WO2013176771A1 - Method for analysis of relevance and interdependencies in geoscience data - Google Patents
Method for analysis of relevance and interdependencies in geoscience data Download PDFInfo
- Publication number
- WO2013176771A1 WO2013176771A1 PCT/US2013/032549 US2013032549W WO2013176771A1 WO 2013176771 A1 WO2013176771 A1 WO 2013176771A1 US 2013032549 W US2013032549 W US 2013032549W WO 2013176771 A1 WO2013176771 A1 WO 2013176771A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- measure
- data
- entropy
- attributes
- information
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000004458 analytical method Methods 0.000 title claims description 30
- 238000007405 data analysis Methods 0.000 claims abstract description 23
- 238000010586 diagram Methods 0.000 claims description 16
- 238000011156 evaluation Methods 0.000 claims description 3
- 230000001186 cumulative effect Effects 0.000 claims description 2
- 238000009795 derivation Methods 0.000 claims description 2
- 238000005315 distribution function Methods 0.000 claims description 2
- 229930195733 hydrocarbon Natural products 0.000 claims description 2
- 150000002430 hydrocarbons Chemical class 0.000 claims description 2
- 230000009466 transformation Effects 0.000 description 38
- 230000006870 function Effects 0.000 description 25
- 230000014509 gene expression Effects 0.000 description 18
- 238000009826 distribution Methods 0.000 description 16
- 238000013459 approach Methods 0.000 description 10
- 230000008569 process Effects 0.000 description 10
- 230000008901 benefit Effects 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 6
- 238000009635 antibiotic susceptibility testing Methods 0.000 description 5
- 238000013461 design Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 3
- 230000003750 conditioning effect Effects 0.000 description 3
- 230000007717 exclusion Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000005481 NMR spectroscopy Methods 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000013519 translation Methods 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 101100333320 Neurospora crassa (strain ATCC 24698 / 74-OR23-1A / CBS 708.71 / DSM 1257 / FGSC 987) end-3 gene Proteins 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 230000003278 mimic effect Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 238000011524 similarity measure Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000011179 visual inspection Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
- G01V1/362—Effecting static or dynamic corrections; Stacking
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V20/00—Geomodelling in general
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/758—Involving statistics of pixels or of feature values, e.g. histogram matching
Definitions
- This disclosure relates generally to the field of geophysical prospecting and, more particularly, to the analysis of geoscience data, including meta-data. More specifically, this disclosure describes a method for analysis of dependencies, relevance and independent content within multi-dimensional or multi-attribute geophysical data.
- the analysis of earth science data often involves the simultaneous interpretation of data and its many derived attributes.
- An attribute of the data is a broadly defined term meaning any quantity computed or otherwise derived from the data, including the data themselves.
- the use of different data sources or types and of their derived attributes helps geophysicists to have a better understanding of the subsurface by providing alternative perspectives.
- the main drawback of this approach has been the increasing number of data elements (i.e., data sources or data sets, data types, or data attributes) because of the increasing number of alternative and complex scenarios that must be considered for analysis, which tends to overload geophysicists when they try to manually combine the different data elements into their interpretation.
- the example highlights a number of problems, such as the fact that geophysicists do not know beforehand whether a data element has the information they need, or if it is redundant because of other data elements already being considered, or if a given relationship between data elements exists and, if it does, where in the data, or which parameter value might be better to highlight a feature in a given set of data elements.
- problems such as the fact that geophysicists do not know beforehand whether a data element has the information they need, or if it is redundant because of other data elements already being considered, or if a given relationship between data elements exists and, if it does, where in the data, or which parameter value might be better to highlight a feature in a given set of data elements.
- For each of these problems one can ask a specific question for which one can formulate, implement, and apply a specific measure or method to answer the question. Indeed, for specific questions and in very limited settings, a number of methods have been described in the literature. However, this approach is very cumbersome in a general paradigm
- US Patent Application Publication No. 2010/0312477 "Automated log quality monitoring systems and methods," by W. C. Sanstrom and R. E. Chemali, discloses a method to analyze the data quality of well log recordings involving the application of a comparison function to determine a log quality indicator.
- the invention is a method for evaluating a geoscience data analysis question, comprising: (a) inputting the data analysis question to a computer through a user interface, said data analysis question pertaining to one or more geophysical data elements; (b) using the computer to perform an automated derivation of a measure to evaluate the data analysis question; and (c) inputting the one or more geophysical data elements to the computer, computing the derived measure from the data elements, and using it to evaluate the data analysis question.
- the geoscience data analysis question is one that, when answered, contributes to exploration for or production of hydrocarbons.
- Fig. 1 is a flowchart showing basic steps in an embodiment of the present invention in which the user selects the data to be analyzed;
- Fig. 2 is a flowchart showing basic steps in an embodiment of the present inventive method using pre-selected data
- Fig. 3 is a flowchart showing the method of Fig. 1 with optional conditioning of the result added;
- Figs. 4A-4D show examples of a Venn diagram analysis interface with three input data sources denoting different analysis queries
- Fig. 5 shows an example of a graph structure analysis interface with three input data sources
- Fig. 6 shows the results of applying the present inventive method to two synthetic data sets.
- This invention provides a framework that gives geophysical data analysts
- this framework simplifies the geophysical data analysis process from the analyst's perspective. This is achieved by automating the process of determining an approach of how to answer the analysis question (the "transformation system” described below) and executing that approach to obtain a result (the below-described "computation system”).
- the user can focus exclusively on understanding the geologic and geophysical meaning and significance of the data, which is the ultimate goal of the analysis.
- the computational processes can, if desired, be transparent to the analyst.
- the present inventive method comprises three main elements, as illustrated in the flowchart of Fig. 1 : an interface 11 for the user to specify to a computer the question of interest, a transformation system 12, programmed into the computer, that automatically builds and implements a quantitative measure to try answering the user's question, and a computation system 13, also programmed into the computer, that computes that measure from the provided data 14. The results can then be presented back to the user, stored, or passed along to another process or system downstream, with or without conditioning. It is noteworthy that the user interface and the transformation system may be integrated into a single element in some embodiments of the invention.
- the invention focuses on analyses that can be formulated from a statistical data analysis perspective.
- questions can be translated and answered quantitatively using a statistical quantity, called the measure above and elsewhere in this disclosure, to be calculated from the data.
- both the transformation and computation systems assume the availability of one or more pre-defined, or user-selected, base statistical measure (or base measures) from which the measure needed to try answering the question can be built.
- the transformation system may select from among its available base measures to build and calculate the measure that will be used to answer the user's question.
- a statistical measure is any quantity that reflects some element of the data statistics, regardless of whether the data statistics are used or accounted for explicitly in the measure's definition or implementation.
- the user interface allows the user to specify the analysis question of interest. Consequently, it plays a major role in determining the flexibility of the method in the sense that it constrains which questions may be asked. By determining how questions must be posed, it also ultimately determines the user's perception of how easy it is to use the method.
- the present invention prefers interfaces that mimic the way the user naturally thinks about the question. An example interface is discussed later in this disclosure. In any case, the user may have his/her own ideas on the subject of the preferred interface for a given application.
- the user may also need to specify or select the data elements involved in the analysis (cf. Fig. 1 vs. Fig. 2).
- Figs. 1-3 dotted lines represent the user's input or selections, and dashed lines represent alternate possibilities. This data selection step may not be necessary, however, if a particular implementation or problem setting works on a fixed set of data, or if the data elements are direct products from another process.
- the user interface can be designed in a large number of ways. In a typical design mode, it may take the form of a graphical user interface (GUI) or a text command in a predefined grammar, for example.
- GUI graphical user interface
- User interfaces using speech recognition, or other human- computer interface modalities, can also be used.
- a GUI interface has the advantage of being much more intuitive, thus making the invention easier and more attractive to use.
- a GUI interface can easily become very complex as the number of data elements increases.
- a text command interface requires the user to learn the syntax of the commands, thus being less easy and intuitive to the user, but it is also much more powerful and allows for much more flexible data analysis scenarios.
- GUI can be implemented using readily available GUI toolkits, such as GTK (http://www.gtk.org/), Qt (http ://qt. noki a. com,'') , or wxWidgets (http://www.wxwidgets.org/).
- GTK http://www.gtk.org/
- Qt http ://qt. noki a. com,''
- wxWidgets http://www.wxwidgets.org/
- a text command interface can be implemented by directly coding a parser, if the structure of the commands is simple, or by using parser and lexical generator tools such as, for example, Yacc and Lex:
- GUI interface may generate a text command instead of interfacing directly with the transformation system, which may simplify the design of the GUI interface.
- a GUI interface can be used to graphically display a text command, which may be useful to verify that the command captures the intended analysis question.
- the transformation system is a key element of the present inventive method. It takes the output of the user interface and automatically builds a measure to be computed from the data. More specifically, the transformation system transforms an input "command" (provided by the user interface) representative of the analysis problem that the user is interested in, into a computational process whose output can be used in trying to answer the question.
- the transformation system 12 helps to automate the computational process for the user and allows for a multitude of general questions to be asked. From the user's perspective, it allows for simplicity of use and for the user to focus on the question of interest. From the computation system's perspective, the transformation system is the engine that allows for the generality of the invention's framework because it determines the approach to answering the user's questions "on-the-fly.” This is done by automatically formulating and implementing the statistical measure needed to try to answer the user's analysis problem.
- the transformation system In order for the transformation system to transform from the representation of the user interface to the representation of the computation system, it must be able to translate from one representation (that of the user interface) to the other (that of the computation system). Consequently, the specific form and implementation of the transformation system will depend on both the form of the output of the user interface and the base measure used by the computation system.
- the translation may occur without the need to actually understand the meaning of the command, i.e. the output of the user interface, as described above. In this case, the system relies instead on a dictionary or, more commonly, on a set of rules to map from one language to the other.
- the translation may require the system to actually understand the command, by inferring the meaning of what the user intends, and only then build the measure that evaluates that intent.
- the latter situation is likely to be necessary when the user interface system allows "ambiguous" commands, such as when natural language questioning is allowed. (In natural language questioning, the user may ask the system directly, "Is attribute A independent of attribute B?”, for example.)
- the computation system is the element responsible for evaluating the measure specified by the transformation system. It achieves this goal by applying its implementation of the base measure to the data elements, sequentially or in parallel, and combining those computations into a result according to a workflow that may be indicated by the transformation system.
- the measure may be calculated from the data elements in a global or local manner.
- evaluating the derived measure yields a single value as a result of the calculation.
- the data elements are sectioned into windows of data and the derived measure is calculated from each window. The windows may overlap and may cover only a region of the entire support of the data elements.
- the process results in another "attribute,” with dimensions determined by the number and location of windows, which answers the question with regards to a region (delineated by a window). This can be very important in detecting where some interesting confluences of attributes might be happening; for example, it might indicate that the attributes are highly interdependent in a portion of a seismic volume but not in other areas.
- the specific form of interaction between the transformation and computation systems depends on the specific design or implementation.
- the two systems may agree on a protocol to specify the computational workflow, and thus the computation system can implement and compute a new measure from the base measure (installed in the computation system beforehand) independently.
- the transformation system may also be responsible for combining the computation results of the base measure, in which case the computation system reduces to an implementation of the base measure.
- the transformation system needs to have three numbers added together. If the computation system is sophisticated enough to understand sequential summation, then the transformation system can just tell the computation system to add the first two numbers and then add the result to the third number. In an alternative embodiment, the transformation system needs to do the work of putting things together. So, the transformation system would have to tell the computation system to add the first two numbers together and get the result, and then the transformation system would give that result and the third number to the computation system and ask it to add them together, thus obtaining the result.
- base statistical measures may be implemented in the computation system.
- Typical examples of base statistical measures include, for example, variance and entropy (as the term entropy is used in information theory), or related measures, such as cross-covariance and mutual information. Note that these base measures have specific parameters or limitations and therefore, even though the user may be shielded from the computation details, the user might need to become knowledgeable about the computation details in order to interpret the analysis results.
- the system may be programmed to present these details in a monitor display on command.
- the computation system may also implement several base statistical measures simultaneously, with the choice of which base measure to use being done by the user or, preferably, automatically by the transformation system.
- One consideration is that the transformation system will need to know how to use each of the possible base measures and how to combine them or how choose the most appropriate base measure for each case. Additionally, if appropriate, the transformation system may also be able to simultaneously leverage multiple base measures. Although the latter scenario would necessarily make the transformation system more complex, it would also allow even greater flexibility and for a derived measure to potentially compensate for limitations of the base measures.
- the computation system may implement all the measures directly or through sub-systems.
- a data attribute is a term of art meaning any quantity that can be computed from the data, but also including the data themselves, i.e. the data amplitude and/or phase.
- GUI graphical user interface
- a Venn diagram can be used to characterize interdependencies between attributes. This GUI is particularly adequate when the interdependencies are characterized through entropy and mutual information; see Figs. 4A-4D for examples.
- Fig. 4A represents the interdependency between data sources A, B and C
- Fig. 4B represents the interdependency between data source A and data sources B or C
- Fig. 4C represents the information in A or B
- Fig. 4D represents the information contained exclusively in A.
- each attribute of interest is denoted through a "marker” in a "workspace,” and then the potential interdependencies about which one wants to analyze or query are expressed through lines or arrow connections between markers as illustrated in the example of Fig. 5.
- the graph shown may be used to answer a two-step analysis question: are B and C interdependent and, if so, does A corroborate the interdependence of B and C.
- Each approach has its advantages and disadvantages.
- a GUI based on a Venn diagram is very straightforward and intuitive, because all possible types of interdependencies are directly shown to the user, and the user needs to select only the ones he's interested in.
- a Venn diagram interface quickly becomes highly complex with more attributes, and Venn diagrams with more than 6 attributes are hard to draw and almost unusable.
- an interdependence diagram can cope with more than 6 attributes but the interdependencies to be analyzed must all be explicitly inputted by the user, instead of directly shown, making the interface less straightforward and intuitive.
- An alternative interface uses a text string to denote the expression characterizing the interdependency or combination of interdependencies that one is interested in analyzing.
- These three interdependency relationships provide the means to express any general interdependency.
- Information refers to the quantification of uncertainty within an attribute or of the interdependence between attributes and will take a specific meaning depending on the base measure used. For example, it may correspond to entropy or mutual information in an information theory sense, or to variance or correlation in a Gaussian statistics sense.
- entity is used herein to refer to a conceptual construct, which may for example be a data element, an interdependency of data elements, or combination of interdependencies of data elements.
- entity is used herein to refer to a conceptual construct, which may for example be a data element, an interdependency of data elements, or combination of interdependencies of data elements.
- a grammar of symbols denoting attributes and operations denoting their interdependencies.
- the above interdependencies can be expressed, respectively, by (1) the intersection of the entities (Figs. 4A and 4B), (2) the union of the entities (Fig. 4C), or (3) the entity excluding the remaining (Fig. 4D).
- These interdependencies can be denoted through symbols, for example '&', '
- the user interfaces may allow weights to be assigned to connections to, for example, obtain a desired scaling of the results or reflect the expected relative relevance.
- the interface may allow the user to specify a normalization factor, such as a scalar or other expression. This can be useful for analysis of the results.
- I(A,B,C) H(A) + H(B) + H(C) - H(A,B) - H(A,C) - H(B,C) + H(A,B,C).
- a marginal distribution is a distribution obtained from the joint distribution by "integrating out” one or more variables.
- ⁇ ( ⁇ > ⁇ > z ) denote the joint distribution of random variables X, Y, and Z.
- the joint distribution of Y and Z is a marginal distribution of ⁇ ( ⁇ > ⁇ > z )-
- the (marginal) distribution of Z or Y can be obtained from fyz(y> z ) or fxYzi x >y> z )- See also http://en.wikipedia.org/wiki/Marginal distribution.1
- ⁇ & B & C the mutual information of three attributes
- I(A,B,C) H(A) + H(B) + H(C) - H(A,B) - H(A,C) - H(B,C) + H(A,B,C).
- Figure 4B represents the interdependency between data source 'A' and data sources 'B' or 'C, and can be expressed as
- Figure 4C represents the information in 'A' or 'B' which maps directly to the union of 'A' and 'B', and thus to their joint entropy, H(A,B).
- Figure 4D represents the information contained exclusively in ⁇ ', that is,
- this example embodiment uses Shannon's entropy as its base measure for computation. Accordingly, information shared by variables, for example, is measured using mutual information. Mutual information, or any of the above-mentioned interdependences, are difficult to compute directly, but can be calculated using multiple entropy calculations and entropy is readily computed. There are a number of advantages to the use of entropy as a base measure, such as the fact that it is shift-invariant, because adding a constant does not change its result, and the fact that it can fully capture the statistics of a random variable if non-parametric estimation methods are used. Intuitively, entropy is a measure of the amount of information required to describe that random variable.
- H(X) - ⁇ xeX p(x)logp(x) , and the joint entropy, the generalization of entropy to multiple random variables or multidimensional random variables, is defined as,
- I(X l ,... ,X transport) I(X l ,... ,X n _ l ) - I(X l ,..., X n _ l ⁇ X tract) (4) where the conditional mutual information is defined as,
- An alternative method in the present disclosure for estimation of mutual information uses the cross-correlation of the normal score-transformed random variables. Unlike the direct approach mentioned earlier which involves estimating the pdf density, this method uses the cumulative distribution function ("cdf ') of the random variable. As a consequence, this method does not have free parameters and is easier and more stable to estimate.
- cdf ' cumulative distribution function of the random variable.
- this method does not have free parameters and is easier and more stable to estimate.
- a Venn diagram GUI is implemented by the file rv_rel_venn_gui . m. This interface was actually used to generate the images in Fig's 4A-4D for the test example below (with some additional image editing for clarity).
- a text command interface is also provided, as implemented by the file parse_rv_expr . m, which parses the command into an internal structure amenable for processing by the transformation system. For simplicity, in this example embodiment, the GUI generates a text command to be parsed by parse_rv_expr instead of the internal structure directly.
- the transformation system is implemented in the files simplify.m, apply_rv_rel_rules .m, and apply_union_distrib_rules .m.
- a user may invoke simplify with the structure obtained from parse_rv_expr, and apply_rv_rel_rules and apply_union_distrib_rules are invoked internally.
- These functions perform the actions described in the transformation system section description of the embodiment, resulting in a list where each element contains a scaling constant and the name of the attributes involved in joint entropy calculation for that term.
- the output_expr support function can be used to visualize the simplified expression.
- Figures 6A-6D illustrate an example of detection of differences in amplitude between two stacks of synthetic seismic data using the above-described example embodiment of the present inventive method.
- Figures 6A and 6B represent slices of two synthetically generated seismic amplitude stacks, representing the same x-z cross-section of a subsurface region. The two slices differ due to a small change in phase of the seismic data: close inspection shows that dark band 4 is slightly thicker in Fig. 6B than in Fig. 6A, but that difference is barely discernible visually, and might easily be overlooked in a visual inspection.
- the two seismic stacks could have been obtained using different data migration steps or they could correspond to seismic surveys collected at different times for a time-lapse (4D) seismic study.
- a possible data analysis question might be whether the data in Fig. 6B contains any information not contained in Fig. 6A. This can be important because if the two data elements contain the same information, then only one of the data elements needs to be considered for subsequent analysis (because the other data element does not bring anything new "to the picture"), thereby facilitating interpretation.
- that analysis question could highlight differences corresponding to changes in the subsurface, typically due to development or production, which can be very useful in characterizing the reservoir.
- Figure 4C shows the Venn diagram used for the user interface, expressing the data analysis question as being the information in 6B not contained in 6A, denoted ' ⁇ ' in the software program listed in the Appendix.
- this analysis question text command would be passed to the text interface implemented by parse_rv_expr for conversion into a structure that is passed to the transformation system.
- the computation system then performs a window-based estimation of the two entropy terms and takes their difference.
- the result from the computation system is shown in Fig. 6D, where red corresponds to a higher "amount of information" in B not contained in A and blue corresponds to lower information.
- Fig. 6D demonstrates, application of the present inventive method has clearly highlighted the presence of the differences in structure indicated at 4 in Figs. 6A and 6B.
- %RV REL VENN GUI provides a GUI to specify the relationship between up to 3
- n round (n) ;
- [xg yg] meshgrid( ...
- [xg yg] meshgrid( ...
- aux bsxfun ( @minus , [xg ( : ) yg ( : ) ] ' , centers ( : , i ) ) ;
- aux reshape ( sqrt ( sum (aux . A 2 , 1)) - radius, size(xg))
- img img
- (abs (aux) ⁇ 1/150);
- msgO 'Mouse click in an area to add/remove it, or press ''q'' to exit.';
- sel_area (end+1 , : ) sel
- aux ones (size (xg), 'single');
- aux aux & squeeze (a ( : , : , j ) ) ;
- aux aux & -squeeze (a ( : , : , j ) ) ;
- img img
- % PARSE_RV_EXPR parses the input string.
- the input string may contain letters (denoting the random variables) ,
- % t the parsed expression is represented by a list (i.e., structure array)
- % operation is mapped to a sign flip of .w.
- the token terms contain a
- nx length (x) ;
- idx [ 1 : nx] ;
- nx max ( idx) ;
- toklst repmat ( struct (' tok ' , [ ] , ' w ' , 1 ) , [maxtok 1 ] ) ;
- jj search_num_stop (x, jj);
- n jj + match_parentheses (x ( j j : end)) - 1;
- jj jj + search_num_stop (x ( j j : end), jj) - 1;
- token remove parentheses (x (ii : (jj-1)));
- n min (strfind (x (ii : (jj-1)), token));
- na str2double (part_a) ;
- nb str2double (part b) ;
- ii ii + min (strfind (token (1 : (mi-1)), part a)) - 1;
- token x(ii : ii+length (part a)-l);
- aux parse rv expr (x, (ii - 1) + [1 : length(token
- aux(n) .w aux(n) .w * toklst(ti) .w;
- token remove parentheses (x (idx) ) ;
- n 1;
- n n - 1 ;
- n n + 1;
- n 2;
- n n + 1;
- n match parentheses (x) ;
- aux remove parentheses (x (2 : end-1) )
- n i - 1;
- n length (x) ;
- %SIMPLIFY reduces (and sorts) an entropy computation expression .
- nr numel (t) ;
- r(i+2 : nr+1) r(i+l : nr);
- nr nr + 1 ;
- r r ( 1 : nr) ;
- u unique (distrib from AST (r (i) .tok) ) ;
- r(i).tok sort (u, 'ascend');
- n eelIfun ( ' length ' , ⁇ r . tok ⁇ ) ;
- str [distrib_from_AST (t . x) distrib_from_AST (t . y) ]
- % APPLY RV REL RULES applies rules to map intersection and exclusion of % random variables to sum, subtraction and union.
- % tO token list (e.g., from parse rv expr)
- % support function apply the rules to one
- a struct('x', t.x, 'y', t.y, 'op', '+');
- t.x struct ( ' x ' , t.x, ' y ' , t.y, ' op ' , ' I ' ) ;
- % tO token list (e.g., from parse rv expr)
- a struct ( ' x ' , t . x . x, y ,t.y, op ' , ) ;
- parsed expression string i.e. , the output of parse rv expr
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geophysics (AREA)
- Remote Sensing (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Acoustics & Sound (AREA)
- Geology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Algebra (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Environmental & Geological Engineering (AREA)
- Complex Calculations (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
Description
Claims
Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP13794684.4A EP2852853A4 (en) | 2012-05-23 | 2013-03-15 | Method for analysis of relevance and interdependencies in geoscience data |
AU2013266865A AU2013266865B2 (en) | 2012-05-23 | 2013-03-15 | Method for analysis of relevance and interdependencies in geoscience data |
US14/376,874 US9014982B2 (en) | 2012-05-23 | 2013-03-15 | Method for analysis of relevance and interdependencies in geoscience data |
CA2867170A CA2867170C (en) | 2012-05-23 | 2013-03-15 | Method for analysis of relevance and interdependencies in geoscience data |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201261650927P | 2012-05-23 | 2012-05-23 | |
US61/650,927 | 2012-05-23 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2013176771A1 true WO2013176771A1 (en) | 2013-11-28 |
Family
ID=49624231
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2013/032549 WO2013176771A1 (en) | 2012-05-23 | 2013-03-15 | Method for analysis of relevance and interdependencies in geoscience data |
Country Status (5)
Country | Link |
---|---|
US (1) | US9014982B2 (en) |
EP (1) | EP2852853A4 (en) |
AU (1) | AU2013266865B2 (en) |
CA (1) | CA2867170C (en) |
WO (1) | WO2013176771A1 (en) |
Families Citing this family (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9471723B2 (en) * | 2012-03-16 | 2016-10-18 | Saudi Arabian Oil Company | Input parsing and array manipulation in reservoir simulation |
US20130261981A1 (en) * | 2012-04-03 | 2013-10-03 | Westerngeco L.L.C. | Covariance estimation using sparse wavelet representation |
CA2910829C (en) * | 2013-05-31 | 2016-10-25 | Landmark Graphics Corporaton | Attribute importance determination |
WO2014200669A2 (en) | 2013-06-10 | 2014-12-18 | Exxonmobil Upstream Research Company | Determining well parameters for optimization of well performance |
US10359523B2 (en) | 2014-08-05 | 2019-07-23 | Exxonmobil Upstream Research Company | Exploration and extraction method and system for hydrocarbons |
CN106154317B (en) * | 2015-04-13 | 2018-05-08 | 中国石油化工股份有限公司 | A kind of method for amalgamation processing for improving time-lapse seismic Data coordinating |
US10268753B2 (en) * | 2015-12-22 | 2019-04-23 | Opera Solutions Usa, Llc | System and method for optimized query execution in computerized data modeling and analysis |
US10275502B2 (en) * | 2015-12-22 | 2019-04-30 | Opera Solutions Usa, Llc | System and method for interactive reporting in computerized data modeling and analysis |
US11175910B2 (en) | 2015-12-22 | 2021-11-16 | Opera Solutions Usa, Llc | System and method for code and data versioning in computerized data modeling and analysis |
WO2017112864A1 (en) | 2015-12-22 | 2017-06-29 | Opera Solutions U.S.A., Llc | System and method for rapid development and deployment of reusable analytic code for use in computerized data modeling and analysis |
US20220012853A1 (en) * | 2020-07-09 | 2022-01-13 | Technoimaging, Llc | Joint minimum entropy method for simultaneous processing and fusion of multi-physics data and images |
US20220036008A1 (en) * | 2020-07-31 | 2022-02-03 | GeoScienceWorld | Method and System for Generating Geological Lithostratigraphic Analogues using Theory-Guided Machine Learning from Unstructured Text |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030208322A1 (en) * | 2002-01-02 | 2003-11-06 | Aoki Kiyoko F. | Apparatus, method, and computer program product for plotting proteomic and genomic data |
US6829570B1 (en) * | 1999-11-18 | 2004-12-07 | Schlumberger Technology Corporation | Oilfield analysis systems and methods |
US7598487B2 (en) * | 2005-11-22 | 2009-10-06 | Exxonmobil Research And Engineering Company | Micro-hydrocarbon analysis |
US20100212909A1 (en) * | 2009-02-20 | 2010-08-26 | Anatoly Baumstein | Method For Analyzing Multiple Geophysical Data Sets |
US20110110192A1 (en) * | 2009-11-11 | 2011-05-12 | Chevron U.S.A. Inc. | System and method for analyzing and transforming geophysical and petrophysical data |
US20110297369A1 (en) * | 2008-11-14 | 2011-12-08 | Krishnan Kumaran | Windowed Statistical Analysis For Anomaly Detection In Geophysical Datasets |
Family Cites Families (108)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4916615A (en) | 1986-07-14 | 1990-04-10 | Conoco Inc. | Method for stratigraphic correlation and reflection character analysis of setsmic signals |
US5047991A (en) | 1989-04-28 | 1991-09-10 | Schlumberger Technology Corporation | Lithology identification using sonic data |
US4992995A (en) | 1989-10-24 | 1991-02-12 | Amoco Corporation | Methods for attenuating noise in seismic data |
US5274714A (en) | 1990-06-04 | 1993-12-28 | Neuristics, Inc. | Method and apparatus for determining and organizing feature vectors for neural network recognition |
US5265192A (en) | 1990-09-20 | 1993-11-23 | Atlantic Richfield Company | Method for the automated editing of seismic traces using an adaptive network |
JP3349196B2 (en) | 1992-06-20 | 2002-11-20 | テキサス インスツルメンツ インコーポレイテツド | Object identification system and method |
US5444619A (en) | 1993-09-27 | 1995-08-22 | Schlumberger Technology Corporation | System and method of predicting reservoir properties |
US5416750A (en) | 1994-03-25 | 1995-05-16 | Western Atlas International, Inc. | Bayesian sequential indicator simulation of lithology from seismic data |
GB2293010B (en) | 1994-07-07 | 1998-12-09 | Geco As | Method of processing seismic data |
US5586082A (en) | 1995-03-02 | 1996-12-17 | The Trustees Of Columbia University In The City Of New York | Method for identifying subsurface fluid migration and drainage pathways in and among oil and gas reservoirs using 3-D and 4-D seismic imaging |
US5539704A (en) | 1995-06-23 | 1996-07-23 | Western Atlas International, Inc. | Bayesian sequential Gaussian simulation of lithology with non-linear data |
FR2738920B1 (en) | 1995-09-19 | 1997-11-14 | Elf Aquitaine | METHOD FOR AUTOMATIC SEISMIC FACIAL RECOGNITION |
US5841735A (en) | 1996-07-09 | 1998-11-24 | The United States Of America As Represented By The Secretary Of The Navy | Method and system for processing acoustic signals |
US6052650A (en) | 1997-02-27 | 2000-04-18 | Schlumberger Technology Corporation | Enforcing consistency in geoscience models |
US6466923B1 (en) | 1997-05-12 | 2002-10-15 | Chroma Graphics, Inc. | Method and apparatus for biomathematical pattern recognition |
US6026399A (en) * | 1997-05-30 | 2000-02-15 | Silicon Graphics, Inc. | System and method for selection of important attributes |
GB9904101D0 (en) | 1998-06-09 | 1999-04-14 | Geco As | Subsurface structure identification method |
GB9819910D0 (en) | 1998-09-11 | 1998-11-04 | Norske Stats Oljeselskap | Method of seismic signal processing |
US6574565B1 (en) | 1998-09-15 | 2003-06-03 | Ronald R. Bush | System and method for enhanced hydrocarbon recovery |
US6236942B1 (en) | 1998-09-15 | 2001-05-22 | Scientific Prediction Incorporated | System and method for delineating spatially dependent objects, such as hydrocarbon accumulations from seismic data |
US6882997B1 (en) | 1999-08-25 | 2005-04-19 | The Research Foundation Of Suny At Buffalo | Wavelet-based clustering method for managing spatial data in very large databases |
DE19943325C2 (en) | 1999-09-10 | 2001-12-13 | Trappe Henning | Process for processing seismic measurement data with a neural network |
US6295504B1 (en) | 1999-10-25 | 2001-09-25 | Halliburton Energy Services, Inc. | Multi-resolution graph-based clustering |
US6226596B1 (en) | 1999-10-27 | 2001-05-01 | Marathon Oil Company | Method for analyzing and classifying three dimensional seismic information |
US6574566B2 (en) | 1999-12-27 | 2003-06-03 | Conocophillips Company | Automated feature identification in data displays |
FR2808336B1 (en) | 2000-04-26 | 2002-06-07 | Elf Exploration Prod | METHOD OF CHRONO-STRATIGRAPHIC INTERPRETATION OF A SEISMIC SECTION OR BLOCK |
US6363327B1 (en) | 2000-05-02 | 2002-03-26 | Chroma Graphics, Inc. | Method and apparatus for extracting selected feature information and classifying heterogeneous regions of N-dimensional spatial data |
US6618678B1 (en) | 2000-05-26 | 2003-09-09 | Jason Geosystems B.V. | Method of joint analysis and interpretation of the subsurface from multiple seismic derived layer property data sets |
US6625541B1 (en) | 2000-06-12 | 2003-09-23 | Schlumberger Technology Corporation | Methods for downhole waveform tracking and sonic labeling |
US6801197B2 (en) | 2000-09-08 | 2004-10-05 | Landmark Graphics Corporation | System and method for attaching drilling information to three-dimensional visualizations of earth models |
FR2813959B1 (en) | 2000-09-11 | 2002-12-13 | Inst Francais Du Petrole | METHOD FOR FACILITATING THE RECOGNITION OF OBJECTS, IN PARTICULAR GEOLOGICAL OBJECTS, BY A DISCRIMINATING ANALYSIS TECHNIQUE |
US6950786B1 (en) | 2000-10-10 | 2005-09-27 | Schlumberger Technology Corporation | Method and apparatus for generating a cross plot in attribute space from a plurality of attribute data sets and generating a class data set from the cross plot |
US7006085B1 (en) | 2000-10-30 | 2006-02-28 | Magic Earth, Inc. | System and method for analyzing and imaging three-dimensional volume data sets |
MXPA03005535A (en) | 2000-12-18 | 2003-10-15 | Schlumberger Holdings | Seismic signal processing method and apparatus for generating correlation spectral volumes to determine geologic features. |
US7203342B2 (en) | 2001-03-07 | 2007-04-10 | Schlumberger Technology Corporation | Image feature extraction |
US20020169735A1 (en) * | 2001-03-07 | 2002-11-14 | David Kil | Automatic mapping from data to preprocessing algorithms |
US6473696B1 (en) | 2001-03-13 | 2002-10-29 | Conoco Inc. | Method and process for prediction of subsurface fluid and rock pressures in the earth |
US6751558B2 (en) | 2001-03-13 | 2004-06-15 | Conoco Inc. | Method and process for prediction of subsurface fluid and rock pressures in the earth |
FR2824148B1 (en) | 2001-04-30 | 2003-09-12 | Inst Francais Du Petrole | METHOD FOR FACILITATING TRACKING OVER TIME OF THE DEVELOPMENT OF PHYSICAL STATES IN A SUBTERRANEAN FORMATION |
DE10142785C2 (en) | 2001-08-31 | 2003-07-03 | Henning Trappe | Method for determining local similarity from 3D seismic measurement data |
US6957146B1 (en) | 2001-12-24 | 2005-10-18 | Rdsp I, L.P. | System for utilizing seismic data to estimate subsurface lithology |
FR2841344B1 (en) | 2002-06-19 | 2005-04-29 | Tsurf | METHOD, DEVICE AND PROGRAM PRODUCT FOR SMOOTHING SUBSURFACE PROPERTY |
US7188092B2 (en) | 2002-07-12 | 2007-03-06 | Chroma Energy, Inc. | Pattern recognition template application applied to oil exploration and production |
US20060184488A1 (en) | 2002-07-12 | 2006-08-17 | Chroma Energy, Inc. | Method and system for trace aligned and trace non-aligned pattern statistical calculation in seismic analysis |
US20050288863A1 (en) | 2002-07-12 | 2005-12-29 | Chroma Energy, Inc. | Method and system for utilizing string-length ratio in seismic analysis |
US7308139B2 (en) | 2002-07-12 | 2007-12-11 | Chroma Energy, Inc. | Method, system, and apparatus for color representation of seismic data and associated measurements |
US7162463B1 (en) | 2002-07-12 | 2007-01-09 | Chroma Energy, Inc. | Pattern recognition template construction applied to oil exploration and production |
US7184991B1 (en) | 2002-07-12 | 2007-02-27 | Chroma Energy, Inc. | Pattern recognition applied to oil exploration and production |
US7295706B2 (en) | 2002-07-12 | 2007-11-13 | Chroma Group, Inc. | Pattern recognition applied to graphic imaging |
GB2394050B (en) | 2002-10-07 | 2005-11-23 | Westerngeco Seismic Holdings | Processing seismic data |
US7053131B2 (en) | 2002-12-03 | 2006-05-30 | Kimberly-Clark Worldwide, Inc. | Absorbent articles comprising supercritical fluid treated HIPE, I-HIPE foams and other foams |
US7206782B1 (en) | 2003-01-29 | 2007-04-17 | Michael John Padgett | Method for deriving a GrAZ seismic attribute file |
US6754380B1 (en) | 2003-02-14 | 2004-06-22 | The University Of Chicago | Method of training massive training artificial neural networks (MTANN) for the detection of abnormalities in medical images |
US7248539B2 (en) | 2003-04-10 | 2007-07-24 | Schlumberger Technology Corporation | Extrema classification |
US6804609B1 (en) | 2003-04-14 | 2004-10-12 | Conocophillips Company | Property prediction using residual stepwise regression |
US6970397B2 (en) | 2003-07-09 | 2005-11-29 | Gas Technology Institute | Determination of fluid properties of earth formations using stochastic inversion |
GB0318827D0 (en) | 2003-08-11 | 2003-09-10 | Bg Intellectual Pty Ltd | Dip value in seismic images |
US20080270033A1 (en) | 2003-08-19 | 2008-10-30 | Apex Spectral Technology, Inc. | Methods of hydrocarbon detection using spectral energy analysis |
US7243029B2 (en) | 2003-08-19 | 2007-07-10 | Apex Spectral Technology, Inc. | Systems and methods of hydrocarbon detection using wavelet energy absorption analysis |
US7092824B2 (en) | 2003-10-20 | 2006-08-15 | Ascend Geo Llp | Methods and systems for interactive investigation of geophysical data |
US7453766B1 (en) | 2003-11-25 | 2008-11-18 | Michael John Padgett | Method for deriving 3D output volumes using summation along flat spot dip vectors |
US7463552B1 (en) | 2003-11-25 | 2008-12-09 | Michael John Padgett | Method for deriving 3D output volumes using filters derived from flat spot direction vectors |
US7453767B1 (en) | 2003-11-25 | 2008-11-18 | Michael John Padgett | Method for deriving a 3D GRAZ seismic attribute file |
US7266041B1 (en) | 2005-06-21 | 2007-09-04 | Michael John Padgett | Multi-attribute background relative scanning of 3D geophysical datasets for locally anomaluous data points |
US7697373B1 (en) | 2003-11-25 | 2010-04-13 | Michael John Padgett | Method for deriving 3D output volumes using dip vector analysis |
US6941228B2 (en) | 2003-12-05 | 2005-09-06 | Schlumberger Technology Corporation | Method and system and program storage device for analyzing compressional 2D seismic data to identify zones of open natural fractures within rock formations |
US20050171700A1 (en) | 2004-01-30 | 2005-08-04 | Chroma Energy, Inc. | Device and system for calculating 3D seismic classification features and process for geoprospecting material seams |
US20060115145A1 (en) | 2004-11-30 | 2006-06-01 | Microsoft Corporation | Bayesian conditional random fields |
US8363959B2 (en) | 2005-03-21 | 2013-01-29 | Yeda Research & Development Co. Ltd. | Detecting irregularities |
FR2884636B1 (en) | 2005-04-15 | 2007-07-06 | Earth Resource Man Services Er | PR0CEDE OF HIERARCHICAL DETERMINATION OF COHERENT EVENTS IN AN IMAGE |
US20070067040A1 (en) | 2005-09-02 | 2007-03-22 | Anova Corporation | Methods and apparatus for reconstructing the anulus fibrosus |
CA2571094C (en) | 2005-12-13 | 2014-06-17 | Calgary Scientific Inc. | Local dominant wave-vector analysis of seismic data |
EP2395375A3 (en) | 2006-06-21 | 2012-04-11 | Terraspark Geosciences, LLC | Extraction of depositional systems |
BRPI0716098A2 (en) | 2006-08-31 | 2013-09-24 | Shell Int Research | Computer program method and product for interpreting a plurality of m-dimensional attribute vectors, method for producing hydrocarbons from subsurface formation |
DE102006050534B4 (en) | 2006-10-26 | 2013-12-12 | Airbus Operations Gmbh | Conduit system for an aircraft, in particular an aircraft |
FR2909185B1 (en) | 2006-11-27 | 2009-01-09 | Inst Francais Du Petrole | METHOD OF STRATIGRAPHIC INTERPRETATION OF SEISMIC IMAGES |
US9835743B2 (en) | 2006-11-28 | 2017-12-05 | Magnitude Spas | System and method for seismic pattern recognition |
GB2444506C (en) | 2006-12-06 | 2010-01-06 | Schlumberger Holdings | Processing of stratigraphic data |
US20100161235A1 (en) | 2007-03-09 | 2010-06-24 | Ikelle Luc T | Imaging of multishot seismic data |
US8346695B2 (en) | 2007-03-29 | 2013-01-01 | Schlumberger Technology Corporation | System and method for multiple volume segmentation |
US7658202B2 (en) | 2007-05-08 | 2010-02-09 | Kohler Co. | Low-profile valve assembly |
US8538702B2 (en) | 2007-07-16 | 2013-09-17 | Exxonmobil Upstream Research Company | Geologic features from curvelet based seismic attributes |
US7502691B2 (en) | 2007-07-31 | 2009-03-10 | Baker Hughes Incorporated | Method and computer program product for determining a degree of similarity between well log data |
US7869955B2 (en) | 2008-01-30 | 2011-01-11 | Chevron U.S.A. Inc. | Subsurface prediction method and system |
CA2729806A1 (en) | 2008-07-01 | 2010-01-07 | Schlumberger Canada Limited | Effective hydrocarbon reservoir exploration decision making |
GB2465079B (en) | 2008-08-06 | 2011-01-12 | Statoilhydro Asa | Geological modelling |
US10353111B2 (en) | 2008-08-21 | 2019-07-16 | Halliburton Energy Services, Inc. | Automated leg quality monitoring systems and methods |
WO2010076638A2 (en) | 2008-12-30 | 2010-07-08 | Schlumberger Technology Bv | Paleoneighborhood hydrocarbon spatial system |
CA2764705A1 (en) | 2009-06-09 | 2010-12-16 | Shell Internationale Research Maatschappij B.V. | Method for stratigraphic analysis of seismic data |
US8463551B2 (en) | 2009-11-17 | 2013-06-11 | Schlumberger Technology Corporation | Consistent dip estimation for seismic imaging |
US8930170B2 (en) | 2009-11-18 | 2015-01-06 | Conocophillips Company | Attribute importance measure for parametric multivariate modeling |
US8326542B2 (en) | 2009-11-19 | 2012-12-04 | International Business Machines Corporation | Method and system for retrieving seismic data from a seismic section in bitmap format |
US9607007B2 (en) | 2009-12-23 | 2017-03-28 | Schlumberger Technology Corporation | Processing of geological data |
US8358561B2 (en) | 2010-04-13 | 2013-01-22 | Spectraseis Ag | Bayesian DHI for seismic data |
US8380435B2 (en) | 2010-05-06 | 2013-02-19 | Exxonmobil Upstream Research Company | Windowed statistical analysis for anomaly detection in geophysical datasets |
US9134443B2 (en) | 2010-05-14 | 2015-09-15 | Schlumberger Technology Corporation | Segment identification and classification using horizon structure |
US8447525B2 (en) | 2010-07-29 | 2013-05-21 | Schlumberger Technology Corporation | Interactive structural restoration while interpreting seismic volumes for structure and stratigraphy |
US8515678B2 (en) | 2010-07-29 | 2013-08-20 | Schlumberger Technology Corporation | Chrono-stratigraphic and tectono-stratigraphic interpretation on seismic volumes |
US20120090001A1 (en) | 2010-10-07 | 2012-04-12 | Tzu-Chiang Yen | Apparatus having multimedia interface and network access management integrated therein |
GB201022128D0 (en) | 2010-12-31 | 2011-02-02 | Foster Findlay Ass Ltd | Active contour segmentation |
US9121968B2 (en) | 2011-01-31 | 2015-09-01 | Chevron U.S.A. Inc. | Extracting geologic information from multiple offset stacks and/or angle stacks |
US8838391B2 (en) | 2011-01-31 | 2014-09-16 | Chevron U.S.A. Inc. | Extracting geologic information from multiple offset stacks and/or angle stacks |
US8861309B2 (en) | 2011-01-31 | 2014-10-14 | Chevron U.S.A. Inc. | Exploitation of self-consistency and differences between volume images and interpreted spatial/volumetric context |
US20120197613A1 (en) | 2011-01-31 | 2012-08-02 | Chevron U.S.A. Inc. | Exploitation of self-consistency and differences between volume images and interpreted spatial/volumetric context |
US8972195B2 (en) | 2011-01-31 | 2015-03-03 | Chevron U.S.A. Inc. | Extracting geologic information from multiple offset stacks and/or angle stacks |
US20120322037A1 (en) | 2011-06-19 | 2012-12-20 | Adrienne Raglin | Anomaly Detection Educational Process |
US8954303B2 (en) | 2011-06-28 | 2015-02-10 | Chevron U.S.A. Inc. | System and method for generating a geostatistical model of a geological volume of interest that is constrained by a process-based model of the geological volume of interest |
US20130138350A1 (en) | 2011-11-24 | 2013-05-30 | Manoj Vallikkat Thachaparambil | Enhanced termination identification function based on dip field generated from surface data |
-
2013
- 2013-03-15 AU AU2013266865A patent/AU2013266865B2/en not_active Ceased
- 2013-03-15 EP EP13794684.4A patent/EP2852853A4/en not_active Withdrawn
- 2013-03-15 US US14/376,874 patent/US9014982B2/en active Active
- 2013-03-15 WO PCT/US2013/032549 patent/WO2013176771A1/en active Application Filing
- 2013-03-15 CA CA2867170A patent/CA2867170C/en not_active Expired - Fee Related
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6829570B1 (en) * | 1999-11-18 | 2004-12-07 | Schlumberger Technology Corporation | Oilfield analysis systems and methods |
US20030208322A1 (en) * | 2002-01-02 | 2003-11-06 | Aoki Kiyoko F. | Apparatus, method, and computer program product for plotting proteomic and genomic data |
US7598487B2 (en) * | 2005-11-22 | 2009-10-06 | Exxonmobil Research And Engineering Company | Micro-hydrocarbon analysis |
US20110297369A1 (en) * | 2008-11-14 | 2011-12-08 | Krishnan Kumaran | Windowed Statistical Analysis For Anomaly Detection In Geophysical Datasets |
US20100212909A1 (en) * | 2009-02-20 | 2010-08-26 | Anatoly Baumstein | Method For Analyzing Multiple Geophysical Data Sets |
US20110110192A1 (en) * | 2009-11-11 | 2011-05-12 | Chevron U.S.A. Inc. | System and method for analyzing and transforming geophysical and petrophysical data |
Non-Patent Citations (1)
Title |
---|
See also references of EP2852853A4 * |
Also Published As
Publication number | Publication date |
---|---|
CA2867170C (en) | 2017-02-14 |
AU2013266865A1 (en) | 2014-09-04 |
CA2867170A1 (en) | 2013-11-28 |
EP2852853A4 (en) | 2016-04-06 |
EP2852853A1 (en) | 2015-04-01 |
US9014982B2 (en) | 2015-04-21 |
US20150066369A1 (en) | 2015-03-05 |
AU2013266865B2 (en) | 2015-05-21 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CA2867170C (en) | Method for analysis of relevance and interdependencies in geoscience data | |
Nelli | Python data analytics with Pandas, NumPy, and Matplotlib | |
Bryant et al. | Thinking inside the box: A participatory, computer-assisted approach to scenario discovery | |
Kurgan et al. | A survey of knowledge discovery and data mining process models | |
RU2554895C2 (en) | Windowed statistical analysis for anomaly detection in geophysical datasets | |
Xie et al. | A semantic-based method for visualizing large image collections | |
Andrei | Continuous nonlinear optimization for engineering applications in GAMS technology | |
Figl et al. | What we know and what we do not know about DMN | |
Anderson et al. | ss3sim: an R package for fisheries stock assessment simulation with Stock Synthesis | |
Khalajzadeh et al. | Survey and analysis of current end-user data analytics tool support | |
Hadjimichael et al. | Rhodium: Python library for many-objective robust decision making and exploratory modeling | |
Mälicke | SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python | |
Xiao et al. | Surrogate-assisted inversion for large-scale history matching: Comparative study between projection-based reduced-order modeling and deep neural network | |
Lavalle et al. | A methodology to automatically translate user requirements into visualizations: Experimental validation | |
Neznanov et al. | Fcart: A new fca-based system for data analysis and knowledge discovery | |
Rabbani et al. | SHACTOR: improving the quality of large-scale knowledge graphs with validating shapes | |
An et al. | Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review | |
Sitterle et al. | 4.3. 3 Integrated Toolset and Workflow for Tradespace Analytics in Systems Engineering | |
Finley et al. | Automatic model calibration applying global optimization techniques | |
Danaei et al. | All-in-one proxy to replace 4D seismic forward modeling with machine learning algorithms | |
WO2022035971A1 (en) | Machine learning-based differencing tool for hydrocarbon well logs | |
Globa et al. | Examples of ontology model usage in engineering fields | |
Li et al. | SGEMS-UQ: An uncertainty quantification toolkit for SGEMS | |
Orta Alemán et al. | Improved Robustness In Long-term Pressure Data Analysis Using Wavelets and Deep Learning | |
Fiorini et al. | An approach for grounding ontologies in raw data using foundational ontology |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 13794684 Country of ref document: EP Kind code of ref document: A1 |
|
WWE | Wipo information: entry into national phase |
Ref document number: 14376874 Country of ref document: US |
|
ENP | Entry into the national phase |
Ref document number: 2013266865 Country of ref document: AU Date of ref document: 20130315 Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 2867170 Country of ref document: CA |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2013794684 Country of ref document: EP |
|
NENP | Non-entry into the national phase |
Ref country code: DE |