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Systems and methods for sensing and predicting the maturity of source rocks

A source rock and maturity technology, applied in the direction of reasoning methods, chemical property prediction, measurement devices, etc., can solve problems such as reducing fluid mobility, expensive production challenges, reducing the net value of final production products, etc., to achieve spatially accurate and timely results

Active Publication Date: 2022-05-17
SAUDI ARABIAN OIL CO
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  • Summary
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  • Claims
  • Application Information

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Problems solved by technology

These reservoirs can present costly production challenges during recovery as high viscosity and low GOR reduce fluid mobility in the reservoir
Refining this type of oil is also expensive due to the removal of excess resins and asphaltenes and the need to crack heavier hydrocarbons during the refining process, reducing the net value of the final product produced

Method used

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  • Systems and methods for sensing and predicting the maturity of source rocks
  • Systems and methods for sensing and predicting the maturity of source rocks
  • Systems and methods for sensing and predicting the maturity of source rocks

Examples

Experimental program
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example 1

[0090] The source rock database contains measurements of various characteristics of source rock samples. For example, without limitation, a source rock database contains parameters (e.g., location and depth) associated with various source rock samples, as well as their Parameters (eg S1, S2, Tmax, HI, Oxygen Index (OI), Yield Index (PI), TOC, %Ro-Tmax and HI-%Ro). The source rock database may also contain FTIR data (eg, raw and preprocessed data, FTIR imaging data, and FTIR data of extracted samples), and elemental composition data (including individual mineral maps and mineral distributions for source rock samples). The source rock database also contains spectroscopic measurements and images, confocal fluorescence images, X-ray fluorescence images, ESR measurements, terahertz images and other data. The various methods used to obtain these metrics and examples of the data contained in the database are provided below.

[0091] In the wave number band 500-4000cm -1 FTIR spec...

example 2

[0106] The following examples are methods for selecting and optimizing sensing bands. The input is usually from curated primary rock databases. Sensing bands are selected and optimized using specific target attributes, such as a specific source rock maturity range or a specific organic facies distribution. Bands are selected to provide the most information for the selected target attribute, or to maximize differences in the target source rock sample. Band selection / optimization can be achieved by feature ranking and may also be constrained by the feasibility of sensor design and deployment.

[0107] In this example of sensing band optimization, the bands were chosen to differentiate clay from kerogen at different maturity levels. Apply a feature selection algorithm to calculate the weight of each frequency / wavenumber point. Figure 18 Alignment of weights representing grading features from FTIR spectroscopic measurements to spectral wavenumber bands to distinguish various c...

example 3

[0109] The following example is a method for clustering source rock samples by hierarchical clustering. The samples are divided into groups, where the source rock samples are similar to each other within each group but differ between groups. Maturity-specific signatures and cluster structures were derived to identify associated wavenumber bands and representative spectra. Figure 19A and Figure 19B is a representation of cluster plots from FTIR spectra of different samples projected on the selected wavenumber axis. Clustering is done in a high-dimensional space defined by wavenumber bands. Figure 19A shows the spectral wavenumber 1490.9cm using -1 (x-axis) and 2806.6cm -1 (y-axis) clustering results. Figure 19B Shown in terms of two different spectral wavenumber positions (3697 cm for the x-axis -1 , and for the y-axis is 2862.5cm -1 ) have the same clustering results. When projected in these two different coordinate systems, the same clustering result will have a d...

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Abstract

Systems, devices, and computer-implemented methods for sensing and predicting properties of source rocks are provided. A method of predicting the maturity of a source rock is disclosed, the method comprising: obtaining a plurality of data of a sample source rock from a plurality of data acquisition devices placed adjacent to the sample source rock, and using predictive correlation to analyze The data received is used to determine the maturity of the sample source rock. A predictive correlation is 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.

Description

technical field [0001] Methods, apparatus, and systems directed generally to sensing and prediction of source rock properties are disclosed herein. Background technique [0002] In conventional reservoirs, hydrocarbons are recovered from stratigraphic or structural traps in sandstone or limestone. Hydrocarbons migrated and accumulated in these reservoirs after being produced from some of the deeper source rocks in the basin. In unconventional reservoirs, the source rock is both a source and a reservoir in the rock structure. The value of hydrocarbons extracted from each type is highly dependent on the properties of the source rocks associated with each type. Understanding, predicting and interpreting the properties of the hydrocarbons produced by each type of reservoir requires the analysis of source rocks using a variety of methods to determine their maturity and type. Maturity and source rock type are the parameters that have the greatest impact on the fluid properties ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N33/24G16C20/30G16C20/70G06N20/00G06N20/20
CPCG01N33/24G16C20/30G16C20/70G01N33/241G06N20/20G06N20/00G01V99/005E21B49/003G01N21/3563G01N2021/3595G06N5/046
Inventor 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·艾希曼
Owner SAUDI ARABIAN OIL CO
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