MÉTODO PARA EXTRAPOLAR A REPRESENTATIVIDADE DAS CLASSES DOS DADOS DE AMOSTRAS LATERAIS DE ROCHA DE PAREDE DE POÇO DE PETRÓLEO

Description

1 / 14 METHOD FOR EXTRAPOLATING THE REPRESENTATIVENESS OF CLASSES FROM LATERAL ROCK SAMPLES OF OIL WELL WALLS Field of Invention

[001] The present invention falls within the technical field of petroleum and gas, specifically related to well drilling, more specifically related to methods of investigating physical or chemical properties of rock samples to determine the nature of borehole walls, and refers to a method for extrapolating the representativeness of classes of data from lateral rock samples of petroleum well walls. Fundamentals of the Invention

[002] There are two ways to obtain geological information: direct and indirect methods. The sum of the knowledge acquired by both methods makes it possible to achieve a greater understanding of the geological scenario, reducing uncertainties about the activities involving the oil and gas industry.

[003] Indirect data acquisition uses tools that measure physical properties of the Earth's surface and subsurface without direct contact with the object of study. An important example is seismic data acquisition, which provides a tomography of the Earth's interior.

[004] This information is not necessarily obtained through oil wells, but it is crucial for drilling, as it allows visualization of the acoustic and geometric properties of subsurface geological layers and enables geological interpretations that can identify potential reservoirs and the likely fluid that they contain. Petition 870250006228, dated 01 / 27 / 2025, pages 38 / 63 2 / 14 fills in, assisting in precise positioning for drilling an oil well and discovering new resources.

[005] Direct methods of acquiring geological information require direct contact with the object of study, in this case the rock. In the oil and gas industry, this contact is made through well drilling, allowing direct sampling of the lithology (rock type) in the subsurface. In summary, there are three methods of acquiring rock data through oil wells: cuttings sampling, sidewall sampling, and core sampling.

[006] Cuttings sampling is a method performed during the well drilling process. The rock crushed by the drill bit rises through the drill string along with the drilling fluid, where it is collected on the drill rig's screen. These samples, despite the imprecision of their origin depth, are important indicators in geonavigation because, from their analysis, it is possible to presume the positioning of the well in the stratigraphic stack.

[007] The second sampling method, by sidewall sampling, is acquired during an operation where special tools are lowered into the open well, without drilling activity, and the sample is acquired by sawing a cylinder from the well wall and subsequently retrieving this sample to the surface. This type of sampling allows for a more accurate study of the rock type and its properties, but it is considered a point sampling method, as its representativeness is not very comprehensive and allows conclusions only at the specific depth at which it was taken. Petition 870250006228, dated 01 / 27 / 2025, pp. 39 / 63 3 / 14 acquired.

[008] Finally, rock core sampling is the only method that can recover continuous sections of rock, allowing for a variety of laboratory studies to be carried out. Despite the wealth of information that this sampling provides, its acquisition is rare and restricted to a relatively short interval, given the high cost of drilling time required to perform this type of operation.

[009] Core acquisition is done using a special drill that can drill through the rock and store an intact cylinder inside the drill string. After drilling the interval of interest, the drill string is recovered to the surface with the rock cylinder inside.

[010] More specifically, considering the acquisition of lateral samples, it is known that adequate representativeness of point rock data is important, as the characterization of rock classes depends on a careful and coherent expansion. If this step is done incorrectly, all subsequent analyses and products will incorporate these errors.

[011] Usually, expansion or extrapolation is performed manually, subject to accuracy errors regarding the top and bottom of the interval, or following a criterion of N samples of electrical profiles from the specific depth of each lateral sample, without considering variations in electrical profile data. Thus, the present invention was created to minimize errors by automating processes and establishing criteria. Petition 870250006228, dated 01 / 27 / 2025, pp. 40 / 63 4 / 14 quantitative factors that take profile data into account so that representativeness can be established.

[012] More specifically, the present invention proposes to extrapolate the representativeness of the data classes of sidewall samples from wells, which are originally assigned to a specific depth and correspond to single sampling points in the studies in which they will be used. These, in turn, often demand a large volume of samples in their ideal operation and, therefore, the need to increase the representativeness of the point data. State of the Art

[013] Document US11767752B2 is part of the general state of the art and describes a method for determining the depth of a sidewall core sample collected from a well, relative to a well reference record. The method helps correlate the sample collection depth using calibrated reference and image profiles, based on the analysis of image artifacts associated with the sample. However, it should be noted that this document only proposes repositioning the point sample from an image profile. This is a processing step that the present invention performs before applying the proposed method, since all sidewall samples must be correctly positioned before increasing the representativeness of the point data.

[014] In turn, document CN109143399B describes a method for identifying the stratigraphic sequence interface in carbonate rocks, seeking to solve problems related to the need for few samples of Petition 870250006228, dated 01 / 27 / 2025, pp. 41 / 63 5 / 14 core, low representativeness, and the difficulty in accurately identifying the sequence interface. It is noted that this document uses core data to calibrate with the profiles and make predictions of carbonate rocks, also called electrofacies. That is, in this document, the data used is rock core data, not lateral samples, as occurs in the present invention. But when this type of study involves lateral samples, the document would probably use manually obtained representativeness, which is precisely what the present invention seeks to automate.

[015] Document US11821857B2 protects a data-based method for determining the mineral composition of materials that uses a data-driven inverse modeling approach to provide a more accurate solution in determining mineral composition based on elemental data and correlation analysis. Note that this document describes a mineralogical composition modeling of well data, including lateral sampling, whereas the present invention does not propose to perform mineralogical modeling, but rather seeks to increase the representativeness of point data.

[016] Finally, document US4646240A is also part of the general state of the art and protects a method and apparatus for determining geological facies in subsurface formations, seeking to classify and display geological facies based on logging data and facilitating the interpretation of formations at different depths. Similarly, as discussed earlier, this document has a similar objective to US11821857B2, which is to create an electrofacies model based on electrical log data. In Petition 870250006228, dated 01 / 27 / 2025, pp. 42 / 63 6 / 14 In both cases, the goal is to predict rock types in intervals where no rock samples exist, based on well data.

[017] It is also important to highlight that the present invention offers advantages considering the economic and productivity impact, which is associated with the fact that it speeds up the process of expanding the representativeness of point rock data. This process is necessary so that this type of data can be used in various applications. Because it is automatic, it saves not only the time spent on manual expansion, but also aligns the established criteria for determining the representativeness of point data with electrical profile data.

[018] Having rock data consistent with electrical profile data is essential to achieving good results in applications. Because, in most cases, rock data will be used in conjunction with electrical profile data. Brief description of the invention

[019] The present invention relates to a method for extrapolating the representativeness of rock class data from lateral rock samples from oil well walls, considering that adequate representativeness of point rock data is important because the characterization of rock classes depends on a careful and coherent expansion. If this step is performed incorrectly, all subsequent analyses and products will incorporate these errors. The method comprises the steps of a) selecting electrical log data and point rock data; b) determining the maximum thickness and standard deviation tolerance that each electrical log will have to condition the representativeness expansion; c) verification in the vicinity of each Petition 870250006228, dated 01 / 27 / 2025, pages 43 / 63 7 / 14 point sample up to where the established criteria are met and determines the limit of representativeness; ed) obtaining the lithology result, or rock type, with the representativeness of the point data expanded. The invention was created to minimize errors by automating processes and establishing quantitative criteria that take into account profile data so that representativeness can be established. Brief description of the figures

[020] To obtain a full and complete visualization of the object of this invention, the figures to which reference is made are presented below.

[021] Figure 1 schematically represents how point data are obtained from the wellbore wall. As shown, point rock data corresponds to samples acquired from the wellbore wall using special tools that can capture part of the subsurface rock formation and bring it to the surface for analysis. Point data are extremely important for characterizing the rocks being drilled, since a variety of laboratory analyses can be performed using them, including rock type classification.

[022] Figure 2 shows schematically the adjustment of the original depth of the lateral sample acquisition to the reference sampling of the well.

[023] Figure 3 schematically illustrates how the method of the present invention is performed. In (a), a possible size n is initially determined, where the greatest increase in representativeness can Petition 870250006228, dated 01 / 27 / 2025, pp. 44 / 63 8 / 14 to be performed (2N + 1), in the example 2 samples up and down, totaling 5 samples in all. In (b) a vector of size (2N + 1) with 2N empty spaces is created, the central element being the label ci of the class referring to sample yi. Then, in (c), the N neighboring samples up and down from yi are tested following the following criterion: for each profile xp, if abs(x1? — xf+n) < of, where n is a value in the sequence [1, ...,N], then yi ± n = c, otherwise the extrapolation is interrupted. The result, in (d), are vectors filled only where this condition is satisfied.

[024] Figure 4 schematically represents the results obtained by applying the method.

[025] Figure 5 schematically represents the same results, but showing the lack of correspondence between the depth of acquisition of the lateral sample and the resolution of the reference data (profile).

[026] Figure 6 schematically represents the results, highlighting the values ​​of the electrical logs (GR, DEN, NEU, DT and PE) relating to this lateral sample. The electrical logs GR, DEN, NEU, DT and PE are mnemonics for well logging tools. Each of these measures a type of physical property of the rock.

[027] Figure 7 schematically represents how the conventional manual method works. A top and base interval is created by dragging the mouse, and a rock class is assigned to this interval. The top and base are determined from the responses of logs around the sample. That is, manually the geoscientist has to visually analyze the log responses and respect the data resolution. This Petition 870250006228, dated 01 / 27 / 2025, pages 45 / 63 9 / 14 This type of extrapolation involves an error associated with manual interpretation.

[028] Figure 8 schematically represents the conventionally used fixed distance method from the depth of the samples, in which all samples have a top and base corresponding to a distance above and below the sample. In this case, it is common for electrical log values ​​that do not correspond to the rock characteristics to end up being included in the label. In addition to not respecting the resolution of the log data.

[029] Figure 9 shows the results obtained using the method of the present invention, demonstrating that all expansions are performed automatically while respecting the data resolution. The expansion is always done by analyzing the values ​​of the set of profiles in the neighborhoods of each sample. Regions with homogeneous profile responses allowed the algorithm to perform a greater expansion of representativeness.

[030] Figure 10 schematically represents the results using the method of the present invention, showing that the value of the algorithm (set of instructions executable on a computer) is proven in cases where there is a massive volume of data and that, if done manually, would require an enormous amount of time or, if done with a fixed distance, would incorporate many errors. Detailed description of the invention

[031] The present invention relates to a method for extrapolating the representativeness of classes of sidewall sample data from wells, which are originally assigned to a specific depth and correspond to Petition 870250006228, dated 01 / 27 / 2025, pp. 46 / 63 10 / 14 unique sampling points in the studies in which they will be used. These, in turn, often require a large volume of samples in their ideal operation and, therefore, the need to increase the representativeness of the point data. Thus, the present invention automates manual processes that are subject to precision errors because it is entirely dependent on profile data.

[032] The motivation for developing the method of the present invention is also related to the fact that the representativeness of a single point for each lateral sample is not sufficient for subsequent studies in which this type of data is used, such as in electrofacial modeling (classification of profile responses in rock characteristics). There is a need to expand this point representativeness into an interval that represents the rock class of that sample.

[033] Side samples are obtained as shown in Figure 1. As we can see, a specific tool for this task is lowered into the open well (without casing in the wellbore interval) to the depth of interest, quickly defined from the tool's cable length. Then, the tool uses a small cylindrical drill bit that saws the rock on the wellbore wall, saving a point sample of rock in the shape of a cylinder.

[034] As shown in Figure 5, any and all studies of lateral samples in wells require that the data have a reference. In this case, the reference will be the first curve, that of GR (Gamma Ray). The red dots represent each sample of the GR data, regularly spaced at a distance of 0.1524 m. Petition 870250006228, dated 01 / 27 / 2025, pages 47 / 63 11 / 14

[035] The side sample data does not follow the same depth reference as the profile data. Therefore, the side sample data is adjusted to the profile point closest to it, and this becomes the reference depth of the side sample data.

[036] In summary, the method comprises the following steps: a) selection of electrical profile data and point rock data. In this step, the electrical profiles that best characterize the different rock types from the point rock data must be chosen.

[037] In step b), considered the step of determining the maximum thickness and the standard deviation tolerance that each electrical profile will have to condition the expansion of representativeness, the parameters that will condition the increase in representativeness made by the algorithm are configured.

[038] The maximum thickness is counted in the number of electrical profile step points from the side sample. The amount of tolerable standard deviation for each electrical profile is used as a criterion for stopping the increase in thickness for the respective electrical profiles.

[039] If this standard deviation limit is not reached in any of the configured criteria, the extrapolated representativeness will be the maximum configured thickness. Each electrical profile tool has a vertical resolution, where each physical property measurement is performed at regular depth intervals.

[040] In most cases the resolution is 0.1524 m, that is, a physical property measurement is made every 0.1524 m. The increase in representativeness will follow the same pattern. Petition 870250006228, dated 01 / 27 / 2025, pages 48 / 63 12 / 14 spacing up to a maximum distance from the respective side sample. This distance is configured in quantities of profile samples.

[041] Step c), considered the verification step in the vicinity of each point sample up to where the established criteria are met and determine the limit of representativeness, the configurations from the previous step are subjected to testing at points adjacent to each side sample up to the maximum configured thickness.

[042] Step d), considered the result acquisition step, with the result being a lithology curve (rock type) with the expanded representativeness of the point data. In this step, the algorithm generates a product consisting of top and base depths of the intervals corresponding to the extrapolated representativeness of each lateral sample.

[043] To maintain similar characteristics, the criterion used is the standard deviation of each electrical profile p for each class c. Thus, the extrapolation of representativeness will be carried out up to a distance limit (number of samples) or until the points neighboring the side sample satisfy variation limits of values ​​below the pre-established threshold of standard deviations. Therefore, where there are values ​​above these deviations, for any profile in the dataset, the extrapolation is interrupted.

[044] A large part of the studies carried out with lateral samples take into account information from electrical logs and, therefore, there is a need to combine this information. When the object of study is a well, usually Petition 870250006228, dated 01 / 27 / 2025, pp. 49 / 63 13 / 14 the information relating to this is all taken to the same reference, such as the measured depth.

[045] From this, all data are resampled to have positional equivalence. This process applies to both electrical log data and rock data. In the case of electrical log data, these have a regular vertical sampling (steps), where the values ​​of the physical properties that the electrical log tools measure are recorded at the regularity of this pre-established interval (frequently 0.1524 meters).

[046] For sidewall sample data, the acquired depth can be at any position, without requiring a specific step. Therefore, the first step of the method is to resamp the position of the sidewall samples to the neighboring equivalent depths of the reference position (Figure 2).

[047] Having all the data at the same depth reference, it is possible to perform operations, enabling the application of the method. The main objective of this method was to increase the representativeness of the point data while maintaining the greatest possible similarity to the characteristics of the original data.

[048] For this case, characteristics are understood to be the values ​​of the measurements of the electrical profiles in question. Therefore, ideally, the extrapolation of representativeness should follow criteria according to these electrical profiles. To maintain similar characteristics, then, the criterion that will be used is the standard deviation of each electrical profile p for each class c. Petition 870250006228, dated 01 / 27 / 2025, pages 50 / 63 14 / 14

[049] As shown in Figure 3, the representativeness extrapolation will be carried out up to a distance limit (number of samples) or until the points neighboring the side sample satisfy variation limits of values ​​below the pre-established threshold of standard deviations. Therefore, where there are values ​​above these deviations, for whatever the profile of the data set, the extrapolation is interrupted.

[050] As shown in Figure 9, all expansions are performed automatically respecting the data resolution. The expansion is always done by analyzing the values ​​of the set of profiles in the neighborhoods of each sample. Regions with homogeneous profile responses allowed the algorithm to perform greater representativeness expansion.

[051] As shown in Figure 10, the efficiency and value of the method with the developed algorithm (set of instructions) is proven in cases where there is a massive volume of data and that, if done manually, would require an enormous amount of time or, if done with a fixed distance, would incorporate many errors.

[052] Those skilled in the art will appreciate the knowledge presented here and will be able to reproduce the invention in the embodiments presented and in other variants, covered within the scope of the appended claims. Petition 870250006228, dated 01 / 27 / 2025, pages 51 / 63

Claims

1 / 3 CLAIMS 1. Method for extrapolating the representativeness of classes of data from lateral rock samples from oil well walls, characterized by comprising the steps of: a) selection of electrical log data and point rock data; b) determination of the maximum thickness and standard deviation tolerance that each electrical log will have to condition the expansion of representativeness; c) verification in the vicinity of each point sample up to where the maximum thickness or standard deviation tolerance is satisfied and determining this as the limit of representativeness; d) obtaining the result.

2. Method, according to claim 1, characterized in that the result is a lithology curve with the representativeness of the point data expanded.

3. Method, according to claim 1 or 2, characterized in that the electrical profile data are electrical resistivity, electrical conductivity, induced polarization and / or electrical permittivity.

4. A method, according to any one of claims 1 to 3, characterized in that the lateral sample data is adjusted to the nearest electrical profile point, where this adjustment becomes the reference depth of the lateral sample data.

5. Method, according to any of the claims 1 to 4, characterized by the fact that the electrical profile data have a regular vertical sampling, in which the values ​​of the physical properties that the electrical profile tools measure are recorded in the regularity of this pre-established interval, preferably of 0.1524 meters.

6. Method, according to any one of claims 1 to 5, characterized in that it further comprises a step for resampling the position of the lateral samples to neighboring depths equivalent to the reference positioning.

7. Method, according to any one of claims 1 to 6, characterized in that the established criteria are the standard deviation of each electrical profile p for each class c.

8. A method, according to any one of claims 1 to 7, characterized in that step c is performed by a set of instructions executed on a computer.

9. Method, according to any one of claims 1 to 8, characterized in that the step is to select the electrical profiles that best characterize the different rock types from the given rock point.

10. Method, according to any one of claims 1 to 9, characterized in that the maximum thickness is counted in the number of electrical profile step points from the side sample.

11. Method, according to any of claims 1 to 10, characterized by the fact that the amount of tolerable standard deviation for each electrical profile enters as a criterion for interrupting the increase in thickness for the respective electrical profiles, in which if the standard deviation limit is not reached in any of the configured criteria, the extrapolated representativeness will be the maximum configured thickness.

12. Method, according to any one of claims 1 to 11, characterized in that in step c the maximum thickness settings and standard deviation tolerance defined in step b are subjected to the test at points adjacent to each lateral sample.

13. Method, according to any one of claims 1 to 12, characterized in that in step d, the set of instructions generates a product with top and bottom depths of the intervals relating to the extrapolated representativeness of each side sample. Petition 870250006228, dated 01 / 27 / 2025, pp. 54 / 63

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