Patent Application
20250052924
The present disclosure generally relates to determining rock properties of geological formations using elemental concentration data.
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
Producing hydrocarbons from a wellbore drilled into a geological formation is a remarkably complex endeavor. In many cases, exploration, appraisal, and decisions involved in hydrocarbon exploration and production may be informed by measurements from downhole well-logging tools that are conveyed deep into the wellbore. The measurements may be used to infer properties and characteristics of the geological formation (e.g., earth formation) surrounding the wellbore. Rock properties (e.g., matrix properties) of the geological formation may be used in the interpretation and determination of measurements made by the well-logging tools (e.g., well logging measurements). Accordingly, improving the accuracy of determining the rock properties may positively influence the exploration, appraisal, and decisions involved in hydrocarbon exploration and production.
A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below.
Various refinements of the features noted above may be undertaken in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination. The brief summary presented above is intended to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter.
Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which:
One or more specific embodiments of the present disclosure will be described below. These described embodiments are examples of the presently disclosed techniques. Additionally, in an effort to provide a concise description of these embodiments, all features of an actual implementation may not be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.
When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Additionally, it should be understood that references to “one embodiment” or “an embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.
Rock properties (e.g., matrix properties) may influence a bulk formation response of well-logging measurements, and as such, it may be desirable to determine accurate rock properties to accurately interpret well-logging measurements with respect to certain formation properties, such as porosity, saturation, and permeability. For example, a rock property such as the matrix grain density may be used to provide an accurate determination of formation porosity using the measurement of formation bulk density. At least in some instances, certain rock properties may be difficult to measure directly or otherwise unobtainable and may be inferred from other measurable characteristics of the rock, such as elemental concentrations measured by downhole neutron spectroscopy, such as neutron-induced gamma-ray spectroscopy. Neutron-induced gamma-ray spectroscopy may be used to derive certain rock characteristics of a formation based on elemental concentrations within a geological formation. In neutron-induced gamma ray spectroscopy, fast neutrons and thermal neutrons generated from a naturally radioactive material or pulsed-neutron generator contained within the housing of the logging sonde (e.g., a portion of a well-logging tool that includes sensors) may interact with elemental nuclei in a geological formation and produce gamma radiation from inelastic nuclear reactions and neutron-capture reactions in a local volume surrounding the logging sonde. The produced gamma rays traverse the geological formation, and some of the gamma rays are detected by a detector (e.g., sensor) contained within the housing of the logging sonde. The detector signal is used to produce spectra indicating contributions from gamma rays representing the elemental nuclei in the formation. Gamma ray spectra associated with inelastic scattering and with thermal-neutron capture can be quantified separately. Inelastic and capture spectra may be plotted as count rate versus energy. The spectra contain information about the element identity (i.e., from the characteristic energies of the gamma rays) and about the element concentration (i.e., from the number or relative number of counts).
In any case, a mathematical method may be utilized to quantitatively estimate or infer certain rock properties (e.g., matrix properties) directly from elemental concentrations associated with certain elements such as silicon, calcium, iron, magnesium, and sulfur. These rock properties include, for example, one or more mineral concentrations in the rock matrix, matrix grain density, matrix neutron porosity response, and aluminum concentration in the rock matrix. One mathematical method for estimating rock properties uses linear regressions. For example, the matrix grain density may be estimated based on equation (Eq.) (1) below:
Further, aluminum concentration may be estimated based on Eq. 2:
At least in some instances, linear functions (e.g., Eq. 1 and Eq. 2) may not accurately capture certain mappings (e.g., a nonlinear mapping) that exists among the input(s) and output(s) in complex geological rock formations. Further, the linear functions may be based on assumptions regarding geochemical relationships among the measurable and interpreted features, which may limit their application. For example, the emulation of aluminum in Eq. 2 assumes calcium and magnesium are associated only with calcite, CaCO3, and dolomite, (Ca,Mg)CO3 (e.g., which does not apply in petroliferous formations containing, for example, anhydrite, CaSO4). The expression of Eq. 2 can be extended in a multi-step routine to adjust calcium for the presence of anhydrite, but such corrections may utilize assumptions about the allocation of elements among minerals. Moreover, existing linear regression models may not incorporate any estimate of the uncertainty on the estimated rock property that arises from the model parameters. Accordingly, the present disclosure is directed to techniques for determining the rock properties of a geological formation using a model to capture nonlinear relationships among the concentrations of measurable elements in geological rock formation(s) and certain rock properties of said rock formation(s).
With the foregoing in mind,
Moreover, while the downhole tool 12 is described as a wireline downhole tool, it should be appreciated that any suitable conveyance may be used. For example, the downhole tool 12 may instead be conveyed as a logging-while-drilling (LWD) tool as part of a bottom hole assembly (BHA) of a drill string, conveyed on a slickline or via coiled tubing, and so forth. For the purposes of this disclosure, the downhole tool 12 may be any suitable downhole tool that uses neutron-induced gamma-ray spectroscopy within the borehole 16 (e.g., downhole environment). The gamma-ray spectroscopy may include, but is not limited to, inelastic, capture, or delayed activation gamma-ray spectroscopy. For example, the gamma-ray spectroscopy may include any suitable neutron-induced gamma-ray spectroscopies.
As discussed further below, the downhole tool 12 may receive energy from an electrical energy device or an electrical energy storage device, such as the auxiliary power source 24 or another electrical energy source to power the tool. Additionally, in some embodiments the downhole tool 12 may include a power source within the downhole tool 12, such as a battery system or a capacitor to store sufficient electrical energy to activate the neutron emitter and record gamma-ray radiation.
Data signals 26 may be transmitted from a data processing system 28 to the downhole tool 12, and the data signals may be related to the spectroscopy results may be returned to the data processing system 28 from the downhole tool 12, additionally, the data signals 26 may include control signals. The data processing system 28 may be any electronic data processing system that can be used to carry out the systems and methods of this disclosure. For example, the data processing system 28 may include a processor 30, which may execute instructions stored in memory 32 and/or storage 34. As such, the memory 32 and/or the storage 34 of the data processing system 28 may be any suitable article of manufacture that can store the instructions. The memory 32 and/or the storage 34 may be read-only memory (ROM), random-access memory (RAM), flash memory, an optical storage medium, or a hard disk drive, to name a few examples. A display 36, which may be any suitable electronic display, may display images generated by the processor 30. The data processing system 28 may be a local component of the vehicle 20 (e.g., within the downhole tool 12), a remote device that analyzes data from other vehicles 20, a device located proximate to the drilling operation, or any combination thereof. In some embodiments, the data processing system 28 may be a mobile computing device (e.g., tablet, smart phone, or laptop) or a server remote from the vehicle 20.
With this in mind, and referring now to
In some embodiments, the mapping function is derived from a minimization of a cost function given a set of data including input data, output data, uncertainties in the input data, missing data, and data of different fidelities as captured by their uncertainties. The cost function may include a mean squared error function, a least squares error function, a maximum likelihood error function, a mean absolute error function, or a cross-entropy function. Additionally or alternatively, the cost function may include a regularization function configured to optimize accuracy and robustness. Further, the cost function may include a neural network, such as a Bayesian Neural Network (BNN) that may account for both data-driven (e.g., aleatoric) uncertainty and model-parameter-based (e.g., epistemic) uncertainty in the nonlinear mapping function
At block 44, the processor may provide the data as an input to a mapping function. In some embodiments, the mapping function may be a linear or a nonlinear mapping function. In an embodiment where the mapping function is a nonlinear mapping function, the mapping function may include a model such as an artificial neural network (ANN). In an embodiment where the mapping function includes an ANN, the mapping function may include an activation function that, for example, may introduce nonlinearities into the mapping function. In some embodiments, the mapping function may include a nonlinear regression or classification technique of machine learning such as a support vector machine, a decision tree, an extended neural network architecture that may comprise a recurrent network, a long-short-term memory (LSTM) network, an attention model, or a combination thereof.
At block 46, the processor 30 may receiving at least one parameter characterizing the geological formation. Put differently, the processor 30 may determine a rock property output based on the mapping function, such as a nonlinear mapping function, by providing the data characterizing the concentrations as an input to the nonlinear mapping function (e.g., the parameter may be one or more rock properties). In general, the rock property output may represent or indicate one or more rock properties of the geological formation. The one or more rock properties may include matrix grain density, matrix apparent thermal neutron porosity, matrix apparent epithermal neutron porosity, matrix hydrogen index, matrix permittivity, matrix thermal-neutron absorption cross section (e.g., matrix Sigma), matrix fast-neutron elastic scattering cross section, matrix photoelectric factor, matrix permeability, cation-exchange capacity (CEC) of the matrix, electrical conductivity or resistivity of the matrix, matrix elements not otherwise measurable via wellbore spectroscopy logging, matrix heat capacity, matrix enthalpy, matrix thermal conductivity, matrix reactivity rates with an acid, matrix reactivity rates with respect to carbon dioxide in various forms, capacity for injection of carbon dioxide into the matrix, elastic moduli or other mechanical properties of the matrix, among others.
To further illustrate the method 40 and application, a dataset(s) containing the measured elemental concentrations will be represented by E (model input), and a dataset(s) containing the estimated rock properties from the method will be represented by P′ (model output). In this example, the model input and output can be vectors or scalars and the symbols E and P are taken to represent either or both. The method 40 may use suitable machine learning techniques to infer or predict P′ from E. Such examples may include, but are not limited to, an artificial neural network (ANN), decision trees, support vector machines (SVMs), Bayesian networks, and regression analysis. The prime symbol on P′ indicates that it is an algorithmic prediction of the true value(s) P.
The one or more estimated rock properties of beneficial interest include but are not limited to: matrix grain density, matrix apparent thermal neutron porosity, matrix apparent epithermal neutron porosity, matrix hydrogen index, matrix permittivity, matrix thermal-neutron absorption cross section (matrix Sigma), matrix fast-neutron elastic scattering cross section, matrix photoelectric factor, matrix permeability, cation-exchange capacity (CEC) of the matrix, electrical conductivity or resistivity of the matrix, matrix elements not otherwise measurable via wellbore spectroscopy logging, matrix heat capacity, matrix enthalpy, matrix thermal conductivity, matrix reactivity rates with an acid, matrix reactivity rates with respect to carbon dioxide in various forms, capacity for injection of carbon dioxide into the matrix, elastic moduli or other mechanical properties of the matrix, among others. These rock properties (e.g., matrix properties), individually or in combinations, are utilized to make accurate interpretations of formation characteristics in routine upstream energy workflows; one example is the use of matrix grain density to compute formation porosity from a measurement of formation bulk density.
As discussed herein, certain mapping functions (e.g., a nonlinear mapping) may more accurately capture mappings that exists among the input(s) and output(s) in complex geological rock formations. Quantifiable relationships may be expected because the bulk concentrations of many elements in rock matrices may be dictated by the concentrations of minerals in the solid rock. For example, the minerals may have elemental compositions that are generally distinct from one another and that are fixed or nearly so. However, certain naturally occurring geological rock formations may contain more than two minerals such that relationships among elemental concentrations are not simple linear functions. This is illustrated in
Accordingly, it may be advantageous to utilize a nonlinear mapping, such as using an ANN.
The neural network may be trained using a feed-forward pass with activation through (hidden) layers between E and P′ where the values for the input and output are known in the training phase. A cost function (e.g., described in more detail herein) may be evaluated over the predicted values of the output P′ and the known (i.e., true) values of the output P is back-propagated to update the weights wij in each layer of the network. Thereafter, the values of the weights wij in the network can be used in the inference phase to predict an output P′ whose true values P are unknown from an input E whose values are measured or otherwise known.
In some embodiments for training a model, wherein the model is an ANN, both the input data E and the output data P should be known and accurate within accepted uncertainties to learn the mapping (e.g., network weights of the model) between the two. Once a model(s) is trained, rock properties P′ may be estimated from the input E without knowledge of the true rock properties P. In certain embodiments, data comprising E and P are rock characteristics, the true values of which are quantified using techniques known to those skilled in the art on formation samples represented by, e.g., drill core and drill cuttings. In the case of the reference inputs, elemental concentrations E may be derived from measurements using laboratory techniques including X-ray spectroscopy, such as X-ray fluorescence (XRF) spectroscopy, atomic absorption spectroscopy, mass spectrometry, neutron activation, or a combination thereof. The reference rock property or properties P may be derived from measurements using laboratory techniques (e.g., measurement of matrix grain density using helium pycnometry) or can be computed from first-principles physics (e.g., computation of matrix Sigma using known neutron cross sections of individual chemical elements).
In certain embodiments, the estimation of rock properties P′ during model inference may be derived from measurements of certain elemental concentrations E that are provided by neutron-induced gamma ray spectroscopy logging sondes, performed downhole within a borehole that traverses and is surrounded by an earth formation.
In other embodiments, in either or both training and prediction, the input data may not be the elemental concentrations themselves, but other data representation pertaining to the elemental concentrations (e.g., indirect elemental concentration data). For example, the input may be the directly measured X-ray spectrum of a formation sample because this spectrum contains the information pertaining to the identification (e.g., X-ray photon energy) and concentration (e.g., X-ray photon counts) of one or more elements in said formation. Similarly, the input could be the directly measured gamma-ray spectrum of a formation sample because in this spectrum is contained the information pertaining to the identification (gamma-ray energy) and concentration (gamma-ray counts) of one or more elements in said formation. The invented method herein is not limited to those inputs explicitly disclosed in this disclosure.
During training, a cost function may be evaluated using the predicted values P′ of the output and the known values of the output P. The weights wij in each layer of the network are updated via back-propagation so as to minimize the cost function. Once the weights wij for the entire network are optimized, the network can be used during inference to derive an estimate of a value for a one or more output P′, whose true values P are unknown, from the value of a one or more input E whose values are known (e.g., measured).
At least in some instances, uncertainties on the input data may be used in training. In general, the uncertainties indicate uncertainty in the output determined rock property. It should be noted that including uncertainties may be beneficial to build robustness to noise into the model during inference (i.e., estimation of rock property or properties values). The use of uncertainties on inputs during training can also benefit the estimation of uncertainties on the rock properties values that are output from the model during inference.
To further illustrate the disclosed techniques,
where ϕ is porosity; ρb is bulk density measured, for example, by a density logging sonde; and ρf is fluid density. When the calculation is performed in connection with measurements of formation samples obtained from a borehole or from measurements performed by a logging device in a borehole, the calculation may provide a continuous estimate of formation porosity as a function of depth along the borehole.
As a second example,
where Sw is water saturation; Σb, Σhc, Σw are, respectively, the Sigma values for the bulk formation, hydrocarbon (oil or gas), and water in the formation; and ϕ is porosity as introduced above. When the calculation is performed in connection with measurements of formation samples obtained from a borehole or from measurements performed by a logging device in a borehole, the calculation can provide a continuous estimate of water saturation as a function of depth along the borehole.
As a third example,
Accordingly, the present disclosure relates to determining one or more rock properties of an earth formation based on at least one measurement of elemental concentrations in a geological formation, using machine learning (ML) techniques, such as an artificial neural network (ANN) to compute the mapping from inputs values to the desired output(s) values. At least in some instances, such as when the model inference is performed in connection with measurements of formation samples obtained from a borehole or from measurements performed by a logging device in a borehole, the techniques may provide a continuous determination of formation rock properties as a function of depth along the borehole. In certain embodiments, these rock properties include one or more of, but not limited, to matrix grain density, matrix apparent thermal neutron porosity, matrix apparent epithermal neutron porosity, matrix hydrogen index, matrix permittivity, matrix thermal-neutron absorption cross section (e.g., matrix Sigma), matrix fast-neutron elastic cross section, matrix photoelectric factor, matrix permeability, cation-exchange capacity (CEC) of the matrix, electrical conductivity or resistivity of the matrix, matrix elements not otherwise measurable via wellbore spectroscopy logging, matrix heat capacity, matrix enthalpy, matrix thermal conductivity, matrix reactivity rates with respect to acids or carbon dioxide in various forms, capacity for injection of carbon dioxide into the matrix, and elastic moduli or other mechanical properties, among other properties.
The techniques presented and claimed herein are referenced and applied to material objects and concrete examples of a practical nature that demonstrably improve the present technical field and, as such, are not abstract, intangible or purely theoretical. Further, if any claims appended to the end of this specification contain one or more elements designated as “means for [perform]ing [a function] . . . ” or “step for [perform]ing [a function] . . . ”, it is intended that such elements are to be interpreted under 35 U.S.C. 112(f). However, for any claims containing elements designated in any other manner, it is intended that such elements are not to be interpreted under 35 U.S.C. § 112(f).
The specific embodiments described above have been shown by way of example, and it should be understood that these embodiments may be susceptible to various modifications and alternative forms. It should be further understood that the claims are not intended to be limited to the particular forms disclosed, but rather to cover all modifications, equivalents, and alternatives falling within the spirit and scope of this disclosure.
This application claims the benefit of U.S. Provisional Application No. 63/266,425 entitled “Rock Property Measurements Based on Spectroscopy Data,” filed Jan. 5, 2022, the disclosure of which is incorporated herein by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2023/010163 | 1/5/2023 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63266425 | Jan 2022 | US |
