Synergistic sensing and prediction of source rock properties
阅读说明：本技术 烃源岩属性的协同感测与预测 (Synergistic sensing and prediction of source rock properties ) 是由 李伟昌 塞巴斯蒂安·丘陶克 大卫·雅可比 马克斯·德芬鲍 蒂凡妮·道恩·麦卡尔平 香农·李 于 2018-03-14 设计创作，主要内容包括：提供了用于感测和预测烃源岩属性的系统、设备和计算机实施的方法。这里公开了一种预测烃源岩的成熟度的方法,该方法包括：从放置在样本烃源岩附近的多个数据获取装置获得样本烃源岩的多个数据、以及使用预测相关性来分析所接收的数据以确定样本烃源岩的成熟度。通过应用机器学习模型将从多个代表性烃源岩获得的多个数据与多个代表性烃源岩的多个属性相关联来生成预测相关性。(Systems, devices, and computer-implemented methods for sensing and predicting properties of hydrocarbon source rock are provided. Disclosed herein is a method of predicting the maturity of a source rock, the method comprising: obtaining a plurality of data of a sample source rock from a plurality of data acquisition devices placed in proximity to the sample source rock, and analyzing the received data using the predicted correlation to determine maturity of the sample source rock. Predictive correlations are generated by applying a machine learning model to correlate a plurality of data obtained from a plurality of representative source rocks with a plurality of attributes of the plurality of representative source rocks.)
1. A computer-implemented method of determining maturity of a sample source rock, the method comprising the steps of:
establishing, by a data analysis engine, a communication link with a source rock database and a plurality of data acquisition devices positioned proximate to a sample source rock, the source rock database comprising a first plurality of data obtained from a plurality of representative source rocks and a plurality of attributes of the plurality of representative source rocks;
obtaining, by the data analysis engine, a second plurality of data of a sample source rock from the plurality of data acquisition devices; and
analyzing, by the data analysis engine, the second plurality of data using predictive correlations to determine maturity of the sample source rock,
wherein the predictive correlations are generated by the data analysis engine by applying a machine learning model to correlate the first plurality of data obtained from the plurality of representative source rocks with a plurality of attributes of the plurality of representative source rocks.
2. The computer-implemented method of claim 1, wherein the plurality of data acquisition devices comprises a spectrometer comprising a light source, a thermoelectric detector, and an assembly for reflecting light from the sample hydrocarbon source rock and directing the reflected light to the thermoelectric detector.
3. The computer-implemented method of claim 2, wherein the pyroelectric detector is integrated with a tunable filter.
4. The computer-implemented method of claim 2, wherein the component for reflecting light from the sample source rock and directing the reflected light to the pyroelectric detector is an attenuated total reflectance cell.
5. The computer-implemented method of claim 1 or claim 2, further comprising the steps of:
preparing the second plurality of data before the data analysis engine analyzes the second plurality of data by performing one or more of outlier detection, baseline correction, peak enhancement, and normalization.
6. The computer-implemented method of claim 1 or claim 2, further comprising the steps of:
storing, by the data analysis engine, the first plurality of data of sample source rocks and the determined maturity of the sample source rocks in a source rock database.
7. The computer-implemented method of claim 1 or claim 2, wherein the plurality of attributes of the plurality of representative source rocks comprises kerogen typing and elemental composition.
8. A computer-implemented method as in claim 1 or claim 2, wherein the first plurality of data comprises two or more of location data, spectral measurements, and optical measurements obtained from the plurality of representative source rocks.
9. The computer-implemented method of claim 8, wherein the spectral measurements include one or more of measurements obtained from fourier transform infrared spectroscopy, electron spin resonance spectroscopy, terahertz spectroscopy, and ultraviolet spectroscopy.
10. The computer-implemented method of claim 8, wherein the first plurality of data further comprises pyrolysis data.
11. The computer-implemented method of claim 10, wherein the pyrolysis data is obtained by Rock-
12. A computer-implemented method as in claim 1 or claim 2, wherein the second plurality of data comprises two or more of location data, spectral measurements, and optical measurements obtained from the sample source rock.
13. The computer-implemented method of claim 12, wherein the spectral measurements include one or more of measurements obtained from fourier transform infrared spectroscopy, electron spin resonance spectroscopy, terahertz spectroscopy, and ultraviolet spectroscopy.
14. The computer-implemented method of claim 12, wherein the optical measurements comprise one or more of measurements obtained by a fluorescence microscope and a confocal laser scanning microscope.
15. The computer-implemented method of claim 1 or claim 2, wherein the machine learning model is based on a support vector machine, Random
16. The computer-implemented method of claim 1 or claim 2, further comprising the steps of:
selecting spectral wavenumber bands for operation of the plurality of data acquisition devices in the vicinity of the sample source rock.
17. The computer-implemented method of claim 16, wherein the spectral wavenumber bands of the sample source rock are selected in response to receiving, by the data analysis engine, one or more selections of a desired maturity and a desired organic phase distribution of the sample source rock from a user interface.
18. A system for determining the maturity of a sample source rock, the system comprising:
a plurality of data acquisition devices placed in proximity to the sample source rock and communicatively coupled to the computing device;
the computing device is coupled to a source rock database via a communication network and is configured to:
obtaining a first plurality of data of a sample source rock from the plurality of data acquisition devices; and
analyzing the first plurality of data using predictive correlations to determine a maturity of the sample source rock,
wherein the predictive correlations are generated by applying a machine learning model to correlate a second plurality of data obtained from a plurality of representative source rocks with a plurality of attributes of the plurality of representative source rocks; and
the source rock database comprising the second plurality of data associated with the plurality of representative source rocks, a plurality of attributes of the plurality of representative source rocks, and the predicted correlation.
19. The system of claim 18, wherein the plurality of data acquisition devices are positioned to obtain data from an optimal sensing band of the sample source rock.
20. The system of claim 18 or claim 19, further comprising: a sample source rock retrieval device to obtain a portion of the sample source rock.
21. The system of claim 18 or claim 19, wherein the plurality of data acquisition devices are positioned to obtain two or more of position data, spectroscopic measurements, and optical measurements.
22. The system of claim 21, wherein the spectral measurements comprise one or more of measurements obtained from fourier transform infrared spectroscopy, electron spin resonance spectroscopy, terahertz spectroscopy, and ultraviolet spectroscopy.
23. The system of claim 21, wherein the optical measurements comprise one or more of measurements obtained by a fluorescence microscope and a confocal laser scanning microscope.
24. The system of claim 18 or claim 19, wherein the plurality of data acquisition devices comprises a spectrometer comprising a light source, a thermoelectric detector, and an assembly for reflecting light from the sample hydrocarbon source rock and directing the reflected light to the thermoelectric detector.
25. The system of claim 24, wherein the pyroelectric detector is integrated with a tunable filter.
26. The system of claim 24, wherein the component for reflecting light from the sample source rock and directing the reflected light to the pyroelectric detector is an attenuated total reflectance cell.
27. A system for determining the maturity of a sample source rock, the system comprising:
an in situ gas data acquisition device disposed proximate to the sample source rock and communicatively coupled to the computing device;
the computing device is coupled to a source rock database via a communication network and is configured to:
obtaining a first plurality of data of a sample hydrocarbon source rock from the in situ gas data acquisition device; and
analyzing the first plurality of data using predictive correlations to determine a maturity of the sample source rock,
wherein the predictive correlations are generated by applying a machine learning model to correlate a second plurality of data obtained from a plurality of representative source rocks with a plurality of attributes of the plurality of representative source rocks; and
the source rock database comprising the second plurality of data associated with the plurality of representative source rocks, a plurality of attributes of the plurality of representative source rocks, and the predicted correlation.
28. The system of claim 27, wherein the in situ gas data acquisition device comprises a spectrometer comprising a light source, a pyroelectric detector, a gas inlet, a gas outlet, and a sample cell.
29. The system of claim 27 or claim 28, wherein the field gas data acquisition device is deployed as part of a logging-while-drilling assembly.
30. The system of claim 27 or claim 28, wherein the in situ gas data acquisition device is deployed as part of a wireline logging assembly.
Technical Field
Methods, devices, and systems are disclosed herein that are generally directed to sensing and prediction of hydrocarbon source rock properties.
Background
In conventional reservoirs, hydrocarbons are recovered from formation or formation traps in sandstone or limestone. Hydrocarbons are produced from some of the deeper source rocks in the basin and migrate and accumulate in these reservoirs. In unconventional reservoirs, the source rock is both the source and reservoir in the rock structure. The value of the hydrocarbons extracted from each type is highly dependent on the properties of the source rock associated with each type. It will be appreciated that predicting and interpreting the properties of the hydrocarbons produced by each type of reservoir requires analysis of the source rock using a variety of methods to determine the maturity and type of the source rock. Maturity and source rock type are parameters that have the greatest effect on the fluid properties of the produced hydrocarbons, such as Gas/oil ratio (GOR), Gas humidity or dryness, and viscosity, all of which affect the mobility and quality of the hydrocarbons. For example, successful hydrocarbon production in unconventional reservoirs is dominated by condensate/gas mixtures. This is mainly associated with type II marine source rocks. These source rocks undergo a transition in the late oil maturation stage, during which the maximum kerogen (kerogen) internal porosity develops as a result of the maturation cycle. The reservoir pressure generated during this maturation period is caused by hydrocarbon cracking, which provides the required gas drive for driving out the produced hydrocarbons. This internal pressure results in greater gas storage capacity, which can be exploited by hydraulic fracturing to break rock and recover hydrocarbons. The produced fluid is a very light, low viscosity and high GOR crude oil containing a large amount of moisture (wet gas). Moisture is more easily extracted into valuable products. In contrast, the density of oil in conventional reservoirs is higher, migrating from less mature oil source types. These reservoirs may present expensive production challenges during recovery, as high viscosity and low GOR reduce the mobility of fluids in the reservoir. Refining such petroleum oils is also expensive due to the removal of excess resins and asphaltenes and the need to crack heavier hydrocarbons during the refining process, thereby reducing the net value of the final product.
Commercial exploitation depends on the identification of effective source rocks that contain the desired organic phase profile and the desired maturity and are currently being produced, or have the ability to produce hydrocarbons. Therefore, mineralogical and organic geochemical information of reservoirs and source rocks is crucial for the evaluation and optimized production of hydrocarbons. The standard method for obtaining properties of source rock is by taking a large number of measurements on homogenized, crushed/comminuted samples. Crushed rock is subjected to an extraction process that separates the organic components of the rock sample. The extracted organic components are analyzed to determine the maturity and organic phase distribution of the source rock sample.
Disclosure of Invention
Some risks are recognized in evaluating extracted and comminuted source rock samples. These risks include, but are not limited to, changes in the components extracted during the analysis and contamination of the analysis by the components during the extraction. In addition, samples are taken from the reservoir and later analyzed at the surface or sent to a laboratory for analysis. This process can take a long time and no information can be obtained in time to inform completion decisions about the well at the collection location. This process is also expensive and therefore mineralogical and maturity information is sometimes collected at several selected sample locations. Due to the limited sampling, important information about the rock may be missed. Therefore, there is a need for a wireline logging tool and other data acquisition device that can provide information about mineralogy and maturity within a reservoir interval in hours and can obtain a complete property profile along the well, rather than a small set of measurements at selected locations. It has also been recognized that there is a need for a casing logging tool that can measure properties of produced fluids as these properties change over time during production to indicate connectivity of the reservoir and whether oil may have been missed.
Spectroscopic measurements involve a light source, reflection of light or transmission of light through the sample, and detection of light intensity by a detector. Furthermore, the light source is monochromatic or the detector is wavelength selective, so that the attenuation of light (whether by reflection or transmission) is observed as a function of wavelength. Current laboratory spectrometers for mid-long Infrared (IR) wavelengths typically use semiconductor photodiode detectors. These detectors are made of materials such as mercury cadmium telluride, indium gallium arsenide, or indium arsenide that must be cooled below ambient temperature (e.g., with thermoelectric or liquid nitrogen) to achieve a usable signal-to-noise ratio.
In certain embodiments, these detectors are replaced with other types of detectors to enable fast spectral measurements with appropriately sized components to be used in wireline tools and to be operable at downhole temperatures. It is further recognized that the composition of reservoir rocks and fluids may be determined using a fraction of the wavelengths, while a full spectrum may not be necessary. Thus, downhole spectrometers can be designed to provide absorption at a small number of selected wavelengths. Since only a few wavelengths are required, it is possible to use the data acquisition device to make longer observation times at each wavelength, thereby having time to average out the higher noise levels produced by the higher downhole temperatures. One embodiment of a downhole spectrometer includes at least one light source, one detector, an assembly that reflects light from a material of interest (fluid or rock) and reflects it to the detector, an assembly that deploys the light source and detector into the well, and an assembly that retrieves data obtained in the spectroscopic measurements.
Disclosed herein are embodiments of systems, computer-implemented methods, and non-transitory computer-readable media having stored computer programs. These embodiments aim to address the shortcomings of the art, including particular methods for spectroscopic and optical measurements of hydrocarbon source rock samples to determine their properties by sensing devices and dedicated algorithms. These methods and systems provide spatially accurate and timely features critical to exploration, development, and reservoir production.
The disclosure herein provides a computer-implemented method for determining the maturity of a sample source rock. One such method comprises the steps of: a communication link is established with a source rock database and a plurality of data acquisition devices positioned proximate to the sample source rock by a data analysis engine. The source rock database includes a first plurality of data obtained from a plurality of representative source rocks and a plurality of attributes of the plurality of representative source rocks. The method further comprises the following steps: obtaining, by a data analysis engine, a second plurality of data of the sample source rock from a plurality of data acquisition devices; and analyzing, by the data analysis engine, the second plurality of data using the predicted correlation to determine a maturity of the sample source rock. The data analysis engine generates a predictive correlation by applying a machine learning model to correlate a first plurality of data obtained from a plurality of representative source rocks with a plurality of attributes of the plurality of representative source rocks. The plurality of data acquisition devices may include a spectrometer having a light source, a pyroelectric detector, and an assembly for reflecting light from the sample hydrocarbon source rock and directing the reflected light to the pyroelectric detector. The pyroelectric detector may be integrated with the tunable filter. In certain embodiments, the component for reflecting light from the sample source rock and directing the reflected light to the pyroelectric detector is an attenuated total reflectance cell. In certain embodiments, the method may further comprise the steps of: the second plurality of data is prepared prior to the step of analyzing the second plurality of data by the data analysis engine by performing outlier detection, baseline correction, peak enhancement, and normalization. The method further comprises the following steps: by a data analysis engineThe first plurality of data of the sample source rock and the determined maturity of the sample source rock are stored in a source rock database. Attributes of a number of representative source rocks may include kerogen typing and elemental composition. The first plurality of data may include two or more of location data, spectral measurements, and optical measurements obtained from a plurality of representative source rocks. The spectral measurements may include one or more of measurements obtained from fourier transform infrared spectroscopy (FTIR), electron spin resonance spectroscopy (ESR), terahertz spectroscopy (THz), and Ultraviolet (UV) spectroscopy. In certain embodiments, the first plurality of data further comprises pyrolysis data. Can be carried out by carrying out the treatment on a plurality of representative hydrocarbon source rocks
Drawings
While the disclosure is susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. The drawings may not be to scale. It should be understood, however, that the drawings and detailed description thereto are not intended to limit the disclosure to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
Fig. 1 is a flow diagram illustrating a method for determining certain properties of a source rock sample, according to an embodiment.
FIG. 2 is a flow diagram illustrating a method for determining certain properties of a source rock sample, according to another embodiment.
FIG. 3 is a flow diagram illustrating a method for selecting spectral wavenumber bands for operation with a plurality of data acquisition devices proximate a sample source rock, according to another embodiment.
Fig. 4A and 4B are sample pyrograms illustrating data obtained from pyrolysis of two representative source rock samples.
Fig. 5 is an IR absorption spectrum showing various wavenumbers corresponding to chemical functional groups with respect to a hydrocarbon source rock.
Fig. 6 is a graphical representation depicting the shift to lower wavenumbers as the maturity of a source rock sample increases.
Fig. 7A is a FTIR spectrum of four groups of source rocks increasing from top to bottom according to their respective maturity. FIG. 7B is an enlarged view of FTIR spectra of four sets of bulk source rocks analyzed in FIG. 7A. Fig. 7C is a further enlarged view of the FTIR spectra of the four sets of bulk source rocks analyzed in fig. 7A.
FIG. 8A is an FTIR spectrum of several source rock powder samples and extracted kerogen. Fig. 8B is an FTIR spectrum of a pure clay powder sample.
Fig. 9A depicts a measurement time trace of a terahertz reference (high density polyethylene (HDPE)) and pulse time waveforms from various source rock sample analyses, and fig. 9B is a corresponding spectrum obtained by fourier transforming the waveforms.
Fig. 10A and 10B depict sample absorption and transmittance spectra of various source rock samples analyzed in fig. 9A and 9B in the terahertz wave band.
Fig. 11A is a two-dimensional (2D) core cross-sectional view showing the bedding (red zone rich in organics) of a source rock. FIG. 11B is a graphical representation of vitrinite reflectance plotted against the hydrocarbon indices of four different maturity levels of source rock identified as sample 1A, sample 2A, sample 3A, and sample 4A.
Fig. 12A-12C provide examples of fluorescence measurements of source rocks and oil.
Fig. 13 is a flow diagram illustrating a method of predicting maturity and organic phase distribution of a sample source rock, according to an embodiment.
Fig. 14A-14D are pre-processed representations of FTIR data from a source rock sample 3A.
FIG. 15 is a representation of a broadband input spectrum of a source rock sample across the terahertz (THz), IR, and Ultraviolet (UV) bands.
Fig. 16A and 16B depict two representations of source rock sample clustering in a reduced-dimension space.
FIG. 17 is a hierarchical double cluster diagram.
Fig. 18 is a representation of the alignment of the weights of the graded features from FTIR spectroscopy with the spectral wavenumber bands to distinguish various clays, minerals and kerogen at different maturity levels.
Fig. 19A and 19B are representations of cluster maps of FTIR spectra from different samples projected on selected wavenumber axes. Fig. 19C is an example dendrogram obtained from hierarchical clustering of sample FTIR spectra for 18 different types of samples.
FIGS. 20A and 20B present the use of two different machine learning algorithms in the form of confusion matrices- -the adaptive enhancement method (as shown in FIG. 20A) and Random
FIG. 21 shows the comparison of maturity indices obtained by two different machine learning algorithms (adaptive enhancement method and Random) with various source rocks processed by conventional methods
Fig. 22A shows a simplified thermal model according to an embodiment, and fig. 22B shows an equivalent circuit of a pyroelectric detector according to this embodiment.
FIG. 23 is a schematic diagram of a device including a commercial pyroelectric detector integrated with a tunable filter according to an embodiment.
Fig. 24 is a schematic diagram of a single reflection configuration of an Attenuated Total Reflectance (ATR) unit, according to an embodiment.
Fig. 25 is a schematic diagram of a multi-reflecting configuration of an Attenuated Total Reflectance (ATR) unit, according to an embodiment.
Fig. 26 is a schematic view of a GIP data acquisition device using a pyroelectric sensor according to an embodiment.
Fig. 27A, 27B, and 27C are photographs of a laboratory prototype of the GIP data acquisition device, the light source, and the pyroelectric detector, respectively, according to an embodiment.
Fig. 28A and 28B are representations of an IR spectrum obtained using ethane and an associated system calibration spectrum, respectively, according to an embodiment.
Fig. 29 is a schematic diagram of a sampling device according to an embodiment.
Embodiments include a system for determining maturity of a sample source rock. One such system includes a plurality of data acquisition devices placed near a sample source rock and communicatively coupled to a computing device. The computing device is coupled to a source rock database via a communication network. The computing device is configured to obtain a first plurality of data of a sample source rock from the plurality of data acquisition devices; the first plurality of data is analyzed using the predictive correlations to determine a maturity of the sample source rock. The source rock database contains a second plurality of data relating to a plurality of representative source rocks, a plurality of attributes of the plurality of representative source rocks, and a predicted correlation generated by applying a machine learning model to correlate the second plurality of data obtained from the plurality of representative source rocks with the plurality of attributes of the plurality of representative source rocks. A plurality of data acquisition devices may be positioned to obtain data from the optimal sensing band of the sample source rock. The system may also include a sample source rock retrieval device to obtain a portion of the sample source rock. The plurality of data acquisition devices may be positioned to obtain two or more of the positional data, the spectral measurements, and the optical measurements. The spectral measurements may include one or more of measurements obtained from fourier transform infrared spectroscopy, electron spin resonance spectroscopy, terahertz spectroscopy, and ultraviolet spectroscopy. The optical measurements may include one or more of measurements obtained by fluorescence microscopy and confocal laser scanning microscopy. The data acquisition device may include a spectrometer including a light source, a pyroelectric detector, and an assembly that reflects light from the sample hydrocarbon source rock and directs the reflected light to the pyroelectric detector. The pyroelectric detector may be integrated with the tunable filter. The component that reflects light from the sample source rock and directs the reflected light to the pyroelectric detector may be an attenuated total reflectance cell.
Another system for determining maturity of a sample source rock includes an in situ gas data acquisition device positioned proximate to the sample source rock and communicatively coupled to a computing device. The computing device is coupled to a source rock database via a communication network and is configured to obtain a first plurality of data of a sample source rock from the in situ gas data acquisition device and analyze the first plurality of data using the predicted correlation to determine a maturity of the sample source rock. The source rock database contains a second plurality of data relating to a plurality of representative source rocks, a plurality of attributes of the plurality of representative source rocks, and a predicted correlation generated by applying a machine learning model to correlate the second plurality of data obtained from the plurality of representative source rocks with the plurality of attributes of the plurality of representative source rocks. The in situ gas data acquisition device may include a spectrometer including a light source, a pyroelectric detector, a gas inlet, a gas outlet, and a sample cell. In certain embodiments, the in situ gas data acquisition device is deployed as part of a logging-while-drilling assembly. In certain embodiments, the in situ gas data acquisition device is deployed as part of a wireline logging assembly.
Many other aspects, features and benefits of the present disclosure can become apparent from the following detailed description taken in conjunction with the accompanying drawings. The system may include fewer components, more components, or different components depending on the desired analysis goals.
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