Patent Application
20240337643
The present disclosure relates to differentiate the geological age of liquid hydrocarbons (also referred to herein as oil). The geological age of a liquid hydrocarbon, as used herein, refers to the depositional age of the source rock from which the liquid hydrocarbons are derived.
Source rocks are organic rich sediments and are the origin of hydrocarbons accumulated in the subsurface. Source rocks are often not penetrable because such rocks are at a great depth. However, sedimentary organic matter signatures preserved in source rocks is transformed into hydrocarbons with increasing temperature and pressure. As illustrated in
Analogous to physical fossils preserved in sediments, oils contain numerous chemical compounds encoding the generative source rock information. More specifically, when an organism fossilizes the physical fossil remains in the source rock and chemicals associated with said physical fossil (or chemical fossils) can leach into the oil. Therefore, just as physical fossils are aged, the chemical fossils can be used to correlate the migrated hydrocarbons to the deeply buried source rocks, from which the oils are derived. Such chemical fossils are also known as age biomarkers.
For example, angiosperm plants have flowers and produce seeds enclosed within a carpel. As the earth evolved through the Late Cretaceous (100-65 million years before present, or 100-65 Ma BP) epoch through the Paleocene (65-55 Ma BP), Eocene (55-34 Ma BP), Oligocene (34-23 Ma BP), and Miocene (23-5 Ma BP) epochs, angiosperm progressively dominated the higher plant community. Therefore, the presences of angiosperm-specific biomarkers as chemical fossils of angiosperm in oil can be used to constrain the oil age of Late Cretaceous or younger (i.e., <100 Ma BP). More specifically, oleanane is one of the most commonly used angiosperm-specific biomarkers because oleanane survives degradation and is easily analyzed using gas spectroscopy-mass spectroscopy (GC-MS) to identify the presence and concentration of oleanane. For age differentiation, a relative concentration of oleanane (defined as the concentration ratio of oleanane to hopane (H30)) higher than 0.03 indicates an age of Cretaceous-Tertiary, or <100 Ma BP as described in “The Molecular Fossil Record of Oleanane and Its Relation to Angiosperms,” Science 635, 768-777 (1994). However, an age resolution of <100 Ma BP based on the presence of oleanane is often insufficient to address the hydrocarbon origin in many petroleum basins and makes pinpointing the stratigraphic depth of the corresponding source rocks almost impossible.
The present disclosure relates to age differentiation of hydrocarbon samples using a chemical fossil assemblage approach
A nonlimiting example method of the present disclosure may comprise: determining a source facies for a hydrocarbon sample; inputting the source facies into a chemical fossil assemblage model; determining, using the chemical fossil assemblage model, one or more candidate chemical fossil assemblages and corresponding age biomarkers for the hydrocarbon sample based on the source facies; measuring a concentration or a related value of corresponding age biomarkers in the hydrocarbon sample to yield an age biomarker fingerprint; inputting the age biomarker fingerprint into a chemical fossil assemblage model; comparing the age biomarker fingerprint to the one or more candidate chemical fossil assemblages using the chemical fossil assemblage model; and estimating an age of the hydrocarbon sample based on the comparison.
Another nonlimiting example method of the present disclosure may comprise: determining a source facies for a hydrocarbon sample; measuring a concentration or a related value of age biomarkers in a hydrocarbon sample to yield an age biomarker fingerprint; inputting the source facies and the age biomarker fingerprint into a chemical fossil assemblage model; comparing the age biomarker fingerprint to one or more facies-dependent chemical fossil assemblages using the chemical fossil assemblage model; and estimating an age of the hydrocarbon sample based on the comparison.
A nonlimiting example system of the present disclosure may comprise: a computing device comprising: a processor; a memory coupled to the processor; and instructions provided to the memory, wherein the instructions are executable by the processor to perform the chemical fossil assemblage model steps of either or both of the methods.
The following figures are included to illustrate certain aspects of the embodiments, and should not be viewed as exclusive embodiments. The subject matter disclosed is capable of considerable modifications, alterations, combinations, and equivalents in form and function, as will occur to those skilled in the art and having the benefit of this disclosure.
To facilitate a better understanding of the embodiments of the present invention, the examples of preferred or representative embodiments are given within the detailed description. In no way should the following examples be read to limit, or to define, the scope of the invention.
The present disclosure relates to age differentiation of hydrocarbon samples using a chemical fossil assemblage approach. More specifically, the chemical fossil assemblage approach includes a correlation to the concentration of two or more chemical fossils (also referred to as age biomarkers) to source rock age. Said correlation may be facies-dependent. That is, the facies describes the rock characteristics imparted by the formation, composition, and fossil content of said rock. Examples of facies include, but are not limited to, marine carbonate and marly facies, marine clastic and deltaic facies, paralic facies, lacustrine facies, terrestrial and coaly facies, paralic facies, and the like.
Generally, the methods and systems described herein include determining the facies of the source rock (also referred to herein as source facies) for a hydrocarbon sample and then aging the hydrocarbon sample based on the concentration of chemical fossil (also referred to herein as age biomarkers) in the hydrocarbon sample using predictive models trained using a database of known sample ages and chemical fossil compositions. Without being limited by theory, it is believed that concentration of an age biomarker in a hydrocarbon sample depends on, among other things, the source facies and the age of the source rock. The methods and systems described herein advantageously account for such dependencies, which may provide higher accuracy of aging hydrocarbon samples.
The systems and methods described herein may be able to age hydrocarbon samples using the chemical fossil assemblage approach. For example, samples may be differentiated into various epochs including, but not limited to, Upper Triassic (237-200 Ma BP), Lower Jurassic (200-175 Ma BP), Middle Jurassic (175-161 Ma BP), Upper Jurassic epoch (161-145 Ma BP), Lower Cretaceous (145-100 Ma BP), Late Cretaceous (100-65 Ma BP), Paleocene (65-55 Ma BP), Eocene-Oligocene (55-23 Ma BP), and Miocene and younger (23-0 Ma BP), and the like, and stages (sub-epochs) thereof, and any combination of abutting epochs. Examples of stages include, but are not limited to, Tithonian (152-145 Ma BP) and Kimmeridgian (157-152 Ma BP). For example, the systems and methods described herein using the chemical fossil assemblage approach may differentiate the age of a hydrocarbon sample between Upper Triassic (237-200 Ma BP), Lower Jurassic (200-175 Ma BP), and Middle/Upper Jurassic (175-145 Ma BP). In another example, the systems and methods described herein using the chemical fossil assemblage approach may differentiate the age of a hydrocarbon sample between Upper Triassic (237-200 Ma BP), Lower Jurassic (200-175 Ma BP), Middle Jurassic (175-161 Ma BP), Upper Jurassic epoch (161-145 Ma BP), Lower/Late Cretaceous (145-65 Ma BP), Paleocene (65-55 Ma BP), Eocene-Oligocene (55-23 Ma BP), and Miocene and younger (23-0 Ma BP). In yet another example, the systems and methods described herein using the chemical fossil assemblage approach may differentiate the age of a hydrocarbon sample between Jurassic or older epoch (237-145 Ma BP), Lower Cretaceous (145-100 Ma BP), Late Cretaceous (100-65 Ma BP), Paleocene (65-55 Ma BP), Eocene (55-34 Ma BP), and Oligocene/Miocene epoch (34-5 Ma BP).
Because crude oils are a complex mixture of natural products, the oil samples may need to be fractionated to remove compounds that interfere the detection of age biomarkers.
Severely-biodegraded samples 306 may not be further analyzed by the chemical fossil assemblage approach.
Un-biodegraded to moderately-biodegraded samples 308 (e.g., having a labile organic matter of 8 to 11) may proceed to further analysis for age differentiation by the chemical fossil assemblage approach. The next step in the illustrated flowchart 300 is fractionation 310 into a plurality of fractions 312, 314, 316, and 318. For example, the asphaltenes 318 may be extracted from the sample 308 using n-pentane. The remaining portion of the sample 308 may be separated into saturates fraction 312, aromatic fraction 314, and polar fraction 316 using high-pressure liquid chromatography (HPLC). The saturates fraction 312 may be analyzed by gas chromatography-isotope ratio mass spectrometry (GC-irMS) 320, gas chromatography/mass spectrometry (GC/MS) 322, and gas chromatography/mass spectrometry tandem mass spectrometry (GC/MS/MS) 324 to ascertain at least a portion of the identity and concentration of chemical fossils (or age biomarker fingerprint) 328 in the saturates fraction 312. The aromatics fraction 314 from the HPLC separation is also analyzed by GC-MS 326 to ascertain at least a portion of the identity and concentration of chemical fossils (or age biomarker fingerprint) 328 in the aromatics fraction 314.
Other secondary analyses (not illustrated) may be performed on the original sample 302 and/or fractions 312, 314, 316, and 318 thereof to ascertain a level of biodegradation, a level of contamination, sample maturity, sulfur content, American Petroleum Institute (API) gravity, the structures of chemical fossils, and other characteristics of the hydrocarbon sample. One skilled in the art will recognize suitable analysis techniques for measuring such characteristics. Nonlimiting examples of analysis technique include: GCxGC-TOFMS (time of flight mass spectroscopy), time of flight mass spectroscopy, whole oil gas chromatography, 13C isotopic composition, full-scan gas chromatography/mass spectroscopy, infrared-gas chromatography/mass spectroscopy, C4 to C19 gas chromatography, and the like, and any combination thereof.
Any of the foregoing analytical techniques can be replaced or augmented with comparable techniques known to one of skill in the art.
As described further herein, the chemical fossil assemblage approach may use predictive models trained using data from samples with known age and source rock and measured at least a portion of identity and concentration of chemical fossils (or age biomarker fingerprint). Said age biomarker fingerprint data may be collected using the description in
For example,
The characteristics 406 (e.g., facies, maturity, at least a portion of an age biomarker fingerprint, and other characteristics) of the hydrocarbon sample 402 may then be input to a chemical fossil assemblage model 408.
A chemical fossil assemblage model (or approach) of the present disclosure may be a predictive model (e.g., a random forest decision tree, a neural network, or the like) that is trained using data for hydrocarbon samples having known a known age and at least following characteristic known: source rock, maturity, at least a portion of an age biomarker fingerprint, and other characteristics.
Random Forest classifier may be used to predict the age of crude oils. The distribution of age biomarker assemblages as well as maturity and facies data in samples with known ago are used as predictors that formulate the training dataset for supervised machine learning. Each decision tree is subsequently grown on a bootstrap sample of the training dataset. A bootstrap sample is a random sample draw from the training dataset with replacement. For a classification or regression tree, the Mean Decrease Gini (MDG) is applied for classification algorithm that searches all possible split of predictors to best predict the response (i.e., known age of the oil in the training sample set). The predictions of all trees are finally aggregated through majority voting. The importance of predictors may be evaluated by the out-of-bag error or impurity. Prior to application, the accuracy of Random Forest prediction is assessed by validation sample set.
Referring back to
The characteristics 506 (e.g., facies, maturity, and other characteristics) of the hydrocarbon sample 502 may then be input to a chemical fossil assemblage model 508. In this example, the age biomarker fingerprint is not yet considered by the chemical fossil assemblage model 508. The chemical fossil assemblage model 508 may output, based on the at least the facies and maturity, the age biomarkers 510 best suited for ascertaining an age of the hydrocarbon sample with unknown age 502. That is, the chemical fossil assemblage model 508 may determine which chemical fossils (or age biomarkers) need to be analyzed for abundance based on the characteristics 506. For example, an age biomarker present in marine carbonate and marly facies may not be present at all or in significant quantities for age differentiation when considering paralic facies. Accordingly, if the facies is determined to be a paralic facies, the chemical fossil assemblage model 508 may not tell a user to analyze the hydrocarbon sample with unknown age 502 for said age biomarker. Whereas, if the facies is determined to be a marine carbonate and marly facies, the chemical fossil assemblage model 508 may tell a user to analyze the hydrocarbon sample with unknown age 502 for said age biomarker.
Once the age biomarkers 510 are designated by the chemical fossil assemblage model 508, the age biomarker fingerprint 514 or a portion thereof may be measured 512 based on the age biomarkers 510 designated by the chemical fossil assemblage model 508. In some instances, some or all of the age biomarker fingerprint 514 may have been previously measured. Accordingly, if needed, additional aspects of the age biomarker fingerprint 514 may be measured based on the age biomarkers 510 designated by the chemical fossil assemblage model 508. Then, the age biomarker fingerprint 514 may be input to the chemical fossil assemblage model 508 to estimate one or more ages 516 (or produce one or more estimated ages 516) for the hydrocarbon sample with unknown age 502 based on the analysis of the characteristics 506 and age biomarker fingerprint 514 of said sample 502. Each estimated age 516 may have a corresponding value that relates to the probability or other metric that indicates how likely the estimated age 516 is correct.
Each age biomarker concentration is illustrated in
A nonlimiting example method of the present disclosure may include inputting the characteristics like facies, optionally maturity, and age biomarker fingerprint for a hydrocarbon sample into a chemical fossil assemblage model described herein. The chemical fossil assemblage model may use the facies and maturity to select the chemical fossil assemblage of
In another nonlimiting example method, the facies and optionally maturity may be input to the chemical fossil assemblage model.
Various aspects of the systems and methods described herein utilize computer systems. Such systems and methods can include a non-transitory computer readable medium containing instructions that, when implemented, cause one or more processors to carry out the methods described herein.
“Computer-readable medium” or “non-transitory, computer-readable medium,” as used herein, refers to any non-transitory storage and/or transmission medium that participates in providing instructions to a processor for execution. Such a medium may include, but is not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, NVRAM, or magnetic or optical disks. Volatile media includes dynamic memory, such as main memory. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, an array of hard disks, a magnetic tape, or any other magnetic medium, magneto-optical medium, a CD-ROM, a holographic medium, any other optical medium, a RAM, a PROM, and EPROM, a FLASH-EPROM, a solid state medium like a memory card, any other memory chip or cartridge, or any other tangible medium from which a computer can read data or instructions. When the computer-readable media is configured as a database, it is to be understood that the database may be any type of database, such as relational, hierarchical, object-oriented, and/or the like. Accordingly, exemplary embodiments of the present systems and methods may be considered to include a tangible storage medium or tangible distribution medium and prior art-recognized equivalents and successor media, in which the software implementations embodying the present techniques are stored.
The methods described herein can be performed using computing devices or processor-based devices that include a processor; a memory coupled to the processor; and instructions provided to the memory, wherein the instructions are executable by the processor to perform the methods described herein. The instructions can be a portion of code on a non-transitory computer readable medium. Any suitable processor-based device may be utilized for implementing all or a portion of embodiments of the present techniques, including without limitation personal computers, networks of personal computers, laptop computers, computer workstations, mobile devices, multi-processor servers or workstations with (or without) shared memory, high performance computers, and the like. Moreover, embodiments may be implemented on application specific integrated circuits (ASICs) or very large scale integrated (VLSI) circuits.
The methods and systems described herein may be useful in distinguishing the age of hydrocarbon samples. As such, the methods and systems described herein may provide a better understanding of the origin of oils, oil stains, or oil seeps, which allows one to de-risk petroleum basins and better differentiates exploration opportunities.
In a first nonlimiting example, the specific age of oils, oil stains, or oil seeps ties the samples to a specific stratigraphic interval (or source rock) where hydrocarbons are derived. As a result, hydrocarbon migration pathways from the source to trap can be illustrated. The migration pathway may determine whether source rocks can effectively charge the location (e.g., a trap, a basin, or the like) from which the hydrocarbon sample was obtained to form economic hydrocarbon accumulation. Therefore, in some instances, once the source rock and migration pathway has been determined, a wellbore may be drilled into the location (e.g., a trap, a basin, or the like) from which the hydrocarbon sample was obtained. When referring to location here, the exact location is not implied by rather a general location of the trap, the seep, the basin, or the like that would contain oil from the same source rock as the hydrocarbon sample.
In another nonlimiting example, the hydrocarbon generation timing can be calculated based on the specific oil age and the thermal history of the basin. The resultant generation timing is further compared with the timing of trap emplacement. If the trap is deposited earlier than hydrocarbon generation, the source rock is likely to charge the trap and it is considered highly risky to form economic hydrocarbon accumulation.
Other applications of aging and the benefits of better resolved aging per the methods and systems described herein will be apparent to those skilled in the art.
The new method is capable of distinguishing Late Cretaceous vs. Paleocene vs. Eocene or younger sourced oils. It improves age resolution compared with published age biomarker resolution of Late Cretaceous or younger. As such, the new method helps with better understanding of the origin of oils, oil stains, or oil seeps, which allows to de-risk petroleum basins and better differentiates exploration opportunities.
In a first nonlimiting example, the specific age of oils or oil stains, or oil seeps ties the samples to a specific stratigraphic interval where hydrocarbons are derived. As a result, hydrocarbon migration pathways from the source to trap can be illustrated. This is critical because the migration pathway determines whether source rocks can effectively charge the reservoir to form economic hydrocarbon accumulation.
In another nonlimiting example, the hydrocarbon generation timing can be calculated based on the specific oil age and the thermal history of the basin. The resultant generation timing is further compared with the timing of trap emplacement. If the trap is deposited earlier than hydrocarbon generation, the source rock is likely to charge the trap and it is considered highly risky to form economic hydrocarbon accumulation.
Other applications of aging and the benefits of better resolved aging per the methods and systems described herein will be apparent to those skilled in the art.
Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as molecular weight, reaction conditions, and so forth used in the present specification and associated claims are to be understood as being modified in all instances by the term “about.” Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the embodiments of the present invention. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claim, each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.
One or more illustrative embodiments incorporating the invention embodiments disclosed herein are presented herein. Not all features of a physical implementation are described or shown in this application for the sake of clarity. It is understood that in the development of a physical embodiment incorporating the embodiments of the present invention, numerous implementation-specific decisions must be made to achieve the developer's goals, such as compliance with system-related, business-related, government-related and other constraints, which vary by implementation and from time to time. While a developer's efforts might be time-consuming, such efforts would be, nevertheless, a routine undertaking for those of ordinary skill in the art and having benefit of this disclosure.
While compositions and methods are described herein in terms of“comprising” various components or steps, the compositions and methods can also “consist essentially of” or “consist of” the various components and steps.
Therefore, the present invention is well adapted to attain the ends and advantages mentioned as well as those that are inherent therein. The particular embodiments disclosed above are illustrative only, as the present invention may be modified and practiced in different but equivalent manners apparent to those skilled in the art having the benefit of the teachings herein. Furthermore, no limitations are intended to the details of construction or design herein shown, other than as described in the claims below. It is therefore evident that the particular illustrative embodiments disclosed above may be altered, combined, or modified and all such variations are considered within the scope and spirit of the present invention. The invention illustratively disclosed herein suitably may be practiced in the absence of any element that is not specifically disclosed herein and/or any optional element disclosed herein. While compositions and methods are described in terms of “comprising,” “containing,” or “including” various components or steps, the compositions and methods can also “consist essentially of” or “consist of” the various components and steps. All numbers and ranges disclosed above may vary by some amount. Whenever a numerical range with a lower limit and an upper limit is disclosed, any number and any included range falling within the range is specifically disclosed. In particular, every range of values (of the form, “from about a to about b,” or, equivalently, “from approximately a to b,” or, equivalently, “from approximately a-b”) disclosed herein is to be understood to set forth every number and range encompassed within the broader range of values. Also, the terms in the claims have their plain, ordinary meaning unless otherwise explicitly and clearly defined by the patentee. Moreover, the indefinite articles “a” or “an,” as used in the claims, are defined herein to mean one or more than one of the elements that it introduces.
This application claims the benefit of U.S. Provisional Patent Application 62/260,214 filed 12 Aug. 2021 entitled AGE DIFFERENTIATION OF CRUDE OILS USING CHEMICAL FOSSIL ASSEMBLAGE, the entirety of which is incorporated by reference herein.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2022/074108 | 7/25/2022 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63260214 | Aug 2021 | US |
