Carbon sequestration and storage location selection and use

Abstract

Techniques for carbon sequestration and storage location selection are presented. The technique includes obtaining a range estimate of criteria for potential carbon sequestration and storage locations; selecting a random criterion value in the respective range estimates; forming a decision matrix according to the random criterion value; weighting the decision matrix to obtain a representative vector; scoring at least some of the potential carbon sequestration and storage locations according to the similarity of the respective potential carbon sequestration and storage location representative vectors to the best and worst vectors; ranking the potential carbon sequestration and storage places according to the scores; repeating the selecting, the forming, the weighting, the scoring, and the ranking a plurality of times to obtain a plurality of sets of scores and a plurality of rankings; displaying a visualization of the plurality of rankings; and selecting a carbon sequestration and storage location from among the potential carbon sequestration and storage locations based on the visualization.

Description

[0001] Cross-reference paragraphs

[0002] This application claims the benefit of U.S. Non-Provisional Application No. 18 / 156,700, filed January 19, 2023, entitled “CARBON SEQUENCE TRATION AND STORAGESITE SELECTION AND USAGE,” the disclosure of which is hereby incorporated herein by reference. Background Art

[0003] Carbon sequestration and capture is a technology used to reduce CO2 emissions into the atmosphere. Any of a variety of sites can be used for carbon sequestration and storage, such as abandoned oil reservoirs, coal seams, salt domes, and saline aquifers. However, site selection involves evaluating many factors, such as reservoir size, reservoir permeability, stability, and geological sealing factors. Choosing from among the many potential carbon sequestration and storage sites may require evaluating hundreds of criteria, exceeding human capability. Summary of the Invention

[0004] According to various embodiments, a method for selecting a carbon sequestration and storage site is provided. The method includes: obtaining a range estimate of multiple criteria for a plurality of potential carbon sequestration and storage sites; selecting random criteria values ​​from the respective range estimates of the multiple criteria for the plurality of potential carbon sequestration and storage sites; forming a decision matrix based on the random criteria values; weighting the decision matrix based on multiple criteria weights, wherein a plurality of potential carbon sequestration and storage site representative vectors are obtained; scoring at least some of the plurality of potential carbon sequestration and storage sites based on similarity of the respective potential carbon sequestration and storage site representative vectors to a best vector and a worst vector, wherein a set of scores for the potential carbon sequestration and storage sites is obtained; ranking the potential carbon sequestration and storage sites based on the set of scores; repeating the selecting, forming, weighting, scoring, and ranking multiple times, wherein a plurality of sets of scores and a plurality of rankings are obtained; displaying a visualization of the plurality of rankings; and selecting a carbon sequestration and storage site from the plurality of potential carbon sequestration and storage sites based on the visualization.

[0005] Various optional features of the above method embodiments include the following. The method may include sequestering carbon at a carbon sequestration and storage site. Forming the decision matrix may include normalizing the matrix formed based on the random criterion values. The method may include determining the plurality of criterion weights, wherein determining the plurality of criterion weights includes: obtaining a plurality of pairwise relative comparisons of the plurality of criteria from at least one evaluator; generating a weight matrix based on the plurality of relative comparisons; and determining eigenvectors of the weight matrix. Visualization may include: a plurality of image portions corresponding to at least some of the potential carbon sequestration and storage sites, wherein the image portion for each potential carbon sequestration and storage site includes a color coding representing the set of scores. Visualization may include: a plurality of curves corresponding to at least some of the potential carbon sequestration and storage sites, wherein the curve for each potential carbon sequestration and storage site indicates a distribution of scores for the respective potential carbon sequestration and storage site within the plurality of sets of scores. The method may include displaying a ranking of at least some of the potential carbon sequestration and storage sites according to a specified percentile of the distribution of scores for the respective potential carbon sequestration and storage sites within the plurality of sets of scores. The visualization may include: a plurality of curves corresponding to at least some of the potential carbon sequestration and storage sites on a graph including an x-axis and a y-axis, wherein the x-axis represents a probability of ranking within the N highest-ranked potential carbon sequestration and storage sites according to the plurality of rankings, and wherein the y-axis represents N. The method may include: selecting a subset of the plurality of potential carbon sequestration sites based on the visualization; repeating the selecting, forming, weighting, and ranking for the subset a plurality of times, wherein a second plurality of rankings is obtained; and displaying a second visualization of the second plurality of rankings, wherein the carbon sequestration and storage site is selected from the subset of potential carbon sequestration and storage sites based further on the second visualization. Forming the decision matrix based on the random criterion values ​​may include applying at least one rule to at least one of the random criterion values ​​to obtain a factor value for at least one criterion.

[0006] According to various embodiments, a system for carbon sequestration and storage site selection is presented. The system includes an electronic processor and a persistent storage device, the persistent storage device storing instructions that, when executed by the electronic processor, configure the electronic processor to perform actions including: obtaining range estimates of multiple criteria for multiple potential carbon sequestration and storage sites; selecting random criterion values ​​from the corresponding range estimates of the multiple criteria for the multiple potential carbon sequestration and storage sites; forming a decision matrix based on the random criterion values; weighting the decision matrix based on multiple criterion weights, wherein multiple potential carbon sequestration and storage site representative vectors are obtained; scoring at least some of the multiple potential carbon sequestration and storage sites based on similarities between the corresponding potential carbon sequestration and storage site representative vectors and the best vector and the worst vector, wherein a set of scores for the potential carbon sequestration and storage sites is obtained; ranking the potential carbon sequestration and storage sites based on the set of scores; repeating the selecting, forming, weighting, scoring, and ranking multiple times, wherein multiple sets of scores and multiple rankings are obtained; displaying a visualization of the multiple rankings; and selecting a carbon sequestration and storage site from the multiple potential carbon sequestration and storage sites based on the visualization.

[0007] Various optional features of the above system embodiments include the following. The system may include carbon sequestered at a carbon sequestration and storage site. Forming the decision matrix may include normalizing the matrix formed based on the random criterion values. The act may also include determining the plurality of criterion weights, wherein determining the plurality of criterion weights includes: obtaining a plurality of pairwise relative comparisons of the plurality of criteria from at least one evaluator; generating a weight matrix based on the plurality of relative comparisons; and determining eigenvectors of the weight matrix. Visualization may include: a plurality of image portions corresponding to at least some of the potential carbon sequestration and storage sites, wherein the image portion for each potential carbon sequestration and storage site includes a color coding representing the set of scores. Visualization may include: a plurality of curves corresponding to at least some of the potential carbon sequestration and storage sites, wherein the curve for each potential carbon sequestration and storage site indicates a distribution of scores for the respective potential carbon sequestration and storage site within the plurality of sets of scores. The act may also include displaying a ranking of at least some of the potential carbon sequestration and storage sites based on a specified percentile of the distribution of scores for the respective potential carbon sequestration and storage sites within the plurality of sets of scores. The visualization may include: a plurality of curves corresponding to at least some of the potential carbon sequestration and storage sites on a graph including an x-axis and a y-axis, wherein the x-axis represents a probability of ranking within the N highest-ranked potential carbon sequestration and storage sites according to the plurality of rankings, and wherein the y-axis represents N. The actions may also include: selecting a subset of the plurality of potential carbon sequestration sites based on the visualization; repeating the selecting, forming, weighting, and ranking for the subset a plurality of times, wherein a second plurality of rankings is obtained; and displaying a second visualization of the second plurality of rankings, wherein the carbon sequestration and storage site is selected from the subset of potential carbon sequestration and storage sites based further on the second visualization. Forming the decision matrix based on the random criterion values ​​may include applying at least one rule to at least one of the random criterion values ​​to obtain a factor value for at least one criterion.

[0008] This Summary is provided to introduce a selection of concepts that are further described below in the Detailed Description. This Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate the present teachings and, together with the described embodiments, serve to explain the principles of the present teachings. In the drawings:

[0010] Figure 1 An example of a wellsite system according to various embodiments is shown;

[0011] Figure 2 is a flow chart of a method for carbon sequestration and storage site selection according to various embodiments;

[0012] Figure 3 is a flow chart of a method of determining a score for a potential carbon sequestration and storage site according to various embodiments;

[0013] Figure 4 is a flow chart of a method for weighting criteria for evaluating carbon sequestration and storage sites according to various embodiments;

[0014] Figure 5 is a visualization of ranked potential carbon sequestration and storage sites according to various embodiments, showing color-coded scores;

[0015] Figure 6 is a visualization of ranked potential carbon sequestration and storage sites according to various embodiments, showing the distribution of scores;

[0016] Figure 7 is a visualization of ranked potential carbon sequestration and storage sites showing the probability of ranking within the N highest rankings according to various embodiments; and

[0017] Figure 8 An example computing system suitable for performing the methods of the present disclosure according to various embodiments is shown. DETAILED DESCRIPTION

[0018] Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth to provide a deeper understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without these specific details. In other cases, well-known methods, processes, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0019] It should also be understood that although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are merely used to distinguish one element from another. For example, a first object could be referred to as a second object, and similarly, a second object could be referred to as a first object, without departing from the scope of the present invention.

[0020] The first object and the second object are two objects respectively, but should not be considered as the same object.

[0021] The terms used in the description of the present invention are only used for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the description of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are also intended to include the plural forms. It should also be understood that the term "and / or" as used herein refers to and covers any possible combination of one or more of the associated listed items. It will also be understood that the terms "includes, including, comprises and / or comprising" when used in this specification specify the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups thereof. In addition, as used herein, the term "if" can be interpreted to mean "when...", "after..." or "in response to determining" or "in response to detecting", depending on the context.

[0022] Various embodiments can be used to assess potential carbon sequestration and storage sites, select one or more sites, and sequester and store carbon at the selected sites. Various embodiments can assess potential carbon sequestration and storage sites based on hundreds of criteria and objectively identify suitable sites. Thus, various embodiments not only automate manual processes but also provide techniques for absorbing hundreds of criteria across any number of sites and quantitatively assessing potential sites suitable for carbon sequestration and storage. These and other features and advantages are presented below with reference to the accompanying figures.

[0023] Figure 1 A wellsite system that can be used according to an example of the present disclosure is shown. The wellsite can be located onshore or offshore. In this example system, a drill string 100 is suspended in a borehole 102 formed in an underground formation 103. The drill string 100 has a bottom hole assembly (BHA) 104, the lower end of which includes a drill bit 105. The surface system 106 includes a platform and a derrick assembly positioned above the borehole 102, the assembly including a rotary table 108, a kelly (not shown), a hook 110, and a swivel 112. The drill string 100 is rotated by the rotary table 108, which is activated by a drive that engages the kelly (not shown) at the upper end of the drill string 100. The drill string 100 is suspended from the hook 110, which is attached to a traveling block (also not shown), by the kelly (not shown) and the swivel 112, which allows the drill string 100 to rotate relative to the hook 110. A top drive system can be used instead Figure 1 The rotating stage system shown.

[0024] In the example shown, the surface system 106 also includes drilling fluid or mud 114 stored in a pit 116 formed at the well site. A pump 118 delivers the drilling fluid to the interior of the drill string 100 via a port (not shown) in the sub 112, causing the drilling fluid to flow downward through the drill string 100, as indicated by directional arrows 120. The drilling fluid exits the drill string 100 via a port (not shown) in the drill bit 105 and then circulates upward through the annular region between the exterior of the drill string 100 and the wall of the borehole 102, as indicated by directional arrows 130A and 130B. In this manner, the drilling fluid lubricates the drill bit 105 and carries formation cuttings upward to the surface as it returns to the pit 116 for recirculation.

[0025] The BHA 104 of the illustrated embodiment may include a measurement while drilling (MWD) tool 132, a logging while drilling (LWD) tool 134, a rotary steerable directional drilling system 136 and motor, and a drill bit 105. It will also be understood that more than one LWD tool and / or MWD tool may be employed, such as indicated at 138.

[0026] The LWD tool 134 is housed in the drill collar and may include one or more logging tools. The LWD tool 134 may include capabilities for measuring, processing, and storing information, as well as for communicating with surface equipment. In this example, the LWD tool 134 may include one or more tools configured to measure, but not limited to, resistivity, acoustic velocity or slowness, neutron porosity, gamma-gamma density, neutron activation spectroscopy, nuclear magnetic resonance, and natural gamma emission spectroscopy.

[0027] The MWD tool 132 is also housed in the drill collar and may include one or more devices for measuring properties of the drill string and drill bit. The MWD tool 132 also includes equipment 140 for generating power for the downhole system. This may typically include a mud turbine generator powered by the flow of drilling fluid, although it will be understood that other power and / or battery systems may also be used. The MWD tool 132 may include (but is not limited to) one or more of the following types of measurement devices: a weight-on-bit measurement device, a torque measurement device, a vibration measurement device, an impact measurement device, a stick-slip measurement device, a direction measurement device, and an inclination measurement device. The power generation equipment 140 may also include a drilling fluid flow regulator for transmitting measurement and / or tool condition signals to the surface for detection and interpretation by the logging and control unit 142.

[0028] The system can be used to access a reservoir, such as an oil or natural gas reservoir. Once the reservoir is depleted, the reservoir can be used for carbon sequestration and storage. Specifically, the pores 102 can be used to transfer CO2 through the subsurface formation 103 to the depleted reservoir. According to various embodiments, the properties of the reservoir and the surrounding geological environment can be assessed for carbon sequestration and storage.

[0029] However, embodiments are not limited to evaluating depleted reservoirs as carbon sequestration and storage sites. More generally, embodiments can evaluate any potential carbon sequestration and storage site, including, as non-limiting examples, coal seams, salt domes, and saline aquifers.

[0030] Figure 2 is a flow chart of a method 200 for carbon sequestration and storage site selection according to various embodiments. The method can be used to generate one or more visualizations of an automatic, objective ranking of potential carbon sequestration and storage sites. According to some embodiments, many (e.g., tens, hundreds, thousands, or more) objective rankings can be visualized according to the method 200. For example, the method 200 can be used as described herein with reference to Figure 7 The system 700 shown and described is implemented.

[0031] Method 200 can utilize multiple (e.g., tens, hundreds, thousands, or more) objective criteria by which to rank multiple potential carbon sequestration and storage sites. Such criteria can include any one or any combination of the following criteria: capacity criteria (e.g., reservoir area, total reservoir thickness, etc.), injectivity criteria, closure criteria (e.g., sealing performance of natural fractures, reservoir depth), loss of closure impact criteria (e.g., impact on health and safety, impact on the environment), cost factors (e.g., injection phase costs, monitoring costs), and / or legal and public factors (e.g., legal and regulatory favorability, ownership favorability).

[0032] Generally speaking, the method 200 includes a Monte Carlo simulation 210, wherein a number (e.g., hundreds, thousands, or more) of Monte Carlo simulation iterations 220 are performed, each Monte Carlo simulation iteration 220 generating a ranking of potential carbon sequestration and storage sites. Each Monte Carlo simulation iteration 220 utilizes reasonable but random values ​​for the criteria for each potential carbon sequestration and storage site.

[0033] Thus, at 212, method 200 includes generating Monte Carlo samples for each criterion for each potential carbon sequestration and storage site. According to various embodiments, this action can be performed once, or for each simulation, such as as part of each individual Monte Carlo simulation iteration 220. Generally speaking, the Monte Carlo samples can be based on a range estimate for each criterion for each potential carbon sequestration and storage site. Therefore, method 200 can include obtaining range estimates for some or all of the criteria for the potential carbon sequestration and storage site. According to various embodiments, the range estimates can take the form of a probability distribution. Generally speaking, the range estimates can be quantitative and in the form of any combination of probability distributions (e.g., uniform distribution, normal distribution, etc.), intervals, range limits, etc. The range estimates can be based on measurements, testing, or other empirical determinations conducted at or for each specific potential carbon sequestration and storage site. Monte Carlo sample values ​​can be based on the range estimates by selecting random values ​​based on the corresponding range estimates. For example, for a probability distribution, individual samples can be determined by selecting uniform random numbers and mapping them to a distribution. Some criteria may allow for discrete ranges of values, such as excellent, good, adequate, suboptimal, and unacceptable. For example, a discretized probability distribution can be used to characterize such discrete value ranges.

[0034] At 222, method 200 includes selecting a sample combination of random criteria values ​​for all criteria for all potential carbon sequestration and storage sites. Thus, for each potential carbon sequestration and storage site, a value for each criterion is selected that satisfies the range estimate for that particular site. The sample combination may be selected so as not to overlap with a sample combination already selected in a previous Monte Carlo simulation iteration 222.

[0035] At 224, method 200 calculates a factor value for each criterion at each location based on predefined rules. The predefined rules may map random criterion values ​​to factor values ​​used in subsequent processing steps of method 200. For example, according to some embodiments, one or more random criterion values ​​may themselves be used for corresponding factor values. As another example, the rules may apply any one or any combination of the following mathematical functions to one or more random criterion values: log, ln, exp, x, y ... -1 、x -2 etc. to obtain corresponding factor values. Alternatively or additionally, the method can use one or more rules to convert discrete random criterion values ​​into factor values, according to 224. As an example, the random criterion values ​​can be selected from the following ranges: excellent, good, adequate, suboptimal, and unacceptable. According to this example, the rules can map these values ​​to numerical values, such as 5, 4, 3, 2, and 1, respectively, and can map the numerical values ​​to factor values, such as using any of the mathematical functions described above or other rules.

[0036] At 226, method 200 determines a score for each potential carbon sequestration and storage site based on the factor values ​​calculated at 224. The action of 226 may be performed using, for example, a method such as that described herein with reference to Figure 3 The techniques of the method 300 are shown and described. According to various embodiments, the score may be a numerical value.

[0037] At 228, method 200 ranks the potential carbon sequestration and storage sites based on the scores determined at 226. Ranking can include sorting the sites according to their respective scores, for example, from lowest to highest (or vice versa). Ranking can be performed by associating the enumeration with the potential carbon sequestration and storage sites (e.g., in a persistent electronic memory). The result of 228 is a ranking of the potential carbon sequestration and storage sites.

[0038] At 232, method 200 visualizes the rankings generated at 228. For example, the visualization can be displayed on a computer monitor. For example, the visualization can be such as that shown in the table above. Figure 5 、 Figure 6 or Figure 7 Any of the visualizations shown and described.

[0039] At 234, a site is selected for carbon sequestration and storage from among potential carbon sequestration and storage sites. One or more sites may be selected.

[0040] According to some embodiments, a subset of potential carbon sequestration and storage sites is selected at 234, and the operations of 212, 222, 224, 226, 228, and 232 are repeated using the subset as described herein with reference to a plurality of carbon sequestration and storage sites. Such repetitions may result in further refined subsets, i.e., subsets of subsets. This entire iterative process itself may be performed once or more than once to produce a final subset representing the most desirable carbon sequestration and storage sites from among the plurality of carbon sequestration and storage sites.

[0041] At 236, the carbon (eg, CO2) is sequestered and stored at a selected carbon sequestration and storage site. For example, this process can be performed by injecting the carbon into an underground reservoir.

[0042] Figure 3 is a flow chart of a method 300 for determining a score for a potential carbon sequestration and storage site according to various embodiments. The method 300 may be used in 226 of the method 200. For example, the method 300 may be used as described herein with reference to Figure 7 The system 700 shown and described is implemented as follows. As a non-limiting example, the method 300 may utilize a technique for prioritizing similar solutions to ideal solutions (TOPSIS).

[0043] At 302, method 300 generates an assessment matrix, also referred to herein as a decision matrix. The assessment matrix may be an m×n matrix, where m is the number of potential carbon sequestration and storage sites and n is the number of criteria. Thus, the assessment matrix may have m rows, one for each potential carbon sequestration and storage site, and n columns, one for each criterion. The cells of the assessment matrix are populated with the corresponding factor values ​​for the criteria for each potential carbon sequestration and storage site. For example, the assessment matrix may be as described herein with reference to Figure 2 The factor values ​​are shown and described with reference to 212, 222, and 224 of method 200. The evaluation matrix may also be normalized at 302.

[0044] At 304, method 300 weights the evaluation matrix to obtain a weighted evaluation matrix. This may include multiplying the factor value in each cell of the evaluation matrix by the corresponding weight to obtain a weighted factor value. For example, the weights may be calculated using the weights as described herein. Figure 4 The method 400 shown and described is used to determine weights. Generally speaking, the weights indicate the relative importance of each criterion. The rows in the weighted assessment can be representative vectors of potential carbon sequestration and storage sites.

[0045] At 306, method 300 determines a best vector and a worst vector corresponding to a theoretically best carbon sequestration and storage location and a theoretically worst carbon sequestration and storage location, respectively. Each of these vectors may include n components, respectively. The best vector may be formed by selecting a weighted factor value for each criterion from the weighted factor values ​​for such criterion obtained at 304, such that the selected weighted factor value for a given criterion represents the most favorable weighted factor value for such criterion. The worst vector may be formed by selecting a weighted factor value for each criterion from the weighted factor values ​​for such criterion obtained at 304, such that the selected weighted factor value for a given criterion represents the most unfavorable weighted factor value for such criterion.

[0046] At 308, method 300 determines the distances of the representative vector of the potential carbon sequestration and storage site from each of the best vector and the worst vector. These distances can be calculated as Euclidean distances in n-dimensional space.

[0047] At 310, the potential carbon sequestration and storage site may be scored based on the distances determined at 308. For a given potential carbon sequestration and storage site, a score may be determined based on how close its representative vector is to the best vector and how far its representative vector is from the worst vector. As a non-limiting example, the score for a given potential carbon sequestration and storage site may be calculated as: S = d w / (d w +d b ), where d wThe distance between the representative vector representing the carbon sequestration and storage location and the worst vector, and d b The distance between the representative vector representing the carbon sequestration and storage site and the optimal vector. Thus, 310 generates a quantitative score for each of the carbon sequestration and storage sites. The score can be found in the referenced Figure 2 The method 200 shown and described is used at 226.

[0048] Figure 4 is a flow chart of a method 400 for weighting criteria for evaluating carbon sequestration and storage sites according to various embodiments. The method 400 may be used to obtain the weights used at 304 of the method 300, as described herein with reference to Figure 3 For example, the method 400 may be performed using the method described herein. Figure 7 The system 700 shown and described is implemented. As a non-limiting example, the method 400 may utilize the Analytic Hierarchy Process (AHP).

[0049] At 402, method 400 obtains a pairwise comparison of criteria. The comparison attributes a quantitative value to each pair of criteria, indicating the relative importance of the two criteria. For example, a first criterion in a pair can be compared to a second criterion in the pair, and the number attributed to the comparison indicates how much more important the first criterion is compared to the second criterion. A standard scale can be used, for example, with 1 indicating that the criteria are equally important, up to 10 indicating that the first criterion is much more important than the second criterion. The comparison can be obtained from a user. Multiple users can contribute to the comparison. According to some embodiments, an average of the comparison values ​​provided by multiple users can be used. The comparison can be obtained using a user interface and stored in persistent storage.

[0050] At 404, a weight matrix is ​​generated. The weight matrix may be n×n, where n is the number of criteria. The columns and rows may represent n criteria in order, where the cell of the matrix at (i, j) may include a weight for comparing the i-th criterion to the j-th criterion. In this case, the entry in cell (j, i) may be the inverse of the value in cell (i, j). For example, if w i,j represents the entry in cell (i, j), then w j,i =1 / w i,j The diagonal cells of the matrix (indicating the comparison of a criterion with itself) can be filled with the value 1.

[0051] At 406, the eigenvectors of the weight matrix are calculated, such as the right principal eigenvector. Any technique may be used to calculate the eigenvectors. For example, an algorithm based on the Caley-Hamilton theorem may be used. The eigenvectors may include n entries.

[0052] At 408, weights may be extracted from the normalized eigenvector. For example, the eigenvector may be normalized so that the sum of its n entries is one, and these entries may then be used as weights to sort using the same criteria enumeration used to sort the criteria in 406. The weights may be stored in persistent memory and used as described herein. Figure 3 The method 300 shown and described is used at 304 .

[0053] According to some embodiments, for example, ranking potential carbon sequestration and storage sites using methods 200, 300, and 400 utilizes significantly fewer comparisons than techniques that require pairwise comparisons of each pair of potential carbon sequestration and storage sites for each criterion. Some such embodiments may reduce the number of user comparison inputs by 75% or more.

[0054] Figure 5 5 is a visualization 500 of ranked potential carbon sequestration and storage sites according to various embodiments, showing color-coded scores. For example, visualization 500 can be displayed on a computer monitor. Visualization 500 is for nine potential carbon sequestration and storage sites, as a non-limiting example, represented along the x-axis. The y-axis represents individual Monte Carlo iterations, for example, as described herein with reference to Figure 2 The iteration 220 of the Monte Carlo simulation 210 shown and described occurs.

[0055] In this potential carbon sequestration and storage site selection example, each of the nine potential carbon sequestration and storage sites was evaluated based on 1,000 different possible input combinations. Thus, visualization 500 displays 9,000 scores on a shaded axis, where the top of the axis represents a high score and the bottom of the axis represents a low score. Note that embodiments may utilize shading or color to represent scores. Figure 5 As shown, the shading exhibits distinct vertical streaks. For example, the sixth potential carbon sequestration and storage site has the most pronounced shading streaks near the top of the shaded axis, while the fifth potential carbon sequestration and storage site has the most pronounced shading streaks near the bottom of the shaded axis. Thus, visualization 500 provides a visual summary of which potential carbon sequestration and storage sites may emerge as top candidates.

[0056] Thus, visualization 500 shows a plurality of image portions (vertical stripes), each portion corresponding to a different potential carbon sequestration and storage site. Each image portion includes a shading or color coding representing the fraction of the corresponding potential carbon sequestration and storage site across iterations of the Monte Carlo simulation.

[0057] In addition, visualization 500 provides some information about the variation in the scores of specific potential carbon sequestration and storage sites. For example, the first potential carbon sequestration and storage site has a more consistent shading across all Monte Carlo simulation iterations than the last potential carbon sequestration and storage site, indicating that the score of the first site is relatively insensitive to input uncertainty.

[0058] Figure 6 6 is a visualization 600 of ranked potential carbon sequestration and storage sites according to various embodiments, showing a distribution of scores. For example, visualization 600 can be displayed on a computer monitor. Visualization 600 corresponds to a distribution of scores for potential carbon sequestration and storage sites. More specifically, a Monte Carlo simulation (e.g., 210 of method 200) can generate multiple sets of scores for potential carbon sequestration and storage sites, with each iteration generating one set of scores (e.g., 220 of method 200). For a given carbon sequestration and storage site, its score can be obtained from the multiple sets of scores. These scores for a given carbon sequestration and storage site from the Monte Carlo iterations can form a distribution of scores, for example, a normal distribution of scores. The distribution can be represented as a curve. Figure 6 Visualization 600 depicts such curves for multiple carbon sequestration and storage sites.

[0059] In summary, visualization 600 shows a plurality of curves corresponding to a plurality of potential carbon sequestration and storage sites.The curve for a given potential carbon sequestration and storage site indicates the distribution of the fraction of that potential carbon sequestration and storage site among iterations from the Monte Carlo simulation.

[0060] and Figure 6 Related to visualization 600, potential carbon sequestration and storage sites can be ranked based on a specified percentile of their score distribution. Example specified percentiles include the 10th percentile, the 50th percentile, and the 90th percentile. In more detail, a specified percentile value for each of the score distributions can be determined, for example, as Figure 6 As depicted in visualization 600, each potential carbon sequestration and storage site is associated with a value at a specified percentile. Potential carbon sequestration and storage sites can be ranked according to such values. Potential carbon sequestration and storage sites can be ranked according to a specified percentile of a distribution of scores for a plurality of sets of potential carbon sequestration and storage sites. The following table depicts the potential carbon sequestration and storage sites of the present invention. Figure 6 Three examples of such rankings are given for the 10th, 50th, and 90th percentiles of the nine potential carbon sequestration and storage sites discussed.

[0061] 10th percentile 50th percentile 90th percentile Location 6 Location 6 Location 6 Location 4 Location 4 Location 4 Location 8 Location 8 Location 8 Location 1 Location 1 Location 1 Location 7 Location 7 Location 7 Location 9 Location 3 Location 2 Location 2 Location 2 Location 3 Location 3 Location 9 Location 9 Location 5 Location 5 Location 5

[0062] sheet

[0063] Figure 7 700 is a visualization of ranked potential carbon sequestration and storage sites, showing the probability of ranking within the top N rankings, according to various embodiments. For example, visualization 700 can be displayed on a computer monitor. In visualization 700, each potential carbon sequestration and storage site is represented by a particular curve, and the y-axis indicates the probability of the particular potential carbon sequestration and storage site ranking within the top N sites across multiple Monte Carlo simulation iterations. The x-axis indicates the value of N.

[0064] Thus, for example, site 6 has approximately a 67% probability of being the optimal design (N=1) and a nearly 100% probability of being within the top three, fourth, fifth, etc., ranked potential carbon sequestration and storage sites. Site 8 has less than a 10% probability of being the optimal design and an almost 100% probability of being within the top three, fourth, fifth, etc., ranked potential carbon sequestration and storage sites.

[0065] Another feature that visualization 700 illustrates is the existence of so-called "probability reversal" scenarios. For example, location 3 falls within the top four locations 6% of the time, and location 9 falls within the top four locations 8% of the time. However, location 3 falls within the top five 26% of the time, while location 9 only falls within the top five 22% of the time. This "probability reversal" occurs whenever two lines in visualization 700 cross. In such a scenario, the suitability of a location will depend on the selected value of N. Therefore, if a decision maker is unsure which N to examine (e.g., there is leeway in how many locations to select for further study), sensitivity data such as in visualization 700 can provide information.

[0066] Figure 8An example computing system 800 suitable for performing the methods of the present disclosure according to various embodiments is shown. Computing system 800 may include a computer or computer system 801A, which may be a single computer system 801A or an arrangement of distributed computer systems. Computer system 801A includes one or more analysis modules 802 configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, analysis module 802 executes independently or in conjunction with one or more processors 804, which are connected to one or more storage media 806. Processor 804 is also connected to a network interface 807 to allow computer system 801A to communicate with one or more additional computer systems and / or computing systems, such as 801B, 801C, and / or 801D, over a data network 809 (note that computer systems 801B, 801C, and / or 801D may or may not share the same architecture as computer system 801A and may be located in different physical locations, e.g., computer systems 801A and 801B may be located in a processing facility while communicating with one or more computer systems, such as 801C and / or 801D, located in one or more data centers and / or in different countries on different continents).

[0067] A processor may include a microprocessor, a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, or another control or computing device.

[0068] The storage medium 806 may be implemented as one or more computer-readable or machine-readable storage media. Figure 8 In the example embodiment of the present invention, the storage medium 806 is depicted as being within the computer system 801A, but in some embodiments, the storage medium 806 can be distributed within and / or across multiple internal and / or external enclosures of the computing system 801A and / or additional computing systems. The storage medium 806 can include one or more different forms of memory, including semiconductor memory devices, such as dynamic or static random access memory (DRAM or SRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory; magnetic disks, such as fixed disks, floppy disks, and removable disks; other magnetic media, including magnetic tape; optical media, such as compact disks (CDs) or digital video disks (DVDs); disk or other type of optical storage medium, or other type of storage device. It should be noted that the instructions discussed above can be provided on one computer-readable or machine-readable storage medium, or alternatively can be provided on multiple computer-readable or machine-readable storage media distributed in a larger system that may have multiple nodes. Such one or more computer-readable or machine-readable storage media are considered to be part of an article (or product). An article or product can refer to any manufactured single component or multiple components. The one or more storage media can be located in the machine that runs the machine-readable instructions, or in a remote location, from which the machine-readable instructions can be downloaded over a network for execution.

[0069] In some embodiments, computing system 800 includes one or more location assessment modules 808. Location assessment modules 808 may perform one or more of methods 200, 300, and / or 400, as described herein, respectively. Figure 2 、 Figure 3 and Figure 4 As shown and described. In the example of computing system 800, computer system 801A includes a location assessment module 808. In some embodiments, location assessment module 800 can be used to perform some or all aspects of one or more embodiments of the methods disclosed herein. In alternative embodiments, multiple location assessment modules, such as location assessment module 808, can be used to perform some or all aspects of the methods disclosed herein.

[0070] It should be understood that computing system 800 is only one example of a computing system and that computing system 800 may have more or fewer components than shown and may be combined with other computing systems. Figure 8 Additional components not depicted in the example embodiment of FIG, and / or computing system 800 may have Figure 8 Different configurations or arrangements of the components depicted in . Figure 8 The various components shown in the can be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0071] In addition, the steps in the processing method described herein can be implemented by running one or more functional modules in an information processing device (such as a general-purpose processor or a dedicated chip (such as an ASIC, FPGA, PLD, or other appropriate device)). These modules, combinations of these modules, and / or their combination with basic hardware are all included in the scope of protection of the present invention.

[0072] For the purpose of explanation, the above description has been described with reference to specific embodiments. However, the above illustrative discussion is not intended to be exhaustive or to limit the present invention to disclosed precise forms. In view of the above teachings, many modifications and variations may be possible. In addition, the order of the elements of the illustrated and described methods can be rearranged, and / or two or more elements can occur simultaneously. These embodiments are selected and described to best explain the principle of the present invention and its practical application, thereby allowing other technical personnel in the art to best utilize the present invention and various embodiments when carrying out various modifications as are suitable for the specific use expected.

Claims

1. A method for carbon sequestration and storage site selection, the method comprising: Obtain range estimates for multiple criteria for multiple potential carbon sequestration and storage sites; selecting a random criterion value within corresponding range estimates of the plurality of criteria for the plurality of potential carbon sequestration and storage sites; forming a decision matrix based on the random criterion values; weighting the decision matrix according to a plurality of criterion weights, wherein a plurality of potential carbon sequestration and storage site representative vectors are obtained; scoring at least some of the plurality of potential carbon sequestration and storage sites based on similarities of corresponding potential carbon sequestration and storage site representative vectors to the best vector and the worst vector, wherein a score set for the potential carbon sequestration and storage sites is obtained; ranking said potential carbon sequestration and storage sites according to said set of scores; repeating the selecting, the forming, the weighting, the scoring, and the ranking a plurality of times, wherein a plurality of sets of scores and a plurality of rankings are obtained; displaying a visualization of the plurality of rankings; as well as A carbon sequestration and storage site is selected from among the plurality of potential carbon sequestration and storage sites based on the visualization.

2. The method of claim 1 , further comprising sequestering carbon at the carbon sequestration and storage site.

3. The method of claim 1, wherein forming the decision matrix comprises normalizing a matrix formed from the random criterion values.

4. The method of claim 1 , further comprising determining the plurality of criterion weights, wherein determining the plurality of criterion weights comprises: obtaining a plurality of pairwise relative comparisons of the plurality of criteria from at least one evaluator; generating a weight matrix based on the plurality of relative comparisons; as well as The eigenvectors of the weight matrix are determined.

5. The method of claim 1 , wherein the visualization comprises: a plurality of image portions corresponding to at least some of the potential carbon sequestration and storage sites, The portion of the image corresponding to the potential carbon sequestration and storage site includes a color code representing the score set.

6. The method of claim 1, wherein the visualization comprises: a plurality of curves corresponding to at least some of the potential carbon sequestration and storage sites, The curves for the respective potential carbon sequestration and storage sites indicate the distribution of the scores of the respective potential carbon sequestration and storage sites in the plurality of sets of scores.

7. The method of claim 1 , further comprising displaying a ranking of at least some of the potential carbon sequestration and storage sites according to a specified percentile of a distribution of respective scores of the potential carbon sequestration and storage sites within the plurality of sets of scores.

8. The method of claim 1 , wherein the visualization comprises: a plurality of curves corresponding to at least some of the potential carbon sequestration and storage sites on a graph comprising an x-axis and a y-axis, Wherein the x-axis represents a probability of being ranked within the N highest ranked potential carbon sequestration and storage sites according to the plurality of rankings, and wherein the y-axis represents N.

9. The method of claim 1 , further comprising: selecting a subset of the plurality of potential carbon sequestration sites based on the visualization; repeating the selecting, forming, weighting, and ranking for the subset a plurality of times, wherein a second plurality of rankings is obtained; as well as A second visualization showing the second plurality of rankings, The carbon sequestration and storage site is selected from among the subset of potential carbon sequestration and storage sites based further on the second visualization.

10. The method of claim 1, wherein forming the decision matrix from the random criterion values ​​comprises applying at least one rule to at least one of the random criterion values ​​to obtain a factor value for at least one criterion.

11. A system for carbon sequestration and storage site selection, the system comprising an electronic processor and a persistent storage device, the persistent storage device storing instructions that, when executed by the electronic processor, configure the electronic processor to perform actions comprising: Obtain range estimates for multiple criteria for multiple potential carbon sequestration and storage sites; selecting a random criterion value within corresponding range estimates of the plurality of criteria for the plurality of potential carbon sequestration and storage sites; forming a decision matrix based on the random criterion values; weighting the decision matrix according to a plurality of criterion weights, wherein a plurality of potential carbon sequestration and storage site representative vectors are obtained; scoring at least some of the plurality of potential carbon sequestration and storage sites based on similarities of corresponding potential carbon sequestration and storage site representative vectors to the best vector and the worst vector, wherein a score set for the potential carbon sequestration and storage sites is obtained; ranking said potential carbon sequestration and storage sites according to said set of scores; repeating the selecting, the forming, the weighting, the scoring, and the ranking a plurality of times, wherein a plurality of sets of scores and a plurality of rankings are obtained; displaying a visualization of the plurality of rankings; as well as A carbon sequestration and storage site is selected from among the plurality of potential carbon sequestration and storage sites based on the visualization.

12. The system of claim 11, further comprising carbon sequestered at the carbon sequestration and storage site.

13. The system of claim 11, wherein the forming the decision matrix comprises normalizing a matrix formed from the random criterion values.

14. The system of claim 11, wherein the actions further comprise determining the plurality of criteria weights, wherein the determining the plurality of criteria weights comprises: obtaining a plurality of pairwise relative comparisons of the plurality of criteria from at least one evaluator; generating a weight matrix based on the plurality of relative comparisons; as well as The eigenvectors of the weight matrix are determined.

15. The system of claim 11, wherein the visualization comprises: a plurality of image portions corresponding to at least some of the potential carbon sequestration and storage sites, The portion of the image corresponding to the potential carbon sequestration and storage site includes a color code representing the score set.

16. The system of claim 11, wherein the visualization comprises: a plurality of curves corresponding to at least some of the potential carbon sequestration and storage sites, The curves for the respective potential carbon sequestration and storage sites indicate the distribution of the scores of the respective potential carbon sequestration and storage sites in the plurality of sets of scores.

17. The system of claim 11, wherein the actions further comprise displaying a ranking of at least some of the potential carbon sequestration and storage sites according to a specified percentile of a distribution of respective scores of the potential carbon sequestration and storage sites in the plurality of sets of scores.

18. The system of claim 11, wherein the visualization comprises: a plurality of curves corresponding to at least some of the potential carbon sequestration and storage sites on a graph comprising an x-axis and a y-axis, Wherein the x-axis represents a probability of being ranked within the N highest ranked potential carbon sequestration and storage sites according to the plurality of rankings, and wherein the y-axis represents N.

19. The system of claim 11, wherein the actions further comprise: selecting a subset of the plurality of potential carbon sequestration sites based on the visualization; repeating the selecting, forming, weighting, and ranking for the subset a plurality of times, wherein a second plurality of rankings is obtained; as well as A second visualization showing the second plurality of rankings, The carbon sequestration and storage site is selected from among the subset of potential carbon sequestration and storage sites based further on the second visualization.

20. The system of claim 11, wherein forming the decision matrix based on the random criterion values ​​comprises applying at least one rule to at least one of the random criterion values ​​to obtain a factor value for at least one criterion.

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