FIELD

Disclosed here are methods, apparatuses, and systems generally directed to the sensing and prediction of the properties of source rock.

BACKGROUND

In conventional reservoirs, oil and gas is recovered from stratigraphic or structural traps in sandstone or limestone. The hydrocarbons migrate and collect in these reservoirs after being generated from some deeper source rock in the basin. In unconventional reservoirs, a source rock serves both as the source and reservoir within the rock fabric. The value of the hydrocarbons extracted from each type is highly dependent on the properties of the source rock associated with each. Understanding, predicting and explaining the properties of the hydrocarbons produced by each type of reservoir requires the analysis of the source rock using a myriad of methods to confirm maturity and type. Maturity and source rock type are parameters that have the greatest effect on the fluid properties of the hydrocarbon generated, such as Gas/Oil Ratio (GOR), gas wetness or dryness, and viscosity all of which influences its mobility and quality. For example, successful oil and gas production in unconventional reservoirs is dominated by condensate/gas mixtures. This is primarily related to Type II marine source rocks. These source rocks have been transformed during late oil maturity phase where the maximum intra-kerogen porosity develops as a result of this maturity cycle. The reservoir pressures generated during this maturity cycle are caused by oil to gas cracking, which provides the gas drive needed for expulsion of the hydrocarbon generated. This internal pressure results in greater gas storage capacity, which can be exploited through hydraulic fracking to fracture the rock and recover the hydrocarbon. The fluid produced is a very light crude of low viscosity and high GOR containing large volumes of wet gas. The wet gas is more easily refined into a valuable product. In contrast, conventional reservoirs contain heavier density petroleum, which has migrated from less mature source types. These reservoirs can present costly production challenges during recovery as the high viscosity and low GOR reduces mobility of the fluid in the reservoir. Refining this type of petroleum is also costly due to removal of excess resins and asphaltenes and the need for cracking heavier hydrocarbons in refining processes, thus reducing the net value of the product finally produced.

Commercial exploitation depends on identification of effective source rock that contains the desired organofacies profile and desired maturity and is presently generating or has the capacity to produce hydrocarbons. Therefore, mineralogical and organic geochemical information on reservoir and source rocks is critical for assessment and optimal production of hydrocarbons. The standard methods of obtaining source-rock properties are via bulk measurements on homogenized, crushed/pulverized samples. Pulverized rock is subject to extraction processes that separate the organic components of the rock sample. The extracted organic components are analyzed to determine the maturity and organofacies profile of the source rock sample.

SUMMARY

Several risks were recognized in the assessment of extracted and pulverized source rock samples. These risks include, but are not limited to, alteration of the extracted components during the analysis and contamination of the analysis by components from the extraction processes. Moreover, samples are taken from the reservoir and analyzed later at the surface or taken to laboratories and analyzed much later. This process can take a long time, and the information is not available in time to inform completion decisions about the well where it is collected. This process is also expensive, so mineralogy and maturity information is collected occasionally and at a few selected sample locations. Significant information about the rocks may be missed due to this limited sampling. Accordingly, there is a need for a wireline logging tool and other data acquisition devices that can provide information within hours about mineralogy and maturity in the reservoir interval and can obtain a full profile of properties along the well instead of a small set of measurements at select locations. A need was also recognized for a cased hole logging tool, which can measure properties of the produced fluids, as these properties change over time during production to indicate the connectivity of the reservoir and whether there may be bypassed oil.

Spectroscopy measurements involve a light source, the reflection of the light off or transmission of the light through a sample, and the detection of the light intensity by a detector. In addition, the source is monochromatic or the detector is made wavelength selective so that the attenuation of the light (whether by reflection or transmission) may be observed as a function of wavelength. Current laboratory spectroscopy instruments for medium and long infrared (IR) wavelengths generally use semiconductor photodiode detectors. These detectors are made of materials such as mercury cadmium telluride, indium gallium arsenide, or indium arsenide, all of which must be cooled below ambient temperatures (for example, thermoelectrically or with liquid nitrogen) to achieve useable signal-to-noise ratios.

In certain embodiments, these detectors are replaced with other kinds of detectors to enable rapid spectroscopy measurements with suitably sized components to use in a wireline tool and can operate at downhole temperatures. It was further recognized that the determination of the composition of reservoir rocks and fluids could be made with a small subset of wavelengths—a full spectrum may not be necessary. So, the downhole spectrometer can be designed to provide absorption at a handful of selected wavelengths. As only a few wavelengths are needed, longer observation times at each wavelength are possible using this data acquisition device, allowing time to average out the higher noise levels produced by the high downhole temperatures. An embodiment of a downhole spectrometer includes at least a light source, a detector, a component to reflect light from the material of interest (whether fluid or rock) and direct reflected light to the detector, components to deploy the source and detector into a well, and components to retrieve data obtained in the spectral measurement.

Disclosed here are embodiments of systems, computer-implemented methods, and a non-transitory, computer-readable medium having stored computer programs. These embodiments are directed to address the shortcomings of the art, including specific methods for spectral and optical measurements of the source rock sample to determine its properties via sensing devices and specialized algorithms. These methods and systems provide spatially accurate and timely characterization that is vital to exploration, development and reservoir production.

The disclosure here provides for a computer implemented methods of determining maturity of a sample source rock. One such method includes the step of establishing, by a data analysis engine, communication links with a source rock database and a plurality of data acquisition devices placed in vicinity of a sample source rock. The source rock database contains a first plurality of data acquired from a plurality of representative source rocks and a plurality of properties of the plurality of representative source rocks. The method further includes the steps of acquiring, 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 a predictive correlation to determine maturity of the sample source rock. The predictive correlation is generated by the data analysis engine by applying a machine learning model to correlate the first plurality of data acquired from a plurality of representative source rocks with the plurality of properties of the plurality of representative source rocks. The plurality of data acquisition devices can include a spectrometer with a light source, a pyroelectric detector, and a component to reflect light from the sample source rock and direct reflected light to the pyroelectric detector. The pyroelectric detector can be integrated with a tunable filter. In certain embodiments, the component to reflect light from the sample source rock and direct reflected light to the pyroelectric detector is an attenuated total reflectance unit. In certain embodiments, the method can further include the steps of preparing the second plurality of data before the step of analyzing the second plurality of data by the data analysis engine by implementation of one or more of outlier detection, baseline correction, peak enhancement, and normalization. The method further includes the steps of storing, by the data analysis engine, the first plurality of data of a sample source rock and the determined maturity of the sample source rock in a source rock database. The properties of the plurality of representative source rocks can include kerogen typing and elemental compositions. The first plurality of data can include two or more of location data, spectral measurements, and optical measurements acquired from the plurality of representative source rocks. The spectral measurements can 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 includes pyrolysis data. The pyrolysis data can be obtained by Rock-Eval® pyrolysis analysis of the plurality of representative source rocks. In certain embodiments, the second plurality of data includes two or more of location data, spectral measurements, and optical measurements acquired from the sample source rock. The spectral measurements can include one or more of measurements obtained from Fourier Transform Infrared spectroscopy, Electron Spin Resonance spectroscopy, terahertz spectroscopy, and Ultraviolet spectroscopy. The optical measurements can include one or more of measurements obtained by fluorescence microscopy and confocal laser scanning microscopy. Certain embodiments include a machine learning model based on one or more of support vector machine, Random Forest®, logistic regression, and Adaptive Boosting algorithms. Certain embodiments of the method further include the step of selecting a spectroscopic wavenumber band for operation of the plurality of data acquisition devices in vicinity of the sample source rock. The spectroscopic wavenumber band for the sample source rock can be selected in response to receiving, by the data analysis engine, one or more selections of desired maturity and desired organofacies profile of the sample source rock from a user interface.

Embodiments include systems to determine maturity of a sample source rock. One such system includes a plurality of data acquisition devices placed in vicinity of 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; and analyze the first plurality of data using a predictive correlation to determine maturity of the sample source rock. The source rock database contains a second plurality of data associated with a plurality of representative source rocks, a plurality of properties of the plurality of representative source rocks, and a predictive correlation that is generated by applying a machine learning model to correlate a second plurality of data acquired from a plurality of representative source rocks with a plurality of properties of the plurality of representative source rocks. The plurality of data acquisition devices can be positioned to acquire data from optimal sensing bands of the sample source rock. The system can further include a sample source rock retrieving apparatus to obtain a portion of the sample source rock. The plurality of data acquisition devices can be positioned to acquire two or more of location data, spectral measurements, and optical measurements. The spectral measurements can include one or more of measurements obtained from Fourier Transform Infrared spectroscopy, Electron Spin Resonance spectroscopy, terahertz spectroscopy, and Ultraviolet spectroscopy. The optical measurements can include one or more of measurements obtained by fluorescence microscopy and confocal laser scanning microscopy. The data acquisition devices can include a spectrometer comprising a light source, a pyroelectric detector, and a component to reflect light from the sample source rock and direct reflected light to the pyroelectric detector. The pyroelectric detector can be integrated with a tunable filter. The component to reflect light from the sample source rock and direct reflected light to the pyroelectric detector can be an attenuated total reflectance unit.

Another system to determine maturity of a sample source rock includes a gas-in-place data acquisition device placed in vicinity of 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 and configured to obtain a first plurality of data of a sample source rock from the gas-in-place data acquisition device and analyze the first plurality of data using a predictive correlation to determine maturity of the sample source rock. The source rock database contains a second plurality of data associated with a plurality of representative source rocks, a plurality of properties of the plurality of representative source rocks, and a predictive correlation generated by applying a machine learning model to correlate the second plurality of data acquired from the plurality of representative source rocks with the plurality of properties of the plurality of representative source rocks. The gas-in-place data acquisition device can include a spectrometer comprising a light source, a pyroelectric detector, a gas inlet, a gas outlet, and a sample chamber. In certain embodiment, the gas-in-place data acquisition device is deployed as part of a logging while drilling assembly. In certain embodiment, the gas-in-place data acquisition device is deployed as part of a wireline logging assembly.

Numerous other aspects, features and benefits of the present disclosure may be made apparent from the following detailed description taken together with the drawing figures. The systems can include less components, more components, or different components depending on desired analysis goals.

