Methods and systems for evaluating reactivity in source rock evaluation

Abstract

A method for evaluating thermal reactivity in a source rock evaluation can include obtaining information related to various source rock samples (140a; 140b; 140c). The method can include determining reactivity of source rock corresponding to the various source rock samples. In a region of interest, the source rock can be at the same thermal maturity level. The method can include interpreting kinetic parameters (540; 624a; 624b; 624c) derived from the plurality of source rock samples (140a; 140b; 140c). The method includes comparing published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of source rock in the region of interest. The method can include converting complex formats of kinetic parameters into a single reactivity variable for evaluating and characterizing source rock in the region of interest.

Description

BACKGROUND

[0001] Kinetic parameters are inputs in basin modeling to determine the onset and rate of hydrocarbon generation and the depth or temperature of the oil and gas generation window. Kinetic parameters that describe the rate of chemical reactions include activation energy and frequency factor. To this end, most petroleum system modeling assumes that oil and gas 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 sub-components of kerogen are stimulated separately (i.e., each sub-component has a different set of frequency factor and activation energy distribution). SUMMARY

[0002] Generally, in one aspect, embodiments disclosed by this specification relate to a method for evaluating thermal reactivity in a source rock evaluation. The method includes obtaining information related to various source rock samples. The method includes determining reactivity of source rock corresponding to the various source rock samples. The source rocks in a region of interest are at the same thermal maturity level. The method includes interpreting kinetic parameters derived from the plurality of source rock samples. The method includes comparing published kinetic parameters, archived kinetic parameters, and measured kinetic parameters for source rocks in the region of interest. The method includes converting the complex format of kinetic parameters into a single reactivity variable for evaluating and characterizing source rocks in the region of interest.

[0003] Generally, in one aspect, embodiments disclosed by this specification relate to a system for evaluating thermal reactivity in a source rock evaluation. The system includes a receiver that receives information related to various source rock samples. The system includes a processor that determines reactivity of source rock corresponding to the various source rock samples. The source rocks in a region of interest are at the same thermal maturity level. The processor interprets kinetic parameters derived from the various source rock samples. The processor compares published kinetic parameters, archived kinetic parameters, and measured kinetic parameters for source rocks in the region of interest. The processor converts the complex format of kinetic parameters into a single reactivity variable for evaluating and characterizing source rocks in the region of interest.

[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 hydrocarbon source rock samples. The instructions include functionality for determining a thermal reactivity of a hydrocarbon source rock corresponding to the various hydrocarbon source rock samples. In a region of interest, the hydrocarbon source rocks are at the same level of thermal maturity. The instructions include functionality for interpreting kinetic parameters derived from the various hydrocarbon source rock samples. The instructions include comparing published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of the hydrocarbon source rocks in the region of interest. The instructions include functionality for converting complex formats of kinetic parameters into a single reactivity variable for evaluation and characterization of the hydrocarbon source rocks in the region of interest.

[0005] Other aspects disclosed in this specification will be apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0006] Specific embodiments of the disclosed technology will now be described in detail with reference to the figures. Like reference numbers are used to indicate like elements in the figures.

[0007] Figure 1 A schematic diagram showing a cross-sectional view of a hydrocarbon source rock sampling process is shown.

[0008] Figure 2 A schematic diagram of a collection tool is shown.

[0009] Figure 3 A hydrocarbon source rock sampling process is shown.

[0010] Figure 4 A plot of using measured reaction rates k and temperature to determine the kinetic parameters Ea and A of the Arrhenius equation is shown.

[0011] Figure 5 A schematic diagram of a system is shown.

[0012] Figure 6 A schematic diagram of a system is shown.

[0013] Figure 7A A plot of reaction rates in pyrolysis experiments with different heating rates is shown.

[0014] Figure 7B A plot presenting a set of kinetic parameters is shown.

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

[0016] Figure 8 A graph showing one or more kinetic parameters reprocessed by a method according to one or more embodiments.

[0017] Figures 9-10B A graph showing a table of source rock pyrolysis and kinetic data according to one or more embodiments.

[0018] Figures 11A-11D A graph showing an example of commonly used kinetic parameters according to one or more embodiments.

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

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

[0021] Figure 13 A graph showing kinetic parameters reprocessed by a method for evaluating source rock reactivity according to one or more embodiments.

[0022] Figure 14 and Figure 15 A graph showing kerogen types and conversion curves for source rocks tested using a method according to one or more embodiments.

[0023] Figure 16 A graph showing kinetic parameters reprocessed by a method according to one or more embodiments for source rocks tested using the method.

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

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

[0026] Figure 19 A schematic diagram of a system according to one or more embodiments. DETAILED DESCRIPTION

[0027] Specific embodiments of the present disclosure will now be described in detail with reference to the drawings. Like reference numerals indicate like elements in the drawings. To be diligent in keeping the scope of the present disclosure, the drawings are to be accurately described.

[0028] In the following detailed description of the embodiments of the present disclosure, numerous specific details are set forth 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 cases, well-known features have not been described in detail to avoid unnecessarily complicating the description.

[0029] Throughout this 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 with 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 only 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, a first element may contain more than one element, and may be ranked after (or before) a second element in the ordering of elements.

[0030] In general, embodiments of the present disclosure include methods and systems for evaluating reactivity in source rock evaluation. In some embodiments, the methods and systems provide a simplified format for kinetic parameters and a graphical approach to evaluating source rock reactivity. In some embodiments, the methods and systems facilitate the use of kinetic parameters as a single variable (e.g., reactivity) for source rock evaluation and characterization. To this end, the methods and systems provide a novel approach to improving the selection and allocation of kinetic parameters in basin modeling.

[0031] In one or more embodiments, the basis for implementing the source rock dynamics of the method and system includes Arrhenius equations and kinetic parameters, derivation of kinetic parameters from pyrolysis experiments, and 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 kerogen into oil in source rocks, which are viewed as a series of irreversible reactions controlled by first-order chemical kinetics. These chemical kinetics can be described by the Arrhenius equation shown in formula (1). Formula (1) determines the conversion rate of kerogen to hydrocarbons under thermal stress during the burial history of the source rock and illustrates the effects of temperature and time on oil generation. Kinetic parameters (activation energy Ea and frequency factor A) are key inputs to source rocks in basin modeling and are used to quantify the generation, retention and expulsion of oil and to determine the timing of any related processes.

[0032]

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

[0034] In one or more embodiments, the thermal reactivity of a source rock (i.e., the thermal stability of kerogen) is a key variable in determining the extent of kerogen conversion and the timing of hydrocarbon generation in the geologic history. In some embodiments, the reactivity is considered together with other parameters such as TOC (i.e., quantity), kerogen type (i.e., quality), and T max or vitrinite reflectance (i.e., maturity) to fully characterize a source rock.

[0035] Advantageously, the methods and systems described in this specification provide a streamlined solution for evaluating the reactivity of a source rock. The methods and systems allow for a direct comparison and interpretation of the format of commonly used kinetic parameters. In this case, the reactivity of a source rock can 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. The methods and systems expand the use of reactivity in source rock evaluation and shale resource assessment by preventing expertise and / or access to appropriate software from limiting source rock evaluation.

[0036] Furthermore, the methods and systems described in this specification improve the practice of basin modeling by addressing the heterogeneity of source rocks (i.e., the kinetics of a source rock can vary along the vertical and lateral directions in a source rock) and by providing a well-founded method to assign measured kinetic parameters derived from immature source rock units in a depositional basin to mature source rock units.

[0037] In one or more embodiments, the methods and systems include a weighted average of Ea (i.e., also referred to as WA-Ea) to simplify the format of a discrete distribution of Ea. Furthermore, the methods and systems can include a crossplot of WA-Ea and log(A) to evaluate reactivity and rank reactivity for a source rock without the need for kinetic or basin modeling software. The methods and systems can include a process to convert kinetic parameters to reactivity as a single variable for evaluation and characterization of a source rock. The methods and systems can include developing a protocol to assign kinetic parameters in basin modeling based on the ranking of reactivity.

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

[0039] In some embodiments, ranking reactivity according to the method and system for kinetic allocation in basin modeling can include ranking thermal reactivity of source rock samples at different thermal maturities. The method and system can include allocating kinetic parameters derived from immature source rock units to mature source rock units in a source rock formation in basin modeling. The method and system can evaluate reactivity to improve selection and allocation of kinetic parameters in basin modeling.

[0040] In one or more embodiments, the method and system consider thermal reactivity as a key variable because reactivity of source rock samples can be evaluated by generating and comparing kerogen conversion curves in kinetic analysis or basin modeling software. The method and system can include an optimized method over methods that only rely on parameters such as depositional environment, stratigraphy, and kerogen type. The method and system can include an optimized method over schemes such as total deposition and stratigraphic age, which define five organic facies and assign a predetermined average kinetics from a set of source rocks in that organic facies to each organic facies. The method and system can include an optimized method over measuring as many possible kinetics as possible on immature samples to constrain uncertainty by using the average of the Ea distribution, the average of A, and the standard deviation of Ea in numerical simulations. The method and system can provide an awareness and quantification of uncertainty in kinetic modeling while providing guidance for kinetic allocation.

[0041] In one or more embodiments, the method and system can include an optimization method relative to applying the determined kinetics at each location to a limited portion of the study area because there is no specific guidance to constrain the study area and stratigraphic units where kinetics can be applied. The method and system can include an optimization method relative to using only a weighted average method where the kinetics parameters from samples at different locations and depths in a source rock interval are combined by giving each sample's Ea distribution a weight proportional to its Rock-Eval S2 yield. In particular, while a single combined kinetics captures the average conversion and total potential of a source rock, this method loses the characteristics of time, potential, and composition of different source rock units (i.e., the heterogeneity of source rocks) (e.g., if it is a multi-component kinetics).

[0042] In one or more embodiments, the method and system provide an evaluation from the kinetics parameters from the kinetics measurements of the source rock samples or from the kinetics parameters collected from publications. In particular, the reactivity of source rocks can be compared by conversion curves generated by extrapolating the kinetics parameters with the geologic heating rate in a kinetics analysis or basin modeling software. In this regard, the method and system do not require commercialized modeling software for evaluating the reactivity of source rocks because the method and system simplify the complex format of the kinetics parameters (i.e., WA-Ea and A) and show these parameters graphically on a crossplot, which facilitates the use of the kinetics parameters by geologists and explorers to evaluate source rocks.

[0043] In one or more embodiments, the method and system provide a ranking based on reactivity to allocate the scheme of measuring kinetics, which can be used as a basis for developing new methods and / or new systems for organic facies mapping and stratigraphic correlation.

[0044] Oil companies and service providers can use the method and system to enhance the evaluation (e.g., characterization) of source rocks and source rock reservoirs (e.g., unconventional petroleum systems) of conventional petroleum systems. The method and system can compare the kinetics parameters and provide an evaluation of source rock reactivity, which can yield more value from the kinetics analysis. To this end, the method and system can improve the classification of kinetics parameters and the selection and allocation of kinetics in basin modeling software.

[0045] Figure 1A diagram illustrating a collection tool 160 for retrieving a source rock sample 150 from a subsurface depositional 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 can have a cylindrical housing that extends along a central axis 180 through an entire length of the collection tool 160. The collection tool 160 can be lowered and raised along the subsurface depositional section 130 to sample the source rock. The collection tool 160 can be lowered to a depth between the upper depositional section 110 and the lower depositional section 170 using a conveyance mechanism 120. In some embodiments, the collection tool 160 includes a top operably connected to the conveyance mechanism 120 that lowers and raises the collection tool 160 along the upper depositional section 110, the subsurface depositional section 130, and the lower depositional section 170. In some embodiments, the upper depositional section 110, the subsurface depositional section 130, and the lower depositional section 170 can produce equal or different lengths of depth. In some embodiments, the upper depositional section 110, the subsurface depositional section 130, and the lower depositional section 170 can be the same or different types of source rock samples having the same or similar maturity.

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

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

[0048] The processing system 220 can include a processor 222 and a memory 224. The processor 222 can perform computational processes simultaneously and / or sequentially. The processor 222 can use received or collected information to determine information to send and processes to perform. Similarly, the processor 222 can control the collection and exchange of geospatial information from the collection tool 160.

[0049] The sensing system 230 can include external sensors 232. The external sensors 232 can be sensors that collect physical data from the environment surrounding the collection tool 160. The external sensors 232 can be light-weight sensors that require a small footprint. These sensors can exchange information with each other and provide it to the processor 222 for analysis. The external sensors 232 can be electrical, nuclear, acoustic, or another type of logging tool. The external sensors 232 can release a signal (i.e., an electrical, nuclear, or acoustic signal) through a signal generator at the sensing portion.

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

[0051] Figure 3 An example of the collection tool 160 being used to collect a source rock sample 150 is shown in accordance with one or more embodiments. The collection system 300 can include a surface equipment 310 that includes an actuation device 350, a sensor 340, and a control system 360 connected to each other using hardware and / or software to produce an interface 320. Further, the collection system 300 can be supported by a structure 330 from a surface 370. The collection system 300 includes an upper depositional section 110, a subsurface depositional section 130, and a lower depositional section 170 extending from the surface 370 to a subsurface formation 390. The subsurface formation 390 can have a porous region that includes a hydrocarbon pool. In some embodiments, the collection tool 160 translates along a longitudinal direction 380 using the surface equipment.

[0052] The collection system 300 can include a control system (“control system”) 360. In some embodiments, the control system 360 can collect and record wellhead data for the collection system 300 during operation of the collection system 300. In some embodiments, the control system 360 can adjust the movement of the conveyance mechanism 120 by modifying the power supplied to the actuation device 350. The conveyance mechanism 120 can 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 to the surface panel described.

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

[0054] In some embodiments, the actuation device 350 can be a motor or pump connected to the conveyance mechanism 120 and the control system 360. In some embodiments, the measurements are recorded in real-time and are available for review or use within seconds, minutes, or hours of sensing a condition (e.g., measurements are available within 1 hour of sensing a condition). In such embodiments, the wellhead data can be referred to as “real-time” wellhead data. Real-time data can enable an operator of the collection system 300 to assess a relatively current state of the collection system 300 and make real-time decisions regarding development of the collection system 300 and the reservoir.

[0055] Figure 4 An example of a graph is shown in accordance with one or more embodiments. The relationship between reaction rate k and temperature T for kerogen conversion can be described by the Arrhenius equation (1). Reaction rate k is linearly related to temperature on a logarithmic scale. Higher temperatures and lower Ea favor a fast conversion rate k of the reaction. As shown in Figure 4 The graph shows the measured reaction rate k, which can be inverted to pairs of Ea and A by the regression line shown as “1 / T vs. ln(k)” in the graph. Based on this relationship, one or more embodiments obtain kinetic parameters from pyrolysis experiments. The determination of kinetic parameters for kerogen conversion is a two-step process that includes an artificial maturation experiment followed by fitting the calculated kinetic parameters to laboratory data.

[0056] Figure 5 A schematic diagram of an example in accordance with one or more embodiments is shown. In one or more embodiments, the method and system include a new approach for processing kinetic parameters and for interpreting kinetic parameters on a crossplot. The method and system can process measured kinetic parameters or reprocess published / archived kinetic parameters without the need to use kinetic analysis or basin modeling software while providing a quick assessment of the reactivity of a source rock.

[0057] In some embodiments, open system pyrolysis 510 at standard heating rate relies on a hydrocarbon source rock sample to evaluate 520 the organic matter (abundance, quality, and maturity) of the hydrocarbon source rock for hydrocarbon source rock evaluation 512 and shale reservoir characterization 514. In some embodiments, open system pyrolysis 510 at standard heating rate is a protocol using pyrolysis experiments to obtain TOC and Rock-Eval parameters and overall kinetic parameters for evaluating a hydrocarbon source rock sample to improve hydrocarbon 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 hydrocarbon source rock evaluation 512 and shale reservoir characterization 514. Pyrolysis experiments conducted with a standard heating rate (i.e., 25 °C / min) provide basic parameters (including TOC, S1, S2, S3, T max , HI, OI, and PI) to quantify the abundance, quality, and maturity of organic matter in a hydrocarbon source rock. Pyrolysis experiments can be conducted at a variety of heating rates (typically using rates ranging from 0.5 °C / min to 50 °C / min) to generate pyrolysis data recording pyrolysis yields, temperature, and time. The pyrolysis data can then be processed in kinetic analysis software (e.g., Kinetics2000, Kinetics05, Kinetics2015) or manual regression and parameter fitting (as shown in Figure 4 ) to derive kinetic parameters.

[0059] In particular, in pyrolysis experiments for kinetic analysis, finely ground hydrocarbon source rock or isolated kerogen samples with good organic matter abundance (i.e., TOC > 1%) can be used. Standard pyrolysis for kinetics can require samples that are not thermally mature to slightly mature (i.e., vitrinite reflectance (Ro) < 0.6%), which allows the derived kinetic parameters to be used for basin modeling to simulate hydrocarbon generation from the beginning of kerogen conversion to the exhaustion of all kerogen potential. In some embodiments, as long as a mature sample can produce a hydrocarbon response (e.g., S2 peak) in pyrolysis, the method and system can be applied to the mature sample to evaluate and compare its reactivity.

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

[0061] In some embodiments, the open-system pyrolysis for kinetic analysis 530 includes multi-heating rate experiments 532 and applies mathematical models of kinetics 534 to derive kinetic parameters 540. Hydrocarbon source rock evaluation by standard open-system pyrolysis can include performing 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 by multi-heating rate pyrolysis can include performing open-system pyrolysis experiments on hydrocarbon source rock samples using multiple heating rates 532 (i.e., at least two heating rates that can differ by one or two orders of magnitude, such as 3 °C / min and 30 °C / min). The hydrocarbon source rock samples can be whole rock powder (i.e., TOC > 1%) or isolated kerogen. The hydrocarbon source rock samples can have different maturities. In some embodiments, using the same pyrolysis instrument can allow for the use of different heating rates and sample types in an evaluation project to minimize the impact 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 a standard heating rate. In some embodiments, the pyrolysis data can be used to derive kinetic parameters 540 based on mathematical models with 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., discrete model) in an evaluation project can ensure that the kinetic parameters obtained are comparable.

[0063] In some embodiments, the characterizing 550 of kinetic parameters includes calculating weighted kinetic parameters 552 and plotting weighted kinetic parameters 554 to determine maturity lines and reactivity ranking 560. The method and system can evaluate and compare published kinetic parameters, archived kinetic parameters, and measured kinetic parameters. Comparison of published / archived / measured kinetics can require that kinetic parameters be obtained by the same mathematical model (e.g., 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 a well or any region of interest, kinetic data can be divided into subgroups based on depth and rock properties to ensure that the change in Ro (vitrinite reflectance) or VRE (Ro equivalent) is small enough (e.g., less than 0.1%) in each subgroup. In this regard, T max The results can be used to estimate VRE and make a trend line for each subgroup.

[0065] In one or more embodiments, for datasets that cannot be grouped by well (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 is made for each maturity group.

[0066] In one or more embodiments, the maturity line can be determined on a crossplot to show a clockwise increase in maturity as shown in the example discussed above, where there is a systematic shift in the Ea distribution as maturity increases. Figure 8

[0067] In some embodiments, the kinetic evaluation 570 of source rocks includes evaluating maturity 572 of source rocks and evaluating and ranking 574 reactivity of source rocks to classify 580 kinetic parameters. In evaluating maturity of source rocks, a gradual color-arc arrow can indicate the direction of increasing maturity. Thus, the maturity of a new source rock sample can be qualitatively estimated based on the respective WA-Ea and A values. In evaluating and ranking reactivity of source rock samples, an arrow can indicate the direction of decreasing reactivity. The reactivity of source rock samples with similar maturity (e.g., Ro change <0.1%) or from source rock formations in one well can be determined based on the specific trend line.

[0068] ​Implementing thermal reactivity 590 for improved source rock characterization can include providing a new variable or dimension beyond the three parameters measured for source rock evaluation (i.e., quantity, quality, and thermal maturity). In this regard, even if two source rock samples have the same kerogen type, TOC, HI, and thermal maturity, the source rock sample with higher reactivity can start generating hydrocarbons and reach a peak of generation before the source rock with lower reactivity. In another example, a shallower source rock with higher reactivity can generate hydrocarbons before a deeper source rock with lower reactivity.

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

[0070] In one or more embodiments, thermal reactivity ranking 595 for optimizing rock kinetic models can include optimizing kinetic models of source rock samples in basin modeling. Kinetic parameters derived from immature source rock samples can be used as kinetic representatives of the entire source rock formation in a basin and then used to simulate hydrocarbon generation and expulsion from source rocks in a petroleum system. The simplified kinetic model can assume that the kinetic parameters of immature source rocks do not change 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 source rock samples is shown in accordance with one or more embodiments. Figure 6 Parallel processing stages are shown to occur simultaneously or in sequence, as a result of source rock sampling 600, more than one source rock sample 140a-140c can be processed for modeling. The devices described with reference to Figure 1 、 Figure 2 and Figure 3 may be used for source rock sampling 600. In some embodiments, source rock sampling 600 can result in source rock static evaluation 610, basin modeling 620, source rock kinetic evaluation 630, and further optimized basin modeling 640. Specifically, source rock sampling 600 can include taking samples of immature, early mature, and late mature source rocks represented by source rock samples 140a-140c, respectively.

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

[0073] In some embodiments, to perform basin modeling 630, the abundance, quality, and maturity of the organic matter of various source rock samples 614a-614c are used to define source rocks in the area of interest. Kinetic parameters from source rock kinetic evaluation 620 are another key input to simulate the conversion of kerogen to hydrocarbons in source rocks for basin modeling. Various source rock samples 140a-140c are processed by multi-heating rate pyrolysis 622 to obtain kinetic parameters corresponding to 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). The samples can be whole rock powder (TOC > 1%) or isolated kerogen. The samples can have different maturities. In an evaluation project, the same pyrolysis instrument, heating rates, and sample types can be maintained to minimize the impact of laboratory and sample conditions on the determination of kinetic parameters. At this point, the pyrolysis data can be used to derive kinetic parameters based on a mathematical model with Ea distribution and a common A. The same kinetic analysis software (or manual calculation method) and the same mathematical model (e.g., discrete model) can be maintained in an evaluation project to ensure that the obtained kinetic parameters are comparable. In some embodiments, published / archived parameters can be accessed and compared. Comparison of published / archived / measured kinetics can require that kinetic parameters are obtained by the same mathematical model (e.g., 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, to perform source rock kinetic evaluation 620, various corresponding Ea distribution / A 624a-624c are processed by calculating weighted kinetic parameters 626 using equation (2) to obtain various WA-Ea / A values 626a, 626b, and 626c.

[0075]

[0076] In Equation (2), Ea i is the value of Ea at each energy interval, W i is the weight (i.e., normalized fraction) of each Ea i .

[0077] Further, a weighted kinetics plot can be obtained and this plot is used to evaluate reactivity 628. In some embodiments, the hydrocarbon source rock kinetics evaluation 620 involves the use of a new parameter. The new parameter can be a single reactivity variable for hydrocarbon source rock evaluation in addition to the abundance, quality, and maturity of the organic matter. In the plot, all the pairs of WA-Ea and A can be paired along the log scale on the crossplot. An example of the crossplot will be discussed with reference to FIG. 7 and Figure 13 the discussion of the crossplot. In the plot, the scale and range of the X and Y axes of the crossplot can be adjusted to show all the data in the actual case.

[0078] In some embodiments, the plotted weighted kinetics and evaluated reactivity 628 are used to rank the reactivity and classify 642 the kinetics parameters. At this point, the kinetics parameters can be assigned 644 to specific plot values based on the reactivity ranking. In this regard, the kinetics parameters assigned 644 based on the reactivity ranking can be used to obtain optimized basin modeling 640. In some embodiments, the optimized basin modeling 640 involves the use of multiple kinetics. The multiple kinetics can be multiple kinetics parameters assigned to different hydrocarbon source rock units in the hydrocarbon source rock based on the reactivity ranking and the classification of the kinetics parameters.

[0079] The kinetics data (WA-Ea and A) can be grouped for the hydrocarbon source rock formations from a well, and an exponential trend line can be generated for these data. The maturity of the same hydrocarbon source rock formations in a well can be very similar unless the formation is very thick (e.g., > 500 feet) or there is evidence of intrusion in the formation. This plot can be used to evaluate the maturity of the published / archived kinetics samples when their maturity data are not available. In the plot, the reactivity of the hydrocarbon source rock samples with similar maturity (e.g., Ro variation < 0.1%) or from the hydrocarbon source rock formations in one well can be determined based on the trend line generated from these data.

[0080] As described above, reactivity or the ranking of reactivity can provide a new variable or dimension beyond the three parameters (quantity, quality, and thermal maturity) measured for source rock evaluation. For example, even if two source rocks have the same kerogen type, TOC, HI, and thermal maturity, a source rock with higher reactivity can start generating hydrocarbons and reach a peak of generation before a source rock with lower reactivity. In another example, a shallower source rock with higher reactivity can generate hydrocarbons before a deeper source rock with lower reactivity. Thus, reactivity can be quickly evaluated without complex calculations and basin modeling. The independent evaluation of reactivity can be added to the source rock evaluation procedure to better characterize source rocks and evaluate hydrocarbon potential in the dynamic geologic 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 periods, data is acquired using the source rock samples 140a, 140b, and 140c by various processing devices. During the processing periods, the processed data can be organized in real-time into one or more aggregated data packages representing the kinetic and standard parameters, such that the static evaluation 610, the kinetic evaluation 620, the basin modeling 630, and the optimized basin modeling 640 can be continually updated (i.e., perform the evaluation of the source rock sampling over time in real-time).

[0082] Figure 7A 、 Figure 7B and Figure 7C A diagram illustrating laboratory pyrolysis, kinetic optimization, and extrapolation to geological conditions is shown in accordance with one or more embodiments. In some embodiments, the method and system focus on bulk kinetics. In this regard, the artificial maturation technique for obtaining bulk kinetic parameters can include using open system pyrolysis (e.g., Rock-Eval, SR Analyzer, HAWK, POPI-TOC, Pyromat) at multiple constant heating rates in the manner shown in FIG. 700. Figure 7A Because kerogen is heterogeneous, the simplification of kinetic analysis and basin modeling includes a discrete distribution of Ea values at 1 kcal / mol intervals and a common A, as shown in FIG. 701. Figure 7B

[0083] In some embodiments, the procedure for determining bulk kinetic parameters can include pyrolysis of 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 the reaction rate with the measured parameters. As shown in FIG. 700, pairs 701, 702, 703, 704, and 705 of experimental and calculated values are shown. The pairs of experimental and calculated values can be distributed on discrete values along various temperature values. Figure 7A ​​

[0084] In some embodiments, the procedure for determining overall kinetic parameters can include obtaining kinetic parameters using a discrete distribution of Ea values spaced at 1 kcal / mol and a common A. As shown in Figure 7B Figure 710, for example, shows the percent of activation energy values (e.g., 711, 712, and 713) that show a change in activation energy over time.

[0085] In some embodiments, the procedure for determining overall kinetic parameters can include applying kinetics at a geologic heating rate (i.e., 1 °C / Ma, where Ma is millions of years) to test the reasonableness of the parameters and to estimate the start and critical point of the oil window. As shown in Figure 7C Figure 715, for example, shows the fractional reaction (e.g., 716 and 717) versus temperature values that show a conversion point over time. In some embodiments, the kinetic parameters can be directly imported into basin modeling software to quantify the hydrocarbon generation process of kerogen conversion and to create thermal maturity and conversion plots. In this regard, all derived kinetics can be applied with the geologic heating rate (based on thermal history) to test the corresponding reasonableness and reactivity before implementing the measured kinetics in a basin model. Extrapolation can be performed in the kinetics module of the kinetic software (e.g., Kinetics 2015) or basin modeling software (e.g., PetroMod) to provide conversion rate TR curves (as shown in Figure 7C ) to estimate the start temperature and time (i.e., TR = 10%) and the critical point (i.e., TR = 50%) of the oil window for a given petroleum system.

[0086] Figure 8 Figure 720, for example, shows a plot of the weighted average Ea versus A on a logarithmic scale for evaluating the thermal reactivity and maturity of a hydrocarbon source rock. The numbers on the X and Y axes and the location of the dashed lines can vary depending on the specifics. The curved arrow indicates the direction of increasing maturity. This representation can be performed using a color gradient that matches the dashed arrow corresponding to Well 1, Well 2, and Well 3 at their respective intersection points. The arrow indicates the direction of decreasing reactivity. In this case, points A, B, and C represent samples from a source rock formation in Well 1 that are immature; points D, E, and F represent samples from the same source rock formation in Well 2 that are within the oil window maturity; and points G, H, and I represent samples from the same source rock formation in Well 3 that are within the gas window.

[0087] As described above, kinetic data (WA-Ea and A) are grouped for source rock formations from a well, and an exponential trend line of the data can be generated. Under the assumption that source rock maturity is similar in a well, the trend line on the WA-Ea and log A plot is a straight line, and the slope of the line is a function of the average maturity of the source rock formations in the well. The T maxor other measures of maturity. This process can be repeated to create more trend lines on the crossplot. For very thick source rock formations in a well, the kinetic data can be divided into subgroups based on depth and rock properties to ensure that the change in Ro (i.e., vitrinite reflectance) or VRE (i.e., Ro equivalent) is small enough (e.g., less than 0.1% in each subgroup).T max VRE can be estimated. Then, a trend line can be made for each subgroup. For datasets that cannot be grouped by well (e.g., sporadic 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 made for each maturity group. Finally, a maturity line can be determined on the crossplot to show the clockwise increase in maturity, where there is a systematic shift in the distribution of Ea as maturity increases.

[0088] As shown in Figure 5 and Figure 6 , the evaluation of the maturity of a source rock sample can explain the maturity gradient of Figure 8 In this regard, the maturity of a new source rock sample can be qualitatively estimated based on its WA-Ea value and A value. For example, the maturity in Figure 8 can be correlated by (A ~ B ~ C) < (D ~ E ~ F) < (G ~ H ~ I). Furthermore, as shown in Figure 5 and Figure 6 , the evaluation and ranking of the reactivity of a source rock sample can explain the variation in reactivity in the sample. For example, when the source rock samples have similar maturity (e.g., a change in Ro < 0.1%), or when the source rock formations in a well can be determined based on the reactivity trend line, Figure 8 the reactivity in Figure 8 can be correlated. For example, in Figure 8 , the reactivity can be correlated by A > B > C, D > E > F, and G > H > I.

[0089] Figure 9 , Figure 10A and Figure 10B , the reactivity evaluated by the methods and systems described in this specification is shown. In some embodiments, a curve perpendicular to the dashed line can be plotted to rank the reactivity of the source rock. As shown in Figure 8As shown, two grading curves can be arbitrarily drawn 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 can be approximately evaluated as (A≈D)>(B≈E≈G)>(C≈F≈I). The grading curve can be improved by measuring the kinetic parameters of a series of source rock samples that have undergone different degrees of artificial maturation. More grading can be performed to establish a source rock kinetic model with more details.

[0090] The reactivity classification can provide a solution for assigning kinetic parameters derived from immature source rock samples to mature units of the source rock. The method and system assume that there are no significant changes in the organic phase within the source rock formation and that the kinetics of the immature source rock units represent the best kinetics of those mature source rock units within the same reactivity classification. For example, Figure 8 Source rock units C, F, and I shown in Figure 1 may be in the same reactivity category (low). In this regard, C (rather than A or B) may be the best kinetic representative of F and I. Furthermore, when modeling a basin outlined by C, F, and I, it is best to use the kinetics derived from the immature source rock unit C (rather than the average or weighted average of the kinetics of A, B, and C). Direct use of average or weighted average kinetics may result in a loss of detail in hydrocarbon generation due to varying reactivities in 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 In the present invention, a test of implementing the method and system is shown with reference to two different data sets. The first case evaluates the measured dynamics of source rock formations from different wells of different maturity. The second case evaluates the published dynamics and measured dynamics derived from different source rock formations of the same maturity (e.g., immature).

[0092] The first case shows measured dynamics from three wells. The source rock is a marine source rock formation (i.e., Type II kerogen) from four wells in Saudi Arabia, as shown in Figure 1. Figure 9 Due to the lack of vitrinite particles, maturity is determined by T maxand pen reflectance determination. In this case, Well T, Well S, Well M and Well A correspond to immature, 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 have an undefined S2 peak. In the test, the pyrolysis instrument was an open system (e.g., HAWK). The laboratory heating rate for the kinetic analysis was 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 Eawith a spacing of 1 kcal / mol, as shown in Figure 10A and Figure 10B The geological heating rate used for extrapolation (i.e., to generate conversion curves) was 1°C / Ma.

[0093] In particular, Figure 9 a table is shown that includes sample information, source rock pyrolysis data and reprocessed kinetic parameters by the method of the present invention. In Figure 9 , "Depth" refers to the depth of the source rock formation in the well, where Well T < Well S < Well M < Well A. "Seq. #" refers to the depth sequence number as shown in Figure 11A , Figure 11B , Figure 11C , Figure 11D , Figure 12A , Figure 12B and Figure 13 "VRE.t" refers to the vitrinite reflectance equivalent calculated by VRE = (0.01867*Tmax) - 7.306, "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 for the discrete Ea distribution. "WA-Ea" refers to the weighted average Eaof the discrete Ea distribution calculated using equation (2).

[0094] Figure 11A , Figure 11B , Figure 11C and Figure 11D Examples of kinetic parameters are shown in FIGS. 11-14, which bar charts 1110, 1120, 1130 and 1140 show the common A and the discrete Ea distribution under Case 1. In these Figure 11A , Figure 11B , Figure 11C and Figure 11D , pyrolysis data were generated by HAWK using open system pyrolysis with two heating rates (3°C / min and 30°C / min) for kinetic calculations in Kinetics2015 software.

[0095] Figure 12A and Figure 12B The evaluation of the conversion curves (1201, 1202, 1203, 1204 and 1205 or 1211, 1212, 1213, 1214 and 1215) generated for the wells discussed in relation to Figure 9 , Figure 10A and Figure 10B is shown. In this case, for well S, the thermal reactivity is conversion curve 1201 > conversion curve 1202 > conversion curve 1203 > conversion curve 1205 > conversion curve 1204, and for well M, the thermal reactivity is conversion curve 1213 > conversion curve 1211 > conversion curve 1215 > conversion curve 1214 > conversion curve 1216 > conversion curve 1212. Figure 12A and Figure 12B The conversion curves generated in the Kinetics2015 software applying a discrete kinetic model of the geothermal heating rate of 1°C / Ma (i.e., common A and Ea discrete distribution) are shown. In addition, the comparison of the conversion curves (i.e., conversion sequence) is the current solution for evaluating reactivity. In this case, from left to right, the conversion of kerogen to hydrocarbons indicates that it becomes more difficult to convert and indicates a decrease in the reactivity of the source rock sample, requiring higher temperatures. The numbers on the curves represent the depth sequence, with conversion curve 1201 being the shallowest sample.

[0096] Figure 13 The evaluation using the method and system described in the present specification is shown. In Figure 13 , the method and system are used to evaluate thermal maturity and reactivity. Two exponential trend lines are plotted for the data of well S and well M, respectively, showing the linear relationship between WA-Ea and Log(A). The thermal maturity includes two exponential trend lines plotted for the data of well S and well M, respectively, in which the linear relationship between WA-Ea and Log(A) is shown. In this case, the relationship of maturity is well T < well S < well M. For well S, the thermal reactivity is conversion curve 1201 > conversion curve 1202 > conversion curve 1203 > conversion curve 1205 > conversion curve 1204, and for well M, conversion curve 1213 > conversion curve 1211 > conversion curve 1215 > conversion curve 1214 > conversion curve 1216 > conversion curve 1212. In Figure 13 , the classification of thermal reactivity is as follows: low reactivity is located in well S; medium reactivity is located in well T, conversion curve 1202, conversion curve 1203 and conversion curve 1205 of well S, and conversion curve 1201 and conversion curve 1213 of well M; high reactivity is located in conversion curve 1212, conversion curve 1214, conversion curve 1215, conversion curve 1216 of well S and well M.

[0097] In one or more embodiments, based on Figures 9-13 The results show that the maturity sequence evaluated by the present invention is the same as that evaluated by T max The slope of the trend line is consistent with the assessment of graptolite reflectivity. The thermal reactivity evaluated by the present invention is consistent when evaluated using a conversion curve generated in Kinetics 2015 software. This method and system provides a classification of the reactivity of source rocks of varying maturity.

[0098] Figure 14 、 Figure 15 and Figure 16 The test of the method and system is shown to be implemented with reference to two different data sets. In this case, the measured kinetics are compared with published kinetics. The source rock samples measured are marine source rock formations in Saudi Arabia (i.e., Type II kerogen). In this case, two samples from well T and well S were used. The published kinetics are all source rocks with reported kinetic parameters published by Tegelaar and Noble (1994). Based on Figure 14 and other geochemical parameters (Tegelaar and Noble, 1994), the kerogen types were determined to be Type I (TI) / Type I-Sulfur (TIS), Type II (TII) / Type II Sulfur (TII-S), and Type III (TIII). The kinetic parameters can be obtained in the kinetic editor of PetroMod. Maturity indicates that based on Rock-Eval HI and T max Data, except for the Barnett Shale, all samples are immature, such as Figure 14 Based on Figure 14 The estimated Ro of the Barnett Shale is greater than 0.70%, which is determined by VRE and T max The empirical formula for the VRE (Hackley and Cardott, 2016) indicates a VRE of 0.96%; although a lower Ro value of 0.53% was reported by Tegelaar and Noble (1994). The pyrolysis apparatus was similar to the open system measured in the HAWK published on the 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 with intervals of 1 kcal / mol. The geological heating rate used for extrapolation (i.e., generating the conversion curves) was 1°C / Ma.

[0099] In one or more embodiments, Figure 14An evaluation is shown through the conversion curves generated in PetroMod software. In this case, the thermal reactivity classification is 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 can be a mature sample, so its kinetics and reactivity are not comparable to the other source rocks (i.e., immature samples). Due to the effect of maturity on the distribution of Ea, the conversion curve of the immature Barnett shale should be shifted to the left, and its reactivity should be located in the middle of the Type II kerogen sequence.

[0100] In one or more embodiments, the classification of thermal reactivity can be any number, as the classification of reactivity can be made based on the conversion 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 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 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 evaluation is shown that indicates thermal maturity, which includes an exponential trend line plotted for all published data, showing a clear linear relationship between WA-Ea and Log(A), which is consistent with maturity evaluation in general. In this case, the Barnett shale is a mature sample, so its kinetics and reactivity are not comparable to the other source rocks (i.e., immature samples). Due to the effect of maturity on the distribution of Ea, the conversion curve of the immature Barnett shale should be shifted to the left, and its reactivity should be located in the middle of the Type II kerogen sequence. Figure 15In this case, the thermal reactivity for Type I and Type III is: T. Akar (TI) > Mae Sot (TI) > Ribesalbes (TIS) > La Luna (TI) > Green River (TI) > Mannville (TI) > Tasmanites (TI); and 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 for the Barnett Shale are not comparable due to its maturity.

[0102] In this case, the thermal reactivity for Type I and Type III is: T. Akar (TI) > Mae Sot (TI) > Ribesalbes (TIS) > La Luna (TI) > Green River (TI) > Mannville (TI) > Tasmanites (TI); and 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 for the Barnett Shale are not comparable due to its maturity. Figure 15 In this case, the thermal reactivity for Type I and Type III is: T. Akar (TI) > Mae Sot (TI) > Ribesalbes (TIS) > La Luna (TI) > Green River (TI) > Mannville (TI) > Tasmanites (TI); and 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 for the Barnett Shale are not comparable due to its maturity.

[0103] In this case, the thermal reactivity for Type I and Type III is: T. Akar (TI) > Mae Sot (TI) > Ribesalbes (TIS) > La Luna (TI) > Green River (TI) > Mannville (TI) > Tasmanites (TI); and 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 for the Barnett Shale are not comparable due to its maturity. Figure 14 and Figure 15 A very similar reactivity ranking is provided for source rocks at the same maturity level (in the case of "immature"). In some embodiments, the method and system use WA-Ea instead of the Ea distribution in the geological extrapolation, which will only quantify the average kinetic behavior and reactivity of source rocks in the main hydrocarbon generation window. This simplification is advantageous for the kinetic parameters used in the reactivity evaluation, but can lose details of the oil-macerals conversion start and / or later stages (e.g. Figure 14 as shown when TR < 10% and TR > 90%).

[0104] Figure 14Rock-Eval HI versus T max crossplot, showing kerogen types and their maturation trends. In Figure 14 and Figure 15 Two samples of marine source rocks (Type II) from Saudi Arabia were measured in the test described 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 the Barnett Shale. The estimated Ro for the Barnett Shale should be greater than 0.70% based on the graph, although the authors reported a relatively low Ro value of 0.53%. The overall kinetic parameters for the 13 source rocks are available in PetroMod.

[0105] Figure 15 Conversion curves generated in the PetroMod software applying discrete kinetic models (common A and Ea discrete distribution) and geological heating rates (1 °C / Ma) are shown. Figure 15 It is shown that the comparison of conversion curves (i.e. conversion sequence) is the current solution to evaluate reactivity. In this case, from left to right, the conversion of kerogen to hydrocarbons requires higher temperatures and becomes more difficult, indicating a decrease in the reactivity of the source rock. For Type I (solid curves) and Type III source rocks, reactivity: T. Akar (TI) > Mae Sot (TI) > Ribesalbes (TIS) > La Luna (TI) > Green River (TI) > Mannville (TIII) > Tasmanites (TI); for Type II source rocks (dashed curves): Monterey (TIIS) > Bakken (TII) > Kimmeridge (TII) > Arabia Well S (TII) > Arabia Well T (TII) > Woodford (TII) > Pematang (TII) > Barnett (TII). In addition, the Barnett Shale used in the analysis can be a mature sample. The numbers next to the curves are an arbitrary ranking of reactivity based on the conversion sequence, for Type I & III and Type II, respectively. In this case, [1.0] is the most reactive source rock, while [7.0] is the most stable source rock in this case.

[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 Figure 13The maturity assessments for the three types of source rocks are consistent (i.e., all except Barnett are immature). 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 (rhombus) 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 flow chart according to one or more embodiments is shown. Specifically, Figure 17 Methods for evaluating reactivity in source rock evaluation are described. In some embodiments, the method may be used with reference to Figure 3 The control system 360 of the collection system 300 is implemented as described above. Figure 17 One or more boxes in Figures 1-3 Although Figure 17 The various blocks in the embodiment are presented and described in order, but those skilled in the art will appreciate that some or all of these blocks may be executed in a different order, may be combined or omitted, and may be executed in parallel. Furthermore, these blocks may be executed actively or passively.

[0108] In block 1710, information related to various source rock samples is obtained from an area of ​​interest (AOI). Figures 1-3 The information can be obtained by collecting the source rock samples using the collection tool 160 described in the previous section. The source rock samples can be collected and tested at the same location using an in-situ laboratory equipment chamber that can be similar to the reference Figure 3 The laboratory equipment room discussed herein may additionally or alternatively test the sample at a location remote from the location where the source rock sample was collected.

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

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

[0111] In block 1740, a kinetic dataset is created, including all measured kinetic parameters, published kinetic parameters, and archived kinetic parameters. The various kinetic parameters correspond to the AOI. As Figures 4-13 shown, the various kinetic parameters are collected, compiled, and compared to determine changes in the evaluation of kinetic parameters from the information obtained. As Figure 5 described with reference to FIG. 5, the open system pyrolysis 510 can be followed by heating rate experiments 530 to generate pyrolysis data for deriving 540 kinetic parameters that are used for characterization 550 of kinetic parameters and evaluation 570 of kinetics. The pyrolysis data can be processed in kinetic analysis software (e.g., Kinetics2000, Kinetics05, Kinetics2015) or manual regression and parameter fitting to derive 540 the kinetic parameters.

[0112] In block 1750, the weighted average Ea is calculated using equation (2), and all pairs of WA-Ea and log A are plotted on a crossplot. The weighted average of the kinetic parameters 540 / 624a- 624c is performed to generate a crossplot in the manner discussed with respect to FIG. 6. As described above, the crossplot of the weighted average of the kinetic parameters 552 or 626a-626c can be plotted on a logarithmic scale to evaluate the thermal reactivity and maturity of the source rock. In Figures 8-1 0, the crossplot can be used to estimate the maturity of a new source rock sample based on its derived kinetic parameters 552 or 626a-626c, since the curved arrows indicate the direction of increasing maturity. Figure 8 In block 1760, a maturity line is determined in the crossplot. The kinetic data (WA-Ea and A) are grouped by well or maturity range. For data from one well or a certain maturity range, an exponential trend line (maturity line) representing the change in source rock kinetics at the same maturity level can be generated in the crossplot. Since there is a systematic shift in the distribution of Ea as maturity increases, the trend line can show a clockwise increase in maturity. In this case, the multiple source rock samples are plotted to visually identify the maturity of these samples relative to each other.

[0113] In block 1770, the reactivity is evaluated at the same maturity level, and the reactivity is ranked for different maturity. As

[0114] In block 1770, the reactivity is evaluated at the same maturity level, and the reactivity is ranked for different maturity. As Figure 8As shown, the arrow of the maturity line indicates a decrease in reactivity at the same maturity level, and reactivity can be ranked across maturity lines for different maturity hydrocarbon source rocks. The graphical representation of the hydrocarbon source rock kinetic data provides a quick assessment of reactivity without the need for complex calculations and basin modeling.

[0115] In block 1780, the complex format of the kinetic parameters is converted to a single reactivity variable for the hydrocarbon source rock. This variable is easier to use than the common kinetic parameters in the evaluation and characterization of the hydrocarbon source rocks in the area of interest.

[0116] Figure 18 A flowchart in accordance with one or more embodiments is shown. In particular, Figure 18 A method for evaluating reactivity in hydrocarbon source rock evaluation to improve the selection and distribution of kinetic parameters in basin modeling is described. In some embodiments, the method can use the collection system 300 described with reference to Figure 3 The control system 360 of the collection system 300 described can be implemented. Further, Figure 18 One or more blocks in the method 1800 can be performed by one or more components as described in Figures 1-3 Although the various blocks in the method 1800 are presented and described in a particular order, one skilled in the art will recognize that some or all of these blocks can be performed in a different order, can be performed at the same time or concurrently, or can be omitted, and that some or all of these blocks can be performed by different components or a different number of components. Moreover, one skilled in the art will recognize that some or all of the blocks in the method 1800 can be performed actively or passively, or can be performed by a combination of active and passive components. Figure 18

[0117] In block 1805, information related to various hydrocarbon source rock samples is obtained for the AOI. As described with reference to block 1710, the hydrocarbon source rock samples can be collected and tested at the same location using an on-site laboratory facility room that can be similar to the laboratory facility room discussed with reference to Figure 3 The samples can additionally or alternatively be tested at a location that is remote from the location where the hydrocarbon source rock samples were collected.

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

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

[0120] ​In block 1820, a kinetic dataset is created, including all measured kinetic parameters, published kinetic parameters, and archived kinetic parameters. Specifically, as described with reference to block 1740 and as described with reference to Figure 5 After open system pyrolysis 510, heating rate experiments 530 can be performed to generate pyrolysis data for deriving 540 kinetic parameters, which are used for characterization 550 of kinetic parameters and kinetic evaluation 570, as described with reference to

[0121] In block 1825, as described with reference to block 1750, the weighted average Ea is calculated using equation (2), and all pairs of WA-Ea and log A are plotted on an intersection plot. Weighted averages of kinetic parameters 540 or 624a-624c are performed to generate an intersection plot in the manner discussed with respect to Figures 8-1 As described above, the intersection plot of weighted averages 552 or 626a-626c of kinetic parameters can be plotted on a logarithmic scale to evaluate thermal reactivity and maturity of a hydrocarbon source rock. In Figure 8 In block 1825, as described with reference to block 1750, the weighted average Ea is calculated using equation (2), and all pairs of WA-Ea and log A are plotted on an intersection plot. Weighted averages of kinetic parameters 540 or 624a-624c are performed to generate an intersection plot in the manner discussed with respect to

[0122] In block 1830, as described with reference to block 1760, a maturity line is determined in the intersection plot. Kinetic data (WA-Ea and A) are grouped by well or maturity range. For data from one well or a certain maturity range, an exponential trend line (maturity line) representing the variation of source rock kinetics at the same maturity level can be generated in the intersection plot. Since there is a systematic shift in the distribution of Ea with increasing maturity, the trend line can 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 block 1835, as described with reference to block 1770, reactivity is evaluated at the same maturity level, and reactivity is ranked for different maturity. As shown in Figure 8 The arrow of the maturity line indicates a decrease in reactivity at the same maturity level, and reactivity can be ranked across the maturity line for source rocks of different maturity. Generating a graphical representation of source rock kinetic data provides a quick evaluation of reactivity without the need for complex calculations and basin modeling.

[0124] Further, in block 1835, the thermal reactivity of the source rock is ranked at different thermal maturities. The ranking curve can be refined by measuring the kinetic parameters 540 or 622 of a series of source rock samples that have undergone different degrees of artificial maturation. As described above, more ranking can be performed to establish a kinetic model of the source rock with more detail. The reactivity or ranking of reactivity can provide a new variable or dimension beyond the parameters routinely measured for source rock evaluation (i.e., quantity, quality, and thermal maturity). For example, source rock samples with higher reactivity can begin to generate hydrocarbons and reach a peak of generation before source rocks with lower reactivity.

[0125] In block 1840, a source rock formation model and facies are defined based on all the information available in the TOC, Rock-Eval parameters, and AOI. The defined formation model and facies are used to represent the geological heterogeneity and organic geochemical characteristics of the source rock, supporting the distribution of multiple kinetics in different units of the source rock formation.

[0126] In block 1845, the kinetic parameters in the facies of the source rock formation are classified according to the reactivity ranking and maturity trend of the crossplot. As described with reference to blocks 1825-1835 and Figure 13 As described, the kinetic parameters of different units in each facies of the source rock formation are processed to evaluate their reactivity. Thus, the kinetic parameters of source rock units with different maturities can be classified based on their facies and ranking of reactivity.

[0127] In block 1850, based on the classification of the kinetic parameters, the kinetic parameters derived from immature source rock units are assigned to the mature source rock units in the source rock formation. In a facies, if a thermally mature source rock unit and an immature source rock unit are in the same reactivity ranking, the thermally mature source rock unit can share the kinetic parameters derived from the immature source rock unit. In the prior art of basin modeling, the kinetic parameters derived from immature source rock samples are used as the kinetic representative of the whole source rock formation in the basin, which is then used to simulate the hydrocarbon generation and expulsion in the petroleum system derived from the source rock. As described above with reference to Figure 9 , FIG. 11, and FIG. 12, because there are considerable regional and vertical variations in the kinetics of a single source rock (even at the same well location), using a single set of kinetic parameters and directly assigning the kinetic parameters derived from the source rock to those mature units of the source rock cannot explain the variations in facies and kinetics in the whole formation. In this regard, the ranking of reactivity allows the kinetic parameters derived from immature source rock samples to be assigned to those mature units of the source rock. In a facies of the source rock, the kinetics of the immature source rock units represent the best kinetics of the mature source rock units in the same reactivity ranking.

[0128] In block 1855, reactivity is evaluated to improve the selection and assignment of kinetic parameters for basin modeling. As described above, the present invention describes a 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 formations in a sedimentary basin. Multiple kinetic parameters are introduced into the source rock model to account for the heterogeneity of organic matter in the source rock, as well as the vertical and lateral variations in kinetics. In this regard, by using better kinetic representations and multiple kinetic parameters, hydrocarbon generation and expulsion from petroleum systems can be improved, resulting in 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, control system 360 can be a computer system located at a remote location, such that the collected data is processed away from the surface 370. In some embodiments, the computing system can be implemented on a remote or handheld device (e.g., a laptop, 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 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 memories 1906 (e.g., a hard disk), a communication interface 1908 (a transmitter and / or a 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 touch screen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device. In some embodiments, the one or more input devices 1920 may be reference Figure 1 and Figure 3 The computing system 1900 may also include one or more output devices 1910, such as a screen (e.g., a liquid crystal display (LCD), a plasma display, or a touch screen), 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, the input device 1920 can be coupled to a receiver and a transmitter for exchanging communications with one or more peripheral devices connected to the network system 1930. The receiver can receive information related to one or more source rock samples. The transmitter can relay the information received by the receiver to other components in the computing system 1900. In addition, the computer processor 1904 can be configured to execute or assist in implementing the reference Figure 18 and / or Figure 19 The process described.

[0132] In addition, one or more elements of the computing system 1900 described above may be located at a remote location and connected to the other elements via a network system 1930. The network system 1930 may be a cloud-based interface that performs processing at a remote location away from the well site and is connected to the other elements via a network. In this case, the computing system 1900 may be connected via a remote connection established using a 5G connection (e.g., protocols established in Release 15 and subsequent releases of the 3GPP / New Radio (NR) standard).

[0133] Figure 19 The computing system in the embodiment may implement a data repository and / or be connected to a data repository. For example, one type of data repository 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 the following: Figures 1-18 Describes the methods and systems associated with published / measured data.

[0134] Although Figures 1-19 Various configurations of components are shown, but other configurations may be used without departing from the scope of this disclosure. For example, Figures 1-3 The various components in the EMBODIMENTS 2000 may be combined to create a single component. As another example, the functionality performed by a single component may be performed by two or more components.

[0135] While the present disclosure has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this disclosure, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as disclosed herein. Accordingly, the scope of the present disclosure should be limited only by the claims appended hereto.

Claims

1. A method for evaluating reactivity in source rock evaluation, the method comprising: collecting a plurality of source rock samples using a collection tool, the collection tool comprising: a sampling system configured to collect and store the plurality of source rock samples, a sensing system configured to collect physical data from the plurality of source rock samples, a processing system configured to perform computational processes and store the physical data related to the plurality of source rock samples, and a communication system configured to receive and transmit signals regarding the physical data related to the plurality of source rock samples; obtaining information related to the plurality of source rock samples (140a; 140b; 140c) using a receiver; determining, using a processor coupled to the receiver, thermal reactivity of source rocks corresponding to the plurality of source rock samples (140a; 140b; 140c) that are at the same thermal maturity level in a region of interest, wherein the thermal reactivity is chemical reactivity under thermal stress; interpreting, using the processor, kinetic parameters derived from the plurality of source rock samples (140a; 140b; 140c) through pyrolysis experiments; and converting, using the processor, a complex format of kinetic parameters into the thermal reactivity of a single variable for implementing source rock evaluation and characterization of the region of interest through an intersection plot of weighted average Ea and frequency factor A on a logarithmic scale.

2. The method of claim 1, further comprising: comparing published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of the region of interest.

3. The method of claim 1, further comprising: estimating thermal maturity of each of the plurality of source rock samples (140a; 140b; 140c) based on the interpreted kinetic parameters.

4. The method of claim 1, wherein, The interpreted kinetic parameters are kinetic parameters derived from thermally mature source rock samples (140a; 140b; 140c).

5. The method of claim 1, wherein, The weighted average of the frequency factor A and Ea distribution is related to the gas constant R and the reaction temperature T.

6. The method of claim 5, wherein, The frequency factor A, the weighted average of the Ea distribution, the gas constant R and the reaction temperature T are input into the basin modeling equation k = A e-E a / RT.

7. A system for evaluating reactivity in source rock evaluation, the system comprising: a collection tool configured to collect a plurality of source rock samples, the collection tool comprising: a sampling system configured to collect and store the plurality of source rock samples, a sensing system configured to collect physical data from the plurality of source rock samples, a processing system configured to perform computational processes and store the physical data related to the plurality of source rock samples, and a communication system configured to receive and transmit signals regarding the physical data related to the plurality of source rock samples; a receiver (214) that receives information related to a plurality of source rock samples (140a; 140b; 140c); a processor (222; 1904) that: determining a thermal reactivity of source rocks corresponding to the plurality of source rock samples (140a; 140b; 140c) that are at the same thermal maturity level in the area of interest, wherein the thermal reactivity is a chemical reactivity under thermal stress, interpreting kinetic parameters derived from the plurality of source rock samples (140a; 140b; 140c) through pyrolysis experiments; and converting the complex format of kinetic parameters into the thermal reactivity of a single variable for implementing source rock evaluation and characterization of the area of interest through the intersection plot of weighted average Ea and frequency factor A on a logarithmic scale.

8. The system of claim 7, wherein, The processor (222; 1904) compares published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of the area of interest.

9. The system of claim 7, wherein, The processor (222; 1904) estimates a thermal maturity of each of the plurality of source rock samples (140a; 140b; 140c) based on the interpreted kinetic parameters.

10. The system of any one of claims 7 to 9, wherein, The interpreted kinetic parameters are kinetic parameters derived from thermally mature source rock samples.

11. The system of claim 7, wherein, The weighted average of the frequency factor A and Ea distribution is related to the gas constant R and the reaction temperature T.

12. The system of claim 11, wherein, The frequency factor A, the weighted average of the Ea distribution, the gas constant R and the reaction temperature T are input into the basin modeling equation k = A e-E a / RT.

13. A non-transitory computer-readable medium storing instructions executable by a computer processor (222; 1904), the instructions comprising the following functions: collecting a plurality of source rock samples using a collection tool comprising: a sampling system configured to collect and store the plurality of source rock samples, a sensing system configured to collect physical data from the plurality of source rock samples, a processing system configured to perform computational processes and store the physical data related to the plurality of source rock samples, and a communication system configured to receive and transmit signals regarding the physical data related to the plurality of source rock samples; obtaining information related to the plurality of source rock samples (140a; 140b; 140c); determining a thermal reactivity of source rocks corresponding to the plurality of source rock samples (140a; 140b; 140c) that are at the same thermal maturity level in the area of interest, wherein the thermal reactivity is a chemical reactivity under thermal stress, interpreting kinetic parameters derived from the plurality of source rock samples (140a; 140b; 140c) through pyrolysis experiments; and converting the complex format of kinetic parameters into the thermal reactivity of a single variable for implementing source rock evaluation and characterization of the area of interest through the intersection plot of weighted average Ea and frequency factor A on a logarithmic scale.

14. The non-transitory computer-readable medium of claim 13, the instructions further comprising the following functions: comparing published kinetic parameters, archived kinetic parameters, and measured kinetic parameters of the area of interest.

15. The non-transitory computer-readable medium of claim 13, the instructions further comprising the following functions: estimating a thermal maturity of each of the plurality of source rock samples (140a; 140b; 140c) based on the interpreted kinetic parameters.

16. The non-transitory computer-readable medium of any one of claims 13-15, wherein, The kinetic parameters explained are derived from thermally mature source rock samples. The kinetic parameters explained are derived from thermally mature source rock samples.

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