WO2017053080A1 - Subsurface volume evaluation - Google Patents

Abstract

An interpolated subsurface volume including control cells and derived cells is obtained, whereby the derived cells include attribute values derived from the control cells. A set of peak cells is selected according to attribute values. The attribute value from the derived cells or the control cells in the peak cell is a local extrema. A correlation coefficient is determined that characterizes a spatial relationship between the location of the peak cells and the location of the control cell. The correlation coefficient is presented.

Description

SUBSURFACE VOLUME EVALUATION

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional

Patent Application Serial Number 62/222,820, filed on September 24, 2015, and entitled "RESEVOIR MODEL VERIFICATION SYSTEM," which is incorporated herein by reference in its entirety.

BACKGROUND

[0002] Obtaining valuable underground resources involves understanding the various rocks and properties of the subsurface. However, because such subsurface is not viewable at the surface, sensor data is acquired for particular control points. Values of various subsurface properties for the control points are determined from the sensor data. Because sensor data cannot be obtained for each point in the subsurface, interpolation is performed using the control points to create a subsurface model.

SUMMARY

[0003] In general, embodiments are directed to evaluating the subsurface volumes generated by data interpolation procedures. An interpolated subsurface volume is obtained using control cells and derived cells. The derived cells include attribute values derived from the control cells. A set of peak cells is selected, according to the attribute values. A correlation coefficient is determined that characterizes a spatial relationship between the location of the peak cells and the location of the control cell. The correlation coefficient may be used to evaluate the interpolated subsurface volume.

[0004] Other aspects of one or more embodiments will be apparent from the following description and the appended claims.

l BRIEF DESCRIPTION OF DRAWINGS

[0005] FIG. 1 is a schematic view, partially in cross-section, of a field in which one or more embodiments of bulls eye identification analysis may be implemented.

[0006] FIG. 2 shows a schematic diagram of a system in accordance with one or more embodiments of the technology.

[0007] FIGs. 3, 4.1, 4.2, 4.3, and 4.4 show flowcharts in accordance with one or more embodiments of the technology.

[0008] FIGs. 5, 6.1, 6.2, 6.3, 6.4, 7.1, 7.2 and 8 show examples in accordance with one or more embodiments.

[0009] FIGs. 9.1 and 9.2 show E&P a computing system in accordance with one or more embodiments.

[0010]

DETAILED DESCRIPTION

[0011] Specific embodiments will now be described in detail with reference to the accompanying figures. Like elements in the various figures are denoted by like reference numerals for consistency.

[0012] In the following detailed description of embodiments, numerous specific details are set forth in order to provide a more thorough understanding of the technology. However, it will be apparent to one of ordinary skill in the art that the method may be practiced without these specific details. In other instances, well- known features have not been described in detail to avoid unnecessarily complicating the description.

[0013] Throughout the application, ordinal numbers (e.g., first, second, third, etc.) may be used as an adjective for an element (i.e., any noun in the application). The use of ordinal numbers is not to imply or create any particular ordering of the elements nor to limit any element to being a single element unless expressly disclosed, such as by the use of the terms "before", "after", "single", and other such terminology. Rather, the use of ordinal numbers is to distinguish between the elements. By way of an example, a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements.

[0014] In general, embodiments are directed to a method and system for analysis of the location and likelihood of extreme values clustering in interpolated data. One or more embodiments include functionality to display a map of the interpolated data along with the data resulted from field measurements. One or more embodiments include functionality to quantify the magnitude of the extreme values clustering effect on the interpolated data.

[0015] FIG. 1 depicts a schematic view, partially in cross section, of a field (100) in which one or more embodiments may be implemented. In one or more embodiments, one or more of the modules and elements shown in FIG. 1 may be omitted, repeated, and/or substituted. Accordingly, embodiments should not be considered limited to the specific arrangements of modules shown in FIG. 1.

[0016] A geologic sedimentary basin contains a subterranean formation (104). As shown in FIG. 1, the subterranean formation (104) may include several geological structures (106-1 through 106-4). As shown, the subterranean formation (104) may include a shale layer (106-1), a limestone layer (106-2), a sandstone layer (106-3), and another shale layer (106-4). A fault plane (107) may extend through the subterranean formation (104). In one or more embodiments, various survey tools and/or data acquisition tools are adapted to measure the subterranean formation (104) and detect the characteristics of the geological structures of the subterranean formation (104). Generally, survey operations and wellbore operations are referred to as field operations of the field (100). These field operations may be performed as directed by the surface unit (112).

[0017] In one or more embodiments, the surface unit (112) is communicatively coupled to the E&P computer system (118). In one or more embodiments, the data received by the surface unit (112) may be sent to the E&P computer system (118) for further analysis. Generally, the E&P computer system (118) is configured to analyze, model, control, optimize, or perform management tasks of the aforementioned field operations based on the data provided from the surface unit (112). In one or more embodiments, the E&P computer system (118) is provided with functionality for manipulating and analyzing the data, such as performing simulation, planning, and optimization of production operations of the wellsite system A (114-1), wellsite system B (114-2), and/or wellsite system C (114-3). In one or more embodiments, the result generated by the E&P computer system (118) may be displayed for an analyst user to view the result in a two-dimensional (2D) display, three-dimensional (3D) display, or other suitable displays. Although the surface unit (112) is shown as separate from the E&P computer system (118) in FIG. 1, in other examples, the surface unit (112) and the E&P computer system (118) may also be combined.

[0018] Although FIG. 1 shows a field (100) on the land, the field (100) may be an offshore field. In such a scenario, the subterranean formation (104) may be in the sea floor. Further, field data may be gathered from the field (100) that is an offshore field using a variety of offshore techniques for gathering field data.

[0019] In FIG. 1, the trajectory (i.e., path) of the wellbore is vertical. Although

FIG. 1 shows a wellbore as being vertical, the wellbore may be horizontal or may follow a winding path to the reservoir. During drilling and other operations in the wellbore, oilfield equipment may gather and store data in one or more well logs. [0020] In one or more embodiments, the data received by the surface unit (112) represents characteristics of the subterranean formation (104) and may include seismic data and/or information related to location of the horizon and fault surfaces or characteristics of the formation rocks like porosity, saturation, permeability, natural fractures, stress magnitude and orientations, elastic properties, etc., during a drilling, fracturing, logging, or production operation of the wellbore (103) at the wellsite system. In one or more embodiments, the data received by the surface unit (112) may be combined and included in a subsurface volume of data.

[0021] FIG. 2 shows a diagram of a system in which one or more embodiments of the technology may be implemented. In one or more embodiments, one or more of the modules and elements shown in FIG. 2 may be omitted, repeated, and/or substituted. Accordingly, embodiments should not be considered limited to the specific arrangements of modules shown in FIG. 2.

[0022] As shown in FIG. 2, the E&P computer system (1 18) includes a data repository (210) for storing input data, intermediate data, and resultant outputs of the analysis data, an analysis tool (240), and a field equipment module (250) for performing various tasks of the field operation. In one or more embodiments, the data repository (210) may include one or more disk drive storage devices, one or more semiconductor storage devices, other suitable computer data storage devices, or combinations thereof. In one or more embodiments, content stored in the data repository (210) may be stored as a data file, a linked list, a data sequence, a database, a graphical representation, any other suitable data structure, or combinations thereof.

[0023] The data repository (210) includes functionality to store subsurface volume data (220) and one or more property maps (230). In one or more embodiments, the data repository (210) is any type of storage unit and/or device (e.g., a file system, database, collection of tables, or any other storage mechanism) for storing data. Further, the data repository (210) may include multiple different storage units and/or devices. The multiple different storage units and/or devices may or may not be of the same type or located at the same physical site.

[0024] The subsurface volume (220) contains data related to one or more attribute of the subterranean formation (104) presented in FIG. l. In one or more embodiments, the subsurface volume (220) includes volume cells, each volume cell being characterized by a single value of an attribute over the entire cell. In one or more embodiments, the volume cells may be created as a result of a gridding operation on the subsurface volume, each grid cell being a volume cell. In one or more embodiments, the volume cells may be control cells (221), interpolated cells (223), or peak cells (225).

[0025] A control cell (221) is a subsurface volume cell that has one or more attribute values extracted from a field measurement operation. In other words, the field measurement operation directly obtains a measurement for an attribute of the location that is represented by the control cell. Thus, the location of the measurement and the location represented by the control cell are the same. The measurement for the attribute may be the attribute value or may be used to determine the attribute value. In one or more embodiments, the control cells are regarded as control cells in relation to the attribute. In other words, a control cell may be a control cell for a first attribute and not a control cell for a second attribute. Thus, a set of volume cells may be referred to as control cells regarding an attribute and not control cells regarding a different attribute. In one or more embodiments, an attribute for a volume cell may be an environment parameter

(e.g., temperature, pressure, heat flow, geothermal gradient), a rock parameter

(e.g., thermal conductivity, porosity, permeability, lithology, age of deposition, etc.), a pore fluid property (e.g., fluid density, fluid viscosity, etc.), or a surface or laver eeometrv Te e., surface depth, layer thickness, etc.), associated with the subsurface volume. Other attributes may be used without departing from the scope of one or more embodiments.

[0026] An interpolated cell (223) is a subsurface volume cell that has one or more attribute values derived from the attribute values of the control cell. In one or more embodiments, the attribute values of the interpolated cells are obtained from an interpolation operation of the control cells. Thus, whereas measurements for the attribute are obtained for the locations of the control cells, the attribute values of interpolated cells are derived from the control cells. In one or more embodiments, the measured data points are associated with a value of an attribute of the control cells (221). In one or more embodiments, the interpolated data points are associated with a value of an attribute of the interpolated cells (223).

[0027] A peak cell (225) is a subsurface volume cell that has the value of an attribute that represents a local extrema. In one or more embodiments, a local extrema is defined as a value of the attribute that is either the biggest or the smallest among the attribute values of volume cells situated within a threshold distance in each direction. In one or more embodiments, the threshold distance is defined in terms of number of volume cells in each direction of the peak cell.

[0028] The property maps (230) contain spatially defined attribute values that characterizes the subsurface volume. In one or more embodiments, the property map may be a density distribution map (231) or a correlation coefficient map (233).

[0029] The density distribution map (231) is a map that displays the level of clustering of the peak cells across the subsurface volume. In other words, the density distribution map shows areas of convergence of the peak cells. In one or more embodiments, the density distribution map is obtained by projecting vertically the peak cells on a surface parallel to the horizon and interpolating the projected points to obtain a surface property map. The projected display representation of the peak cells is therefore characterizing the entire vertical space through the subsurface volume.

[0030] The correlation coefficient map (233) is a surface property map that displays the spatial distribution of the likelihood of extreme values clustering affected interpolation results. In one or more embodiments, the correlation coefficient of a point within the subsurface volume is defined as the attribute that quantify the degree of spatial separation of the peak cells from the control cells at the location of the point. In one or more embodiments, the correlation coefficient map is obtained by projecting vertically the peak cells on a surface parallel to the horizon and interpolating the correlation coefficient values of the projected points to obtain a surface property map.

[0031] The correlation coefficient volume (235) is a subsurface property volume that displays the spatial distribution of the likelihood of extreme values clustering affected interpolation results. In one or more embodiments, the correlation coefficient volume is obtained by interpolating the correlation coefficient values of the peak cells within the subsurface volume to obtain a subsurface property volume.

[0032] Continuing with FIG. 2, the E&P computer system (118) additionally includes an analysis tool (240) in accordance with one or more embodiments. The analysis tool (240) includes a user interface (241), a cell interpolator (243), a volume calculator (245), a surface calculator (247), and a visualization creator (249). Each of these components is described below.

[0033] In one or more embodiments, the user interface (241) corresponds to a graphical user interface (GUI) that includes functionality to receive input from a user and present or display graphical data to the user. The user interface (241) may include a 3D volume data viewer, a 2D section of subsurface volume viewer, and parameters value input fields in accordance with one or more embodiments.

[0034] The cell interpolator (243) is software component that is configured to interpolate the attribute to obtain attribute values for interpolated cells from the attribute values of the control cells. In other words, the cell interpolator (243) includes functionality to generate a set of calculated data points at locations within the subsurface volume where no data measurements exist. In one or more embodiments, the result of the interpolation process is a continuous property map or a continuous property volume.

[0035] The volume calculator (245) is a software component that is configured to execute calculations on volume cell attributes and execute spatial analysis within the subsurface volume. In one or more embodiments, the volume calculator (245) may perform calculations on multiple attribute data that cover the same area to study the relationships between different attributes.

[0036] The surface calculator (247) is a software component that is configured to execute calculations on surface properties maps and execute spatial analysis on the surfaces associated with the subsurface volume.

[0037] The visualization creator (249) is a software component that is configured to render 3D perspective visualizations of the subsurface volume components. In one or more embodiments, the visualization creator (249) is operatively connected to the user interface (241) and the data repository (210). The visualization creator (249) includes functionality to generate display representations of subsurface geologic layers for presentation using coded maps (e.g., greyscale coded, color coded, pattern coded, etc.) linked to volume cells attributes. In one or more embodiments, the visualization creator (249) includes functionality to generate 3D surfaces for display using a color map linked to a surface attribute. Specifically, the visualization creator (249) may include functionality to perform final rendering and show the components of the subsurface volume. The subsurface geometry may include, for example, natural faults, subsurface layers of different density, density distribution maps, and other such information. In one or more embodiments, the visualization creator (249) includes functionality to display subsurface field measurements or any other data points on top of the layers. In one or more embodiments, the visualization creator (249) includes functionality to highlight the control cells at the control cell's location within subsurface volume. In one or more embodiments, the visualization creator (249) displays a rendered image of the subsurface volume in the user interface (241). In such embodiments, a portion of the volume cells are shown on the display. For example, the portion may include a left portion, a rear portion, and a slice through subsurface volume with the volume cells of the front right section removed from display. In one or more embodiments, the rendering is defined by an observation point angle. The observation point defines the perspective from which the volume cells are viewed. For example, the observation point is a position of a virtual camera or theoretical user from which the volume cells are viewed. In particular, as an example, a top portion may be shown where the view is from above the surface of the earth looking downward at the subsurface volume. In one or more embodiments, the E&P computer system (118) includes the field equipment module (250) that is configured to generate a field operation control signal based at least on a result generated by the E&P computer system (118), such as based on the likelihood of unre liable subsurface information due to a bull-eyes effect in some regions of the field (100) depicted in FIG. 1 above. As noted above, the field operation equipment depicted in FIG. 1 above may be controlled by the field operation control signal. For example, the field operation control signal may be used to control drilling equipment, an actuator, a fluid valve, or other electrical and/or mechanical devices disposed about the field (100). [0039] While FIGs. 1 and 2 show a configuration of components, other configurations may be used without departing from the scope. For example, various components may be combined to create a single component. As another example, the functionality performed by a single component may be performed by two or more components.

[0040] FIGs. 3, 4.1, 4.2, 4.3, and 4.4 show flowcharts in accordance with one or more embodiments. While the various blocks in these flowcharts are presented and described sequentially, one of ordinary skill will appreciate that at least some of the blocks may be executed in different orders, or may be combined or omitted, and at least some of the blocks may be executed in parallel. Furthermore, the actions in the blocks may be performed actively or passively. For example, some actions may be performed using polling or be interrupt driven in accordance with one or more embodiments. By way of an example, determination blocks may not use a processor to process an instruction unless an interrupt is received to signify that condition exists in accordance with one or more embodiments. As another example, determination blocks may be performed by performing a test, such as checking a data value to test whether the value is consistent with the tested condition in accordance with one or more embodiments.

[0041] FIG. 3 shows a flowchart for performing analysis of the location and likelihood of extreme values clustering in interpolated data in accordance with one or more embodiments. In Block 301, an interpolated subsurface volume including volume cells is obtained. For example, the interpolated subsurface volume may be obtained from the data repository in accordance with one or more embodiments. In one or more embodiments, the volume cells include interpolated cells or control cells. In one or more embodiments, the obtained volume is spatially defined based on a set of location boundaries specified by the user or in a file. For example, the user may draw the boundaries in the user interface. As another example, the boundaries may be selected to match the boundaries of a seismic data cube.

[0042] In Block 303, a set of peak cells is selected from the volume cells in accordance with one or more embodiments. In one or more embodiments, the volume cells with an attribute value equal to a local extrema are selected as peak cells. In one or more embodiments, the selection is performed by applying a local moving data volume through the subsurface volume, wherein the moving volume is selected as to accommodate one or more volume cells at any time. The moving data volume returns a flag when the centroid of the volume is either the maxima or the minima within the local volume. For grids representing geological property data, where the variations of attribute values are typically laterally related rather than vertical, the moving volume can be vertically thin (e.g. 1 volume cell thick within the layer of interest). In one or more embodiments, the lateral size of the volume is dependent on the scale of maxima or minima chosen but may be as small as a 3*3 volume cell window. The identification of maxima and minima is performed through the volume of interest and results in a property that is the maxima and minima flags that identify the local highs and lows in the data. Further, once a local maxima or a local minima is detected, the volume cell that contains the centroid of the flagged moving volume is selected as a peak cell. In one or more embodiments, the presence and location of the peak cells may indicate where an extreme values clustering condition could be occurring.

[0043] In Block 305, a correlation coefficient that characterizes a spatial relationship between the location of the peak cells and the location of the control cells is determined in accordance with one or more embodiments. In one or more embodiments, the correlation coefficient is a measurement of the degree of clustering of the peak cells around the location of the control cells. In one or more embodiments, the degree of clustering is calculated based on the distance in space between the r>eak cell locations and the nearest control cell location. In one or more embodiments, an extreme values clustering condition present as a result of interpolation is characterized by a very short distance (i.e., less than a threshold distance) between peak cells and the control cells in comparison to the average distance of entire volume cells to the nearest control cell. In one or more embodiments, a condition where no or minimum extreme values clustering is present is characterized by an even spread in distances between peak cells and the control cells in comparison to the average distance of entire volume cells to the nearest control cell. In one or more embodiments, the correlation coefficient at each peak cell location can be interpolated to generate a subsurface property volume or a surface property map for further spatial analysis.

[0044] In Block 307, the correlation coefficient is presented for visualization. In one or more embodiments, the correlation coefficient subsurface property volume or the correlation coefficient surface property map is sent to the visualization creator to generate 3D visualizations of correlation coefficient property for display using a color map linked to the correlation coefficient values. In one or more embodiments, the correlation coefficient may be scaled to a range of extreme values clustering occurrence probability risk intervals and presented as a risk intervals volume or risk intervals map. For example, a user can analyze the risk interval volume and identify zones with unreliable data.

[0045] Although not shown in FIG. 3, the displayed correlation coefficient may be used to perform oilfield operations. For example, based on the position of zones with umeliable data along with other field simulations and modeling, oilfield equipment may be physically adjusted to change the monitoring of the wellbore, inject fluids, and/or extract hydrocarbons from the wellbore.

[0046] FIG. 4.1 shows a flowchart for interpolating a set of control cells in accordance with one or more embodiments. In Block 411, the control cells information is read by the cell interpolator. In one or more embodiments, the information about control cells includes location information and the value of the attribute being interpolated.

[0047] In Block 412, the interpolation function is selected in one or more embodiments. In one or more embodiments, the interpolation function is defined as the algorithm that predicts values for cells in a raster from a limited number of sample data points. In one or more embodiments, the interpolation function is used to predict values for any volume cell attribute at any location between the control cells. For example, the interpolation function may be used to predict values for subsurface depth, geothermal gradient, porosity, or seismic velocity.

[0048] In Block 413, values for an attribute at interpolated cells location are obtained based on the interpolation of the control cells by the interpolation function. In one or more embodiments, the interpolation operation results in a spatial correlation of the interpolated cells. In other words, cells that are in close proximity tend to have similar attribute value using the interpolation operation.

[0049] In Block 414, the control cells and interpolated cells attribute values are combined to create an interpolated subsurface volume. In one or more embodiments, the subsurface volume contains entire control cells and entire interpolated cells. In one or more embodiments, the subsurface volume contains volume cells, wherein a volume cell is either a control cell or an interpolated cell. The process then proceeds to end.

[0050] FIG. 4.2 shows a flowchart for selecting the peak cells in accordance with one or more embodiments. In Block 421, a volume cell is selected from the interpolated subsurface volume, and the volume's cell attribute value is read.

[0051] In Block 422, attribute values of cells within a predefined range of the selected volume cell are read, and a set of extrema values is calculated for the predefined range. In one or more embodiments, the predefined range may be defined in terms of the number of volume cells in the proximity of the selected volume cell. In one or more embodiments, the predefined range may be extended by different lengths in different directions. For example, the predefined range can be defined like 5 cells in lateral direction and 3 cells in vertical direction. In one or more embodiments, the size of the predefined range is a constant through the selection process and is considered therefore a moving volume as discussed in FIG 3. In one or more embodiments, the predefined range is centered on the selected cell location. In other words, the centroid of the moving volume falls within the selected volume cell. In one or more embodiments, the set of extrema values is selected from each of the minimum value of entire attribute values of cells within the selected range, and the maximum value of entire attribute values of cells within the selected range.

[0052] In Block 423, the selected volume cell attribute value is tested against each of extrema values of the set of extrema values within the predefined range. In one or more embodiments, a determination is made as to whether the selected volume cell attribute value is lower than or equal to the minimum value or the selected volume cell attribute value is higher than or equal to the maximum value. If the selected volume cell attribute value is either less than or equal to the minimum value, or greater than or equal to the maximum value, then the selected volume cell attribute value is defined as a local extrema.

[0053] In Block 424, if the selected volume cell attribute value is identified as a local extrema, then the selected volume cell is assigned to the peak cells set. If the selected volume cell attribute value is not identified as a local extrema, then the process proceeds to Block 425.

[0054] In Block 425, the subsurface volume is scanned, and a determination is made as to whether there is any cell whose attribute value was not read. If a volume cell is found that was not previously selected, the process proceeds to Block 421. If the cells have been read, then the process ends. [0055] FIG. 4.3 shows a flowchart for determining a correlation coefficient in accordance with one or more embodiments. In Block 431, an interpolated cell location is read from the subsurface volume. In one or more embodiments, the cell location is defined by a set of X, Y, Z coordinates.

[0056] In Block 432, the distance between the location of the interpolated cell and the location of the nearest control cell is calculated.

[0057] In Block 433, the interpolated cell is compared to the list of cells in the set of peak cells, and a determination is made as to whether the interpolated cell status is identified as being part of the peak cell set.

[0058] In Block 434, if the interpolated cell is identified as a peak cell, the calculated distance to the peak cell is added to a peak cell total distance variable. In one or more embodiments, the peak cell total distance variable cumulates the distance of each peak cells to the peak cells nearest control cell.

[0059] In Block 435, if the interpolated cell is not identified as a peak cell, the calculated distance is added to an interpolated cell total distance variable. In one or more embodiments, the interpolated cell total distance variable cumulates the distance of each interpolated cells to the interpolated cells nearest control cell. In other words, the interpolated cell total distance summarize entire interpolated cells distances to the interpolated cell's nearest control cell including the peak cells' distances.

[0060] In Block 436, the subsurface volume is scanned, and a determination is made as to whether there is any cell whose location coordinate was not read. If an interpolated cell is found that was not previously selected, the process proceeds to Block 431.

[0061] In Block 437, if it is determined that the cells' location coordinates have been read, then an average peak cell distance is calculated based on peak cell total distance. In one or more embodiments, the average peak cell distance is obtained by dividing the peak cell total distance y the total number of peak cells.

[0062] In Block 438, an average interpolated cell distance is calculated based on interpolated cell total distance. In one or more embodiments, the average interpolated cell distance is obtained by dividing the interpolated cell total distance by the total number of interpolated cells.

[0063] In Block 439, a correlation coefficient is determined based on the ratio between the average peak cell distance and the average interpolated cell distance. In one or more embodiments, the correlation coefficient may be expressed as a percentage ratio. In one or more embodiments, the correlation coefficient is characterizing the degree of interpolated value clustering around the location of cells with attribute values used to generate the interpolated values. In one or more embodiments, a low value for a correlation coefficient is characterized by a high degree of interpolated value clustering. In one or more embodiments, a correlation coefficient at each interpolated cell location can be calculated and a subsurface property volume derived, wherein the property is the correlation coefficient. Further, the volume can be rendered by the visualization creator and displayed in the user interface for further spatial analysis. In one or more embodiments, results of the interpolation operation can be assessed in terms of the extreme values clustering probability of occurrence based on an established threshold for the correlation coefficient. For example, a correlation coefficient value of 5% or lower may suggest an unacceptable level of extreme values clustering probability. In one or more embodiments, based on the level of extreme values clustering probability, a field operation may be initiated. The process then proceeds to end.

[0064] FIG. 4.4 shows a flowchart for determining a surface correlation coefficient in accordance with one or more embodiments. In Block 441, the location coordinates of the peak cells within the subsurface volume are read by the volume calculator.

[0065] In Block 443, the subsurface volume is divided into a set of vertical columns based on a predefined reference surface lattice. In one or more embodiments, the lattice is defined as a repetitive arrangement of polygons that cover a reference surface. In one or more embodiments, the polygons may be either regular or irregular. In one or more embodiments, the reference surface may be represented by the top of the subsurface volume, top of a geologic layer, or may be a horizontal plane overlapping the subsurface volume.

[0066] In Block 445, the number of peak cells within each column's volume is counted for each column. In other words, each column has a corresponding value specifying the number of peak cells in the column.

[0067] In Block 447, a density distribution map is computed for the entire subsurface volume, based on the number of peak cells within each column's volume. In one or more embodiments, the density map is derived by plotting the results of the peak cell count for each volume in the center of the corresponding draped polygon. Further, by interpolating the center points across the reference surface, a property raster map is created, wherein the property is the density of the peak cells. In one or more embodiments, the entire draped polygon may be assigned the value of the peak count, producing a polygon vector map.

[0068] In Block 449, a correlation coefficient that characterizes a spatial relationship between the control cells and the density distribution map is determined in the surface calculator. In one or more embodiments, the correlation coefficient may be defined as the percentage of the number of the peak cells to the total number of cells within the subsurface volume. In one or more embodiments, a correlation coefficient map may be calculated based on the density distribution map. In one or more embodiments, the correlation coefficient map is derived by dividing the density distribution map by the horizontal distance from the center point to the nearest control cell, for each draped polygon. Further, the surface property map can be rendered by the visualization creator and displayed in the user interface for further spatial analysis.

[0069] While the various stages in these flowcharts are presented and described sequentially, one of ordinary skill will appreciate that some or at least some of the blocks may be executed in different orders, may be combined or omitted, and at least some of the blocks may be executed in parallel. Furthermore, the blocks may be performed actively or passively. For example, some blocks may be performed using polling or be interrupt driven in accordance with one or more embodiments. By way of an example, determination blocks may not use a processor to process an instruction unless an interrupt is received to signify that condition exists in accordance with one or more embodiments. As another example, determination blocks may be performed by performing a test, such as checking a data value to test whether the value is consistent with the tested condition in accordance with one or more embodiments.

[0070] The following examples are for explanatory purposes only and not intended to limit the scope of one or more embodiments. FIG. 5 shows a visualization of the spatial distribution of an interpolated property derived from the input well control data. In the example, the interpolation resulted in a low extreme values clustering density. The labeled pipes (501) depicted in FIG. 5 represents the locations of the input data associated with the control cells. The input data is obtained from the wells (101) of an oilfield (100).

[0071] FIGs. 6.1 - 6.4 show examples of interpolation results. FIG. 6.1 shows a sample map of an attribute values distribution. Each square is a representation of a volume cell and the grey shades represent the value of the attribute. In one or more embodiments, the attribute value is mapped to a grey scale. The interpolated cell values are based on the control cells values. The labeled pipes (610) depicted in FIG. 6.1, FIG. 6.2, FIG. 6.3, FIG. 6.4, represents the locations of the control cells. In one or more embodiments, the control cell values represent measurements from wells (101) of an oilfield (100). FIG. 6.2 shows a sample map over the same area with the result of local minima detection. The dark squares represent the peak cells identified based on a local minima criteria for the value of the attribute. FIG. 6.3 shows a sample map over the same area with the result of local maxima detection. The dark squares represent here the peak cells identified based on a local maxima criteria for the value of the attribute. FIG. 6.4 shows a sample map over the same area with the combined result of local minima and maxima detection. The dark squares represent here the peak cells identified based either on a local minima or a local maxima criteria for the value of the attribute. FIGs. 7.1 and 7.2 show examples of density distribution maps. FIG. 7.1 shows an example of a non-clustered extreme values data set resulted from the interpolation of the control cells (710) shown as labeled pipes in FIG. 7.1. As shown in FIG. 7.1 the light areas that represents high clustering (density distribution) level exhibit a relatively random distribution relative to the control cells locations. In one or more embodiments, the control cells have attribute values derived well measurements. For example, based on well logging data, a set of porosity measurements are derived along a set of vertical wellbore trajectories. The control cells (710) are defined as the volume cells crossed by the wellbore trajectories. Based on the porosity values at the control cells location a porosity volume is interpolated for a reservoir layer similar with the sandstone layer (106- 3) of the oilfield (100). The volume porosity is used to determine the volume of hydrocarbons that may be hosted by the sandstone layer in an exploration project and based on the degree of clustering a risk factor is assumed for the estimation of the volume of hydrocarbons. In this case, a low risk value is assumed, as the random distribution of the peak cells location relative to the control cells locations implies a high level of confidence in the interpolation results.

[0073] FIG. 7.2 shows an example of a strong clustered extreme values data set resulted from the interpolation of the control cells (720) shown as labeled pipes in FIG. 7.2. As shown in FIG. 7.2, the light areas that represent high clustering (density distribution) level exhibit a pronounced localization of the density distribution around the control cells locations. Based on the example described in FIG 7.1, in this case, a high risk value is assumed, as the localized distribution of the peak cells location relative to the control cells locations implies a low level of confidence in the interpolation results.

[0074] FIG. 8 shows a 3D view of an example of the peak cells' distribution in a highly clustered extreme values interpolated data. The locations of the local extrema values marking the peak cells are displayed as dark areas. The input data associated with the control cells come from the wells shown as pipes (810). As shown in FIG. 8 the peak cells are clustered around the control cells. In one or more embodiments, a correlation coefficient derived based on the dataset presented in FIG. 8 yield a low value (peak cells situated close to control cells relatively to the average distance of volume cells). In this case the interpolation results present a low level of confidence.

[0075] Embodiments may be implemented on an E&P computing system. Any combination of mobile, desktop, server, router, switch, embedded device, or other types of hardware may be used. For example, as shown in FIG. 9.1, the E&P computing system (900) may include one or more computer processors (902), non- persistent storage (904) (e.g., volatile memory, such as random access memory (RAM), cache memory), persistent storage (906) (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory, etc.), a communication interface (912) (e.g., Bluetooth interface, infrared interface, network interface, optical interface, etc.), and numerous other elements and functionalities.

[0076] The computer processor(s) (902) may be an integrated circuit for processing instructions. For example, the computer processor(s) may be one or more cores or micro-cores of a processor. The E&P computing system (900) may also include one or more input devices (910), such as a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device.

[0077] The communication interface (912) may include an integrated circuit for connecting the E&P computing system (900) to a network (not shown) (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, mobile network, or any other type of network) and/or to another device, such as another computing device.

[0078] Further, the E&P computing system (900) may include one or more output devices (908), such as a screen (e.g., a liquid crystal display (LCD), a plasma display, touchscreen, cathode ray tube (CRT) monitor, projector, or other display device), a printer, external storage, or any other output device. One or more of the output devices may be the same or different from the input device(s). The input and output device(s) may be locally or remotely connected to the computer processor(s) (902), non-persistent storage (904), and persistent storage (906). Many different types of E&P computing systems exist, and the aforementioned input and output device(s) may take other forms.

[0079] Software instructions in the form of computer readable program code to perform embodiments may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer readable medium such as a CD, DVD, storage device, a diskette, a tape, flash memory, physical memory, or any other computer readable storage medium. Specifically, the software instructions may correspond to computer readable program code that, when executed by a processor(s), is configured to perform one or more embodiments.

[0080] The E&P computing system (900) in FIG. 9.1 may be connected to or be a part of a network. For example, as shown in FIG. 9.2, the network (920) may include multiple nodes (e.g., node X (922), node Y (924)). Each node may correspond to an E&P computing system, such as the E&P computing system shown in FIG. 9.1, or a group of nodes combined may correspond to the E&P computing system shown in FIG. 9.1. By way of an example, embodiments may be implemented on a node of a distributed system that is connected to other nodes. By way of another example, embodiments may be implemented on a distributed E&P computing system having multiple nodes, where each portion may be located on a different node within the distributed E&P computing system. Further, one or more elements of the aforementioned E&P computing system (900) may be located at a remote location and connected to the other elements over a network.

[0081] Although not shown in FIG. 9.2, the node may correspond to a blade in a server chassis that is connected to other nodes via a backplane. By way of another example, the node may correspond to a server in a data center. By way of another example, the node may correspond to a computer processor or micro-core of a computer processor with shared memory and/or resources.

[0082] The nodes (e.g., node X (922), node Y (924)) in the network (920) may be configured to provide services for a client device (926). For example, the nodes may be part of a cloud E&P computing system. The nodes may include functionality to receive requests from the client device (926) and transmit responses to the client device (926). The client device (926) may be an E&P computing system, such as the E&P computing system shown in FIG. 9.1. Further, the client device (926) may include and/or perform at least a portion of one or more embodiments. [0083] The E&P computing system or group of E&P computing systems described in FIG. 9.1 and 9.2 may include functionality to perform a variety of operations disclosed herein. For example, the E&P computing system(s) may perform communication between processes on the same or different system. A variety of mechanisms, employing some form of active or passive communication, may facilitate the exchange of data between processes on the same device. Examples representative of these inter-process communications include, but are not limited to, the implementation of a file, a signal, a socket, a message queue, a pipeline, a semaphore, shared memory, message passing, and a memory-mapped file. Further details pertaining to a couple of these non-limiting examples are provided below.

[0084] Based on the client-server networking model, sockets may serve as interfaces or communication channel end-points enabling bidirectional data transfer between processes on the same device. Foremost, following the client- server networking model, a server process (e.g., a process that provides data) may create a first socket object. Next, the server process binds the first socket object, thereby associating the first socket object with a unique name and/or address. After creating and binding the first socket object, the server process then waits and listens for incoming connection requests from one or more client processes (e.g., processes that seek data). At this point, when a client process wishes to obtain data from a server process, the client process starts by creating a second socket object. The client process then proceeds to generate a connection request that includes at least the second socket object and the unique name and/or address associated with the first socket object. The client process then transmits the connection request to the server process. Depending on availability, the server process may accept the connection request, establishing a communication channel with the client process, or the server process, busy in handling other operations, may queue the connection request in a buffer until server process is ready. An established connection informs the client process that communications may commence. In response, the client process may generate a data request specifying the data that the client process wishes to obtain. The data request is subsequently transmitted to the server process. Upon receiving the data request, the server process analyzes the request and gathers the requested data. Finally, the server process then generates a reply including at least the requested data and transmits the reply to the client process. The data may be transferred, more commonly, as datagrams or a stream of characters (e.g., bytes).

[0085] Shared memory refers to the allocation of virtual memory space in order to substantiate a mechanism for which data may be communicated and/or accessed by multiple processes. In implementing shared memory, an initializing process first creates a shareable segment in persistent or non-persistent storage. Post creation, the initializing process then mounts the shareable segment, subsequently mapping the shareable segment into the address space associated with the initializing process. Following the mounting, the initializing process proceeds to identify and grant access permission to one or more authorized processes that may also write and read data to and from the shareable segment. Changes made to the data in the shareable segment by one process may immediately affect other processes, which are also linked to the shareable segment. Further, when one of the authorized processes accesses the shareable segment, the shareable segment maps to the address space of that authorized process. Often, one authorized process may mount the shareable segment, other than the initializing process, at any given time.

[0086] Other techniques may be used to share data, such as the various data described in the present application, between processes without departing from the scope. The processes may be part of the same or different application and may execute on the same or different E&P computing system. [0087] Rather than or in addition to sharing data between processes, the E&P computing system performing one or more embodiments may include functionality to receive data from a user. For example, in one or more embodiments, a user may submit data via a GUI on the user device. Data may be submitted via the GUI by a user selecting one or more GUI widgets or inserting text and other data into GUI widgets using a touchpad, a keyboard, a mouse, or any other input device. In response to selecting a particular item, information regarding the particular item may be obtained from persistent or non-persistent storage by the computer processor. Upon selection of the item by the user, the contents of the obtained data regarding the particular item may be displayed on the user device in response to the user's selection.

[0088] By way of another example, a request to obtain data regarding the particular item may be sent to a server operatively connected to the user device through a network. For example, the user may select a uniform resource locator (URL) link within a web client of the user device, thereby initiating a Hypertext Transfer Protocol (HTTP) or other protocol request being sent to the network host associated with the URL. In response to the request, the server may extract the data regarding the particular selected item and send the data to the device that initiated the request. Once the user device has received the data regarding the particular item, the contents of the received data regarding the particular item may be displayed on the user device in response to the user's selection. Further to the above example, the data received from the server after selecting the URL link may provide a web page in Hyper Text Markup Language (HTML) that may be rendered by the web client and displayed on the user device.

[0089] Once data is obtained, such as by using techniques described above or from storage, the E&P computing system, in performing one or more embodiments, may extract one or more data items from the obtained data. For example, the extraction mav be nerformed as follows by the E&P computing system in FIG. 9.1. First, the organizing pattern (e.g., grammar, schema, layout) of the data is determined, which may be based on one or more of the following: position (e.g., bit or column position, Nth token in a data stream, etc.), attribute (where the attribute is associated with one or more values), or a hierarchical/tree structure (consisting of layers of nodes at different levels of detail-such as in nested packet headers or nested document sections). Then, the raw, unprocessed stream of data symbols is parsed, in the context of the organizing pattern, into a stream (or layered structure) of tokens (where each token may have an associated token "type").

[0090] Next, extraction criteria are used to extract one or more data items from the token stream or structure, where the extraction criteria are processed according to the organizing pattern to extract one or more tokens (or nodes from a layered structure). For position-based data, the token(s) at the position(s) identified by the extraction criteria are extracted. For attribute/value-based data, the token(s) and/or node(s) associated with the attribute(s) satisfying the extraction criteria are extracted. For hierarchical/layered data, the token(s) associated with the node(s) matching the extraction criteria are extracted. The extraction criteria may be as simple as an identifier string or may be a query presented to a structured data repository (where the data repository may be organized according to a database schema or data format, such as XML).

[0091] The extracted data may be used for further processing by the E&P computing system. For example, the E&P computing system of FIG. 9.1, while performing one or more embodiments, may perform data comparison. Data comparison may be used to compare two or more data values (e.g., A, B). For example, one or more embodiments may determine whether A > B, A = B, A !=

B, A < B, etc. The comparison may be performed by submitting A, B, and an opcode specifying an operation related to the comparison into an arithmetic logic unit (ALU) (i.e.. circuitry that performs arithmetic and/or bitwise logical operations on the two data values). The ALU outputs the numerical result of the operation and/or one or more status flags related to the numerical result. For example, the status flags may indicate whether the numerical result is a positive number, a negative number, zero, etc. By selecting the proper opcode and then reading the numerical results and/or status flags, the comparison may be executed. For example, in order to determine if A > B, B may be subtracted from A (i.e., A - B), and the status flags may be read to determine if the result is positive (i.e., if A > B, then A - B > 0). In one or more embodiments, B may be considered a threshold, and A is deemed to satisfy the threshold if A = B or if A > B, as determined using the ALU. In one or more embodiments, A and B may be vectors, and comparing A with B includes comparing the first element of vector A with the first element of vector B, the second element of vector A with the second element of vector B, etc. In one or more embodiments, if A and B are strings, the binary values of the strings may be compared.

[0092] The E&P computing system in FIG. 9.1 may implement and/or be connected to a data repository. For example, one type of data repository is a database. A database is a collection of information configured for ease of data retrieval, modification, re-organization, and deletion. Database Management System (DBMS) is a software application that provides an interface for users to define, create, query, update, or administer databases.

[0093] The user, or software application, may submit a statement or query into the

DBMS. Then the DBMS interprets the statement. The statement may be a select statement to request information, update statement, create statement, delete statement, etc. Moreover, the statement may include parameters that specify data, or data container (database, table, record, column, view, etc.), identifier(s), conditions (comparison operators), functions (e.g. join, full join, count, average, etc.), sort (e.g. ascending, descending), or others. The DBMS may execute the statement. For exarrmle. the DBMS may access a memory buffer, a reference or index a file for read, write, deletion, or any combination thereof, for responding to the statement. The DBMS may load the data from persistent or non-persistent storage and perform computations to respond to the query. The DBMS may return the result(s) to the user or software application.

[0094] The E&P computing system of FIG. 9.1 may include functionality to present raw and/or processed data, such as results of comparisons and other processing. For example, presenting data may be accomplished through various presenting methods. Specifically, data may be presented through a user interface provided by a computing device. The user interface may include a GUI that displays information on a display device, such as a computer monitor or a touchscreen on a handheld computer device. The GUI may include various GUI widgets that organize what data is shown as well as how data is presented to a user. Furthermore, the GUI may present data directly to the user, e.g., data presented as actual data values through text, or rendered by the computing device into a visual representation of the data, such as through visualizing a data model.

[0095] For example, a GUI may first obtain a notification from a software application requesting that a particular data object be presented within the GUI. Next, the GUI may determine a data object type associated with the particular data object, e.g., by obtaining data from a data attribute within the data object that identifies the data object type. Then, the GUI may determine any rules designated for displaying that data object type, e.g., rules specified by a software framework for a data object class or according to any local parameters defined by the GUI for presenting that data object type. Finally, the GUI may obtain data values from the particular data object and render a visual representation of the data values within a display device according to the designated rules for that data object type. [0096] Data may also be presented through various audio methods. In particular, data may be rendered into an audio format and presented as sound through one or more speakers operably connected to a computing device.

[0097] Data may also be presented to a user through haptic methods. For example, haptic methods may include vibrations or other physical signals generated by the E&P computing system. For example, data may be presented to a user using a vibration generated by a handheld computer device with a predefined duration and intensity of the vibration to communicate the data.

[0098] The above description of functions present a few examples of functions performed by the E&P computing system of FIG. 9.1 and the nodes and/ or client device in FIG. 9.2. Other functions may be performed using one or more embodiments.

[0099] Although the preceding description has been described herein with reference to particular means, materials, and embodiments, it is not intended to be limited to the particular disclosed herein. By way of further example, embodiments may be utilized in conjunction with a handheld system (i.e., a phone, wrist or forearm mounted computer, tablet, or other handheld device), portable system (i.e., a laptop or portable computing system), a fixed computing system (i.e., a desktop, server, cluster, or high performance computing system), or across a network (i.e., a cloud-based system). As such, embodiments extend to functionally equivalent structures, methods, uses, program products, and compositions as are within the scope of the appended claims.

[00100] While a limited number of embodiments have been described, those skilled in the art, having benefit of this disclosure, will appreciate that other embodiments can be devised which do not depart from the scope as disclosed herein. Accordingly, the scope should be limited by the attached claims.

Claims

CLAIMS What is claimed is:

1. A method for subsurface volume evaluation comprising:

obtaining an interpolated subsurface volume comprising a plurality of control cells and a plurality of derived cells, the plurality of derived cells comprising a plurality of attribute values derived from the plurality of control cells;

selecting, according to the plurality of attribute values, a set of peak cells from the plurality of derived cells and the plurality control cells, each peak cell in the set of peak cells being a local extrema;

determining a correlation coefficient that characterizes a spatial relationship between a location of the set of peak cells and a location of a control cell; and

presenting the correlation coefficient.

2. The method of claim 1, further comprising:

performing an oilfield operation based on the determined comparison coefficient.

3. The method of claim 1, wherein the spatial relationship between the location of the set of peak cells and the location of the control cell is defined based on a distance between a location of a selected interpolated cell and a location of a nearest control cell.

4. The method of claim 1, wherein the correlation coefficient is defined as a ratio between an average distance of the set of peak cells to the plurality of control cells and an average distance of the plurality of interpolated cells to the plurality of control cells.

5. The method of claim 1, wherein the correlation coefficient is calculated at each interpolated cell location and a subsurface property volume is derived.

The method of claim 1, wherein the determined correlation coefficient is determined based on a spatial relationship between the plurality of control cells and a density distribution map.

The method of claim 6, wherein the density distribution map is calculated based on a peak cell distribution within the interpolated subsurface volume projected on a surface.

The method of claim 1, wherein the determined correlation coefficient comprises percentage of a number of the set of peak cells to a total number of volume cells within the interpolated subsurface volume.

A system for evaluating subsurface volumes generated by data interpolation procedures comprising:

a computer processor; and

memory storing instructions, which, when executed on the computer processor, cause the computer processor to:

obtain an interpolated subsurface volume comprising a plurality of control cells and a plurality of derived cells, the plurality of derived cells comprising a plurality of attribute values derived from the plurality of control cells;

select, according to the plurality of attribute values, a set of peak cells, wherein an attribute value from the plurality of derived cells or the plurality of control cells in the set of peak cells is a local extrema; determine a correlation coefficient that characterizes a spatial relationship between a location of the set of peak cells and a location of a control cell; and

present the determined correlation coefficient.

10. The system of claim 9, wherein the spatial relationship between the location of the set of peak cells and the location of the control cell is defined based on a distance between a location of a selected interpolated cell and a location of a nearest control cell.

11. The system of claim 9, wherein the determined correlation coefficient is defined as a ratio between an average distance of the set of peak cells to the plurality of control cells and an average distance of interpolated cells to the plurality of control cells.

12. The system of claim 9, wherein the determined correlation coefficient is determined based on a spatial relationship between the plurality of control cells and a density distribution map.

13. The system of claim 12, wherein the density distribution map is calculated based on a peak cell distribution within the interpolated subsurface volume projected on a surface.

14. The system of claim 9, wherein the determined correlation coefficient is defined as a percentage of a number of the set of peak cells to the total number of cells within the interpolated subsurface volume.

15. A non-transitory computer readable medium comprising computer readable program code for causing a computer processor to:

obtain an interpolated subsurface volume comprising a plurality of control cells and a plurality of derived cells, the plurality of derived cells comprising a plurality of attribute values derived from the plurality of control cells;

select, according to the plurality of attribute values, a set of peak cells from the plurality of derived cells and the plurality control cells, each peak cell in the set of peak cells being a local extrema; determine a correlation coefficient that characterizes a spatial relationship between a location of the set of peak cells and a location of a control cell; and present the correlation coefficient.

16. The non-transitory computer readable medium of claim 15, wherein the computer readable program code further causes the computer processor to:

performing an oilfield operation based on the determined comparison coefficient.

17. The non-transitory computer readable medium of claim 15, wherein the spatial relationship between the location of the set of peak cells and the location of the control cell is defined based on a distance between a location of a selected interpolated cell and a location of a nearest control cell.

18. The non-transitory computer readable medium of claim 15, wherein the correlation coefficient is defined as a ratio between an average distance of the set of peak cells to the plurality of control cells and an average distance of the plurality of interpolated cells to the plurality of control cells.

19. The non-transitory computer readable medium of claim 15, wherein the correlation coefficient is calculated at each interpolated cell location and a subsurface property volume is derived.

20. The non-transitory computer readable medium of claim 15, wherein the determined correlation coefficient is determined based on a spatial relationship between the plurality of control cells and a density distribution map.