Method for extrapolating the representativeness of the classes of the data of oil well wall rock lateral samples

Abstract

Method for extrapolating the representativeness of classes of data of oil well wall rock lateral samples comprises steps of a) selecting electrical log data and rock point data; b) determining maximum thickness and standard deviation tolerance which each electrical log will have to condition the expansion of the representativeness; c) verifying in the vicinity of each point sample to what extent the established criteria are met and determining that as the limit of the representativeness; and d) obtaining lithology result, or rock type, with the expanded representativeness of the point data. The method minimizes errors by automating processes and establishing quantitative criteria that take into account log data so that the representativeness is established. Considering adequate representativeness of rock point data is important—since characterization of the rock classes depends on a judicious and coherent expansion—if not performed incorrectly, all subsequent analyses and products will incorporate these errors.

Description

RELATED APPLICATION DATA

[0001] This application is based on and claims priority to Brazilian Application No. BR 10 2025 001518 8, filed on Jan. 27, 2025, the entire contents of which are incorporated herein by reference.FIELD OF THE INVENTION

[0002] 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 the classes of the data of oil well wall rock lateral samples.BACKGROUND OF THE INVENTION

[0003] 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 the uncertainties about the activities involving the oil and gas industry.

[0004] Indirect methods of acquisition use 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 acquisition, which provides a tomography of the Earth's interior.

[0005] 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 allows geological interpretations that can identify possible reservoirs and the likely fluid that fills the same, assisting in precise positioning for drilling an oil well and in the discovery of new resources.

[0006] 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 from oil wells: cuttings sampling, well wall lateral sampling, and rock core sampling.

[0007] 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 screen. These samples, despite the imprecision of the depth of origin, are important indicators in geonavigation because, from their analysis, it is possible to presume the positioning of the well in the stratigraphic stack.

[0008] The second form of sampling, by well wall lateral sampling, is acquired during an operation where special tools descend into the open well, without drilling activity, and the sample acquisition is carried out by sawing a cylinder from the well wall and, subsequently, this sample is recovered to the surface. This type of sampling allows for a more accurate study of the type of rock and its properties, but it is a sampling said to be a point one, as its representativeness is not very comprehensive and allows conclusions only at the specific depth at which it was acquired.

[0009] Finally, the rock core sampling is the only one that can recover continuous sections of rock, allowing 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 small interval, given the high cost of drilling rig time required to perform this type of operation.

[0010] The core acquisition is done using a special drill bit 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.

[0011] More specifically, considering the acquisition of lateral samples, it is known that adequate representativeness of the rock point data is important, as the characterization of the rock classes depends on a judicious and coherent expansion. If this step is performed incorrectly, all subsequent analyses and products will incorporate these errors.

[0012] Usually, the expansion or extrapolation is performed manually, subject to precision errors regarding the top and bottom of the interval, or following a criterion of N samples of electrical log from the specific depth of each lateral sample, without considering the variations in electrical log data. In this way, the present invention was created to minimize errors by automating processes and establishing quantitative criteria that take into account log data so that the representativeness is established.

[0013] More specifically, the present invention proposes to extrapolate the representativeness of the classes of the data of well wall rock lateral samples, 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, there is a need of increasing the representativeness of the point data.STATE OF THE ART

[0014] Document U.S. Pat. No. 11,767,752B2 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 to correlate the sample collection depth, using calibrated reference and image logs, based on the analysis of the image artifacts associated with the sample. However, it is noted that this document only proposes the repositioning of the point sample from an image log. This is a process that the present invention performs before applying the proposed method, since all lateral samples must be correctly positioned in order to increase the representativeness of the point data.

[0015] 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 core samples, low representativeness, and the difficulty in accurately identifying the sequence interface. It is noted that this document uses core data to calibrate with the logs and make predictions of carbonate rocks, also called electrofacies. That is, in this document, the data used is rock core, not lateral samples, as occurs in the present invention. But when this type of study involves lateral samples, the document would probably use manually made representativeness, which is precisely what the present invention seeks to automate.

[0016] Document U.S. Pat. No. 11,821,857B2 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 the 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.

[0017] Finally, document U.S. Pat. No. 4,646,240A is also part of the general state of the art and protects a method and apparatus for determining geological facies in underground formations, seeking to classify and display geological facies based on well logging data and facilitating the interpretation of the formations at different depths. Similarly, as discussed earlier, this document has a similar objective to U.S. Pat. No. 11,821,857B2, which is to create an electrofacies model based on electrical log data. In both, the objective is, from well data, to predict rocks in intervals where there are no rock samples.

[0018] It is further 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 the rock point 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 the point data with the electrical log data.

[0019] Having rock data consistent with electrical log data is essential to achieving good results in applications, because, in most cases, rock data will be used in conjunction with electrical log data.SUMMARY OF THE INVENTION

[0020] The present invention relates to a method for extrapolating the representativeness of the classes of the data of oil well wall rock lateral samples, considering that adequate representativeness of rock point data is important, since the characterization of rock classes depends on a judicious 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 rock point data; b) determining the maximum thickness and standard deviation tolerance which each electrical log will have to condition the expansion of the representativeness; c) verifying in the vicinity of each point sample to what extent the established criteria are met and determining that as the limit of the representativeness; and d) obtaining the lithology result, or rock type, with the expanded representativeness of the point data. The invention was created to minimize errors by automating processes and establishing quantitative criteria that take into account log data so that the representativeness is established.BRIEF DESCRIPTION OF THE FIGURES

[0021] To obtain a complete and thorough visualization of the object of this invention, the figures to which reference is made below are presented.

[0022] FIG. 1 schematically represents how point data are obtained from the well wall. As shown, the rock point data corresponds to samples acquired from the well wall by 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 that the well is drilling, since from them it is possible to carry out a variety of laboratory analyses, including the classification of the rock type.

[0023] FIG. 2 schematically shows the adjustment of the original depth of the lateral sample acquisition for the reference sampling of the well.

[0024] FIG. 3 schematically illustrates how the method of the present invention is performed. In (a), a possible size n is initially determined, in which the greatest increase in representativeness can be made (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 log xp, ifabs⁢(xip-xi±np)<σcp,where n is a value in the sequence [1, . . . , N], then yi+n=ci, otherwise the extrapolation is interrupted. The result, in (d), are vectors filled only where this condition is met.FIG. 4 schematically represents the results obtained with the application of the method.

[0026] FIG. 5 schematically represents the same results, but showing the lack of correspondence between the acquisition depth of the lateral sample and the resolution of the reference data (log).

[0027] FIG. 6 schematically represents the results, highlighting the values of the electrical logs (GR, DEN, NEU, DT, and PE) referring 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.

[0028] FIG. 7 schematically represents how the conventional manual method works. A top and bottom interval is created by dragging the mouse, and a rock class is assigned to this interval. The top and bottom 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 type of extrapolation involves an error associated with manual interpretation.

[0029] FIG. 8 schematically represents the conventionally used fixed distance method from the depth of the samples, in which all samples have a top and bottom 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 characteristics of the rock to end up being included in the label, in addition to not respecting the resolution of the log data.

[0030] FIG. 9 shows the results using the method of the present invention, showing that all expansions are performed automatically respecting the data resolution. The expansion is always done by analyzing the values of the set of logs in the vicinity of each sample. Regions with homogeneous log responses allowed the algorithm to perform a greater expansion of the representativeness.

[0031] FIG. 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 a huge amount of time or, if done with a fixed distance, would incorporate many errors.DETAILED DESCRIPTION OF THE INVENTION

[0032] The present invention relates to a method for extrapolating the representativeness of the classes of the data of well wall lateral samples, 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 require a large volume of samples in their ideal operation and, therefore, there is the need of increasing the representativeness of the point data. In this way, the present invention automates manual processes that are subject to precision errors because it is entirely subject to log data.

[0033] The motivation for developing the method of the present invention is further 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 electrofacie modeling (classification of the log responses in rock characteristics). There is a need to expand this point representativeness over an interval that represents the rock class of that sample.

[0034] The lateral samples are obtained as shown in FIG. 1. As there can be seen, a specific tool for this task is lowered into the open well (without casing in the well 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 well wall, saving a point sample of rock in the shape of a cylinder.

[0035] As shown in FIG. 5, any study of lateral samples in wells requires that the data have a reference. In this case, the reference will be the first curve, that of GR (Gamma Ray). The red points represent each sample of the GR data, regularly spaced at a distance of 0.1524 m.

[0036] The lateral sample data does not follow the same depth reference as the log data. Therefore, the lateral sample data is adjusted to the log point closest to the same, and this becomes the reference depth of the lateral sample data.

[0037] In summary, the method comprises the following steps: a) selecting electrical log data and rock point data. In this step, the electrical logs that best characterize the different rock types from the rock point data must be chosen.

[0038] Step b), considered the step for determining the maximum thickness and standard deviation tolerance which each electrical log will have to condition the expansion of the representativeness, the parameters that will condition the increase in representativeness made by the algorithm are configured.

[0039] The maximum thickness is counted in the number of electrical log step points from the lateral sample. The amount of tolerable standard deviation for each electrical log is used as a criterion for interrupting the increase in thickness for the respective electrical logs.

[0040] 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 log tool has a vertical resolution, in which each physical property measurement is performed at regular depth intervals.

[0041] 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 this same spacing up to a maximum distance from the respective lateral sample. This distance is configured in quantities of log samples.

[0042] Step c), considered the step of verifying in the vicinity of each point sample to what extent the established criteria are met and determining that as the limit of the representativeness, the configurations of the previous step are submitted to the test at the points neighboring each lateral sample up to the maximum configured thickness.

[0043] Step d), considered the step of obtaining the result, 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 bottom depths of the intervals referring to the extrapolated representativeness of each lateral sample.

[0044] To maintain similar characteristics, the criterion used is the standard deviation of each electrical log p for each class c. Thus, the extrapolation of the representativeness will be carried out up to a distance limit (number of samples) or until the points neighboring the lateral sample meet variation limits of values below the pre-established threshold of standard deviations. Therefore, where there are values above these deviations, for whatever the log of the dataset, the extrapolation is interrupted.

[0045] 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 the information relating to it is all taken to the same reference, such as the measured depth.

[0046] 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 in the regularity of this pre-established interval (frequently 0.1524 meters) are recorded.

[0047] In turn, for well wall lateral sample data, the acquired depth can be in any position, without requiring a specific step. Therefore, the first step of the method is to resample the position of the lateral samples to the neighboring depths equivalent to the reference positioning (FIG. 2).

[0048] 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.

[0049] In this case, characteristics are understood as the values of the electrical log measurements in question. Therefore, ideally, the extrapolation of the representativeness should follow criteria according to these electrical logs. To maintain similar characteristics, then, the criterion that will be used is the standard deviation of each electrical log p for each class c.

[0050] As shown in FIG. 3, the extrapolation of representativeness will be carried out up to a distance limit (number of samples) or until the points neighboring the lateral sample meet variation limits of values below the pre-established threshold of standard deviations. Therefore, where there are values above these deviations, for whatever the log of the dataset, the extrapolation is interrupted.

[0051] As shown in FIG. 9, all expansions are performed automatically respecting the data resolution. The expansion is always done by analyzing the values of the set of logs in the neighborhoods of each sample. Regions with homogeneous responses from the logs allowed the algorithm to perform a greater expansion of the representativeness.

[0052] As shown in FIG. 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 which, if done manually, would require an enormous amount of time or, if done with a fixed distance, would incorporate many errors.

[0053] Those skilled in the art will appreciate the knowledge presented herein and will be able to reproduce the invention in the presented embodiments and in other variants, encompassed within the scope of the attached claims.

Claims

1. A method for extrapolating representativeness of classes of data of oil well wall rock lateral samples, comprising the following steps:a) selecting electrical log data and rock point data;b) determining a maximum thickness and a standard deviation tolerance which each electrical log will have to condition expansion of the representativeness;c) verifying in a vicinity of each rock point data to what extent the maximum thickness or the standard deviation tolerance are met and determining that as the limit of the representativeness; andd) obtaining a result including a lithology curve with the expanded representativeness of the rock point data.

2. The method according to claim 1, wherein the electrical log data are electrical resistivity, electrical conductivity, induced polarization, and / or electrical permittivity.

3. The method according to claim 1, wherein the data of oil well wall rock lateral samples is adjusted to a nearest electrical log point, andwherein the adjustment becomes a reference depth of the data of oil well wall rock lateral samples.

4. The method according to claim 1, wherein the electrical log data has a regular vertical sampling interval, andwherein values of physical properties that the electrical log measures are recorded in the regular vertical sampling interval.

5. The method according to claim 4, wherein the regular vertical sampling interval is 0.1524 meters.

6. The method according to claim 3, further comprising the step of:resampling a position of the lateral samples to a neighboring depth equivalent to the reference depth.

7. The method according to claim 1, wherein the established criteria are the standard deviation of each electrical log p for each class c.

8. The method according to claim 1, wherein step c is performed by a set of instructions executed on a computer.

9. The method according to claim 1, wherein step a selects the electrical log data that best characterizes different rock types from the rock point data.

10. The method according to claim 1, wherein the maximum thickness is counted in a number of electrical log step points from the oil well wall rock lateral sample.

11. The method according to claim 1, wherein the standard deviation tolerance for each electrical log data is used as a criterion for interrupting a thickness increase for the respective electrical log data, andwherein if the standard deviation tolerance is not reached, the extrapolated representativeness will be the maximum thickness.

12. The method according to claim 1, wherein, in step c, the maximum thickness and standard deviation tolerance defined in step b are subjected to testing at points neighboring each oil well wall rock lateral sample.

13. The method according to claim 1, wherein, in step d, the result includes a product with top and bottom depths of intervals referring to the extrapolated representativeness of each oil well wall rock lateral sample.

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