.

[0125] In box 1840, source rock models and organic facies are defined based on TOC, Rock-Eval parameters, and all available information in the AOI. The defined stratigraphic models and organic facies are used to represent the geological heterogeneity and organic geochemical characteristics of source rocks, supporting the allocation of multiple dynamics in different units of the source rock strata.

[0126] In box 1845, the kinetic parameters of the organic phase in the source rock are classified according to the reactivity classification and maturity trend of the cross-plot. See also boxes 1825 to 1835. Figure 13 The described method involves processing the kinetic parameters of different units within each organic phase of a source rock stratum to evaluate their reactivity. Therefore, the kinetic parameters of source rock units with different maturities can be classified based on their organic phase and reactivity levels.

[0127] In box 1850, based on the classification of kinetic parameters, kinetic parameters derived from immature source rock units are assigned to mature source rock units within the source rock layer. In the organic phase, if thermally mature and immature source rock units are at the same reactivity level, thermally mature source rock units can share the kinetic parameters derived from immature source rock units. In existing basin modeling techniques, kinetic parameters derived from immature source rock samples are used as a kinetic representative of the entire source rock layer in the basin, and then used to simulate hydrocarbon generation and expulsion in petroleum systems originating from that source rock. (Refer to the above...) Figure 9 , Figures 11A to 11D As illustrated in Figure 12, because there are considerable regional and vertical variations in the kinetics of individual source rocks (even at the same well location), using a single set of kinetic parameters and directly assigning kinetic parameters to mature units of the source rock from which these parameters are derived cannot explain the variations in organic phases and kinetics throughout the formation. In this respect, reactivity grading allows for the assignment of kinetic parameters derived from immature source rock samples to mature units of the source rock. Within the organic phase of the source rock, the kinetics of immature source rock units represent the optimal kinetics of mature source rock units within the same reactivity gradation.

[0128] In box 1855, reactivity is evaluated to improve the selection and assignment of kinetic parameters in basin modeling. As described above, the present invention describes an well-founded method for selecting kinetic parameters derived from immature source rock units for mature source rock samples and allows for the assignment of multiple kinetic parameters to different units of source rock layers in sedimentary basins. Multiple kinetic parameters are introduced into the source rock model to address the heterogeneity of organic matter in source rocks and the vertical and lateral variations in kinetics. In this respect, by using better kinetic representation and multiple kinetic parameters, hydrocarbon generation and expulsion in petroleum systems can be improved, thereby achieving optimized basin modeling.

[0129] Embodiments of the present invention can be implemented using virtually any type of computing system, regardless of the platform used. In some embodiments, the control system 360 may be a computer system located at a remote location, allowing the collected data to be processed remotely from the ground surface 370. In some embodiments, the computing system may be implemented on a remote or handheld device (e.g., a laptop computer, smartphone, personal digital assistant, tablet computer, or other mobile device), a desktop computer, a server, a blade in a server chassis, or any other type of computing device that includes at least the minimum processing power, memory, and input and output devices required to perform one or more embodiments of the present invention.

[0130] like Figure 19 As shown, the computing system 1900 may include one or more computer processors 1904, non-persistent memory 1902 (e.g., random access memory (RAM), cache memory, or flash memory), one or more persistent memory 1906 (e.g., hard disk), a communication interface 1908 (transmitter and / or receiver), and many other elements and functions. The computer processor 1904 may be an integrated circuit for processing instructions. The computing system 1900 may also include one or more input devices 190, such as a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device. In some embodiments, one or more input devices 1920 may be referenced... Figure 1 and Figure 3 The described ground surface panel. Furthermore, the computing system 1900 may include one or more output devices 1910, such as a screen (e.g., a liquid crystal display (LCD), plasma display, or touchscreen), a printer, external storage, or any other output device. The one or more output devices may be the same as or different from the input devices. The computing system 1900 may be connected to a network system 1930 (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, a mobile network, or any other type of network) via a network interface connection (not shown).

[0131] In one or more embodiments, for example, input device 1920 may be coupled to a receiver and transmitter for exchanging communication with one or more peripheral devices (which are connected to network system 1930). The receiver may receive information related to one or more source rock samples. The transmitter may relay information received by the receiver to other elements in computing system 1900. Furthermore, computer processor 1904 may be configured to perform or assist in the implementation of references. Figure 18 and / or Figure 19 The process described.

[0132] Furthermore, one or more components of the aforementioned computing system 1900 may be located in a remote location and connected to other components via a network system 1930. The network system 1930 may be a cloud-based interface that performs processing at a remote location far from the well site and connects to other components via a network. In this case, the computing system 1900 can be connected via a remote connection established using 5G connectivity (e.g., protocols established in version 15 and later of the 3GPP / New Radio (NR) standard).

[0133] Figure 19 The computing system in the system can implement and / or connect to a data store. For example, one type of data store is a database. A database is a collection of information configured to facilitate data retrieval, modification, reorganization, and deletion. In some embodiments, the database includes components such as those referenced... Figures 1 to 18 The methods and system described are related to the published / measurement data.

[0134] Although Figures 1 to 19 Various configurations of the components are shown, but other configurations may be used without departing from the scope of this disclosure. For example, Figures 1 to 3 Various components can be combined to create a single component. As another example, a function performed by a single component can be performed by two or more components.

[0135] Although this disclosure has been described with respect to a limited number of embodiments, those skilled in the art will recognize, with benefit from this disclosure, that other embodiments can be devised without departing from the scope of the invention disclosed herein. Therefore, the scope of this disclosure should be defined only by the appended claims.

Claims

1. A method for classifying reactivity for dynamic assignment in basin modeling, the method comprising: Obtain information related to multiple source rock samples; The thermal reactivity of the source rocks corresponding to the plurality of source rock samples is determined, wherein the source rocks are at the same level of thermal maturity in the region of interest; The thermal reactivity at different thermal maturity levels is graded. The published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of the source rocks in the region of interest are compared. The kinetic parameters of the organic phase in the source rock layer are classified according to reactivity and maturity. The kinetic parameters derived from immature source rock units in the source rock layers of the sedimentary basin are assigned to mature source rock units; and The reactivity is evaluated to improve the selection and assignment of the dynamic parameters in basin modeling.

2. The method according to claim 1, further comprising: The thermal reactivity of the source rock in the region of interest is classified accordingly. Furthermore, the kinetic parameters in the organic phase of the source rock layer are classified according to the reactivity and maturity.

3. The method according to claim 1, further comprising: Explain the kinetic parameters derived from the plurality of source rock samples; and The complex format of the kinetic parameters is transformed into a single reactive variable for evaluation and characterization of source rocks in the region of interest.

4. The method according to claim 3, further comprising: The thermal maturity of each of the plurality of source rock samples is estimated based on the interpreted kinetic parameters.

5. The method according to claim 1, wherein, The thermal reactivity is the chemical reactivity under thermal stress.

6. The method according to claim 3, wherein, The kinetic parameters explained are derived from thermally mature source rock samples.

7. The method according to any one of claims 1 to 6, wherein, The dynamic parameters include the frequency factor A and the weighted average of the Ea distribution.

8. A system for classifying reactivity for dynamic allocation in basin modeling, the system comprising: A receiver that receives information related to multiple source rock samples; as well as The processor, the processor: The thermal reactivity of the source rocks corresponding to the plurality of source rock samples is determined, wherein the source rocks are at the same level of thermal maturity in the region of interest; The thermal reactivity at different thermal maturity levels is graded. The published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of source rocks in the region of interest are compared. The kinetic parameters of the organic phase in the source rock layer are classified according to reactivity and maturity. The kinetic parameters derived from immature source rock units in the source rock layers of the sedimentary basin are assigned to mature source rock units; and The reactivity is evaluated to improve the selection and assignment of the dynamic parameters in basin modeling.

9. The system according to claim 8, wherein, The processor also: The thermal reactivity of the source rock in the region of interest is classified accordingly. Furthermore, the kinetic parameters in the organic phase of the source rock layer are classified according to the reactivity and maturity.

10. The system according to claim 8, wherein, The processor also: Explain the kinetic parameters derived from the aforementioned source rock samples; and The complex format of the kinetic parameters is transformed into a single reactive variable for evaluation and characterization of source rocks in the region of interest.

11. The system according to claim 10, wherein, The processor estimates the thermal maturity of each of the plurality of source rock samples based on the interpreted kinetic parameters.

12. The system according to claim 8, wherein, The reactivity mentioned is chemical reactivity under thermal stress.

13. The system according to claim 10, wherein, The kinetic parameters explained are derived from thermally mature source rock samples.

14. The system according to any one of claims 8 to 13, wherein, The dynamic parameters include the frequency factor A and the weighted average of the Ea distribution.

15. A non-transitory computer-readable medium storing instructions executable by a computer processor, the instructions comprising the following functions: Obtain information related to multiple source rock samples; The thermal reactivity of the source rocks corresponding to the plurality of source rock samples is determined, wherein the source rocks are at the same level of thermal maturity in the region of interest; The thermal reactivity at different thermal maturity levels is graded. The published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of the source rocks in the region of interest are compared. The kinetic parameters of the organic phase in the source rock layer are classified according to reactivity and maturity. Kinetic parameters derived from immature source rock units in sedimentary basin source rock layers are assigned to mature source rock units; and The reactivity is evaluated to improve the selection and assignment of the dynamic parameters in basin modeling.

16. The non-transitory computer-readable medium of claim 15, wherein the instructions further include the following functions: The thermal reactivity of the source rocks in the region of interest is classified according to their properties. Furthermore, the kinetic parameters in the organic phase of the source rock layer are classified according to the reactivity and maturity.

17. The non-transitory computer-readable medium of claim 15, wherein the instructions further include the following functions: Explain the kinetic parameters derived from the aforementioned source rock samples; and The complex format of the kinetic parameters is transformed into a single reactive variable for evaluation and characterization of source rocks in the region of interest.

18. The non-transitory computer-readable medium of claim 17, wherein the instructions further include the following functions: The thermal maturity of each of the plurality of source rock samples is estimated based on the interpreted kinetic parameters.

19. The non-transitory computer-readable medium according to claim 17, wherein, The kinetic parameters explained are derived from thermally mature source rock samples.

20. The non-transitory computer-readable medium according to any one of claims 15 to 19, wherein, The dynamic parameters include the frequency factor A and the weighted average of the Ea distribution.

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