EP4649339A1 - Determining a detectability probability of subsurface evolutions related to a reservoir - Google Patents

Determining a detectability probability of subsurface evolutions related to a reservoir

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Publication number
EP4649339A1
EP4649339A1 EP23707457.0A EP23707457A EP4649339A1 EP 4649339 A1 EP4649339 A1 EP 4649339A1 EP 23707457 A EP23707457 A EP 23707457A EP 4649339 A1 EP4649339 A1 EP 4649339A1
Authority
EP
European Patent Office
Prior art keywords
seismic
graph
determining
value
representation
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP23707457.0A
Other languages
German (de)
French (fr)
Inventor
Didier Rappin
Christian Hubans
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
TotalEnergies Onetech SAS
Original Assignee
TotalEnergies Onetech SAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by TotalEnergies Onetech SAS filed Critical TotalEnergies Onetech SAS
Publication of EP4649339A1 publication Critical patent/EP4649339A1/en
Pending legal-status Critical Current

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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
    • G01V1/30—Analysis
    • G01V1/308—Time lapse or 4D effects, e.g. production related effects to the formation
    • 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
    • G01V1/34—Displaying seismic recordings or visualisation of seismic data or attributes

Definitions

  • the disclosure relates to the field of computer programs and systems, and more specifically to a method, system and program for determining, based on at least one 4D seismic attribute, a detectability probability of subsurface evolutions related to a reservoir.
  • 4D seismic data processing relates to techniques that allow monitoring the evolution of a subsoil over a period based on the acquisition of a 3D seismic signal of the subsoil at different times covering the period. This is particularly useful in the context of extraction and/or injection of any type of material out of and/or into a zone of the subsoil, for example oil and/or gas production, CO2 sequestration, or any waste disposal.
  • 4D seismic data offer an understanding of the impact of operation on subsoil evolution and may thus be used, for example, to enhance the process.
  • Seismic data may typically be acquired during a seismic campaign. During this campaign, it may be desired or required to assess and/or quantify the confidence in the measurements by designing record specificities in agreement with the detectability objective
  • the method comprises providing a 4D seismic model of a subsoil that includes the reservoir.
  • the method further comprises providing a seismic repeatability value associated with a seismic acquisition.
  • the method further comprises determining a graph representation of the 4D seismic model.
  • the graph represents values of a petrophysical property based on a 4D seismic attribute associated with the 4D seismic model.
  • the graph includes a representation of the seismic repeatability value and a representation of a cumulative density function of the 4D seismic attribute.
  • the method further comprises determining, based on the representation of the cumulative density function and of the seismic repeatability value on the graph, a detection probability for a given value of the petrophysical property.
  • the method may comprise one or more of the following:
  • - determining the detection probability comprises determining a density of points given by the cumulative density function at the intersection between the seismic repeatability value and the given value;
  • the detection probability equals 100% minus the determined density of points at the intersection
  • the method further comprises, during the determining of the detection probability, displaying the graph;
  • - displaying the graph comprises positioning the seismic repeatability value on the graph and representing the cumulative density function on the graph;
  • - providing the 4D seismic model comprises: o providing a geomodel of the subsoil; and o determining the 4D seismic model by performing simulations using the geomodel and based on variations of petrophysical properties for the subsoil;
  • the 4D seismic attribute is an amplitude difference or a time shift
  • the method further comprises determining an uncertainty relative the seismic repeatability value.
  • a computer readable storage medium having recorded thereon the computer program. It is further provided a system comprising a processor coupled to a memory and a graphical user interface, the memory having recorded thereon the computer program.
  • FIG. 1 illustrates the method
  • FIG. 2 shows an example of the system.
  • the method comprises providing a 4D seismic model of a subsoil that includes the reservoir.
  • the method further comprises providing a seismic repeatability value associated with a seismic acquisition.
  • the method further comprises determining a graph representation of the 4D seismic model.
  • the graph represents values of a petrophysical property based on a 4D seismic attribute associated with the 4D seismic model.
  • the graph includes a representation of the seismic repeatability value and a representation of a cumulative density function of the 4D seismic attribute.
  • the method further comprises determining, based on the representation of the cumulative density function and of the seismic repeatability value on the graph, a detection probability for a given value of the petrophysical property.
  • the method allows to determine a detection probability for a given value of a petrophysical property.
  • the method allows to quantify a certainty/un certainty of detection of a given value of the petrophysical property, i.e. to provide a confidence quantification for the detection of this given value. This may for example be done for several such given values, several petrophysical properties and/or several 4D seismic properties.
  • the method thus provides an objective quantification of detection uncertainties for petrophysical property values.
  • the method may thereby be used upstream to a seismic campaign, the provide one or more confidence predictions (i.e.
  • the method may thus be referred to as a method for computing success probability of a 4D seismic integrating geological risks and seismic risks.
  • the method may thereby be included in a seismic data acquisition process, comprising:
  • the seismic data acquisition process may itself be included in, or be an initial/upstream stage of, a hydrocarbon (e.g. oil and/or gas) production and/or gas (e.g. CO2, H2S and/or H) storage process, which comprises, besides and/or after the seismic data acquisition process, performing one or more physical actions for hydrocarbon production and/or gas storage based on the results of the seismic data acquisition process.
  • Such actions may include one or more of: determining locations of one or more exploration or exploitation wells to be drilled, drilling the well(s), determining locations for gas storage and/or storing the gas.
  • This graph is a representation of a provided 4D seismic model of a subsoil that includes the reservoir and represents values of a petrophysical property based on a 4D seismic attribute associated with the 4D seismic model.
  • the graph includes a representation of the seismic repeatability value and a representation of a cumulative density function of the 4D seismic attribute, based on which the determination of a detection probability is performed by the method.
  • the graph thus includes the objective physical data and values that allow determining the detection probability, and thereby forms a compact and ergonomic graphical tool that allows objective determination of the detection probability. In other words, a user needs only reading the graph to objectively obtain an objective detection probability.
  • the method thus provides for an ergonomically improved solution for detection probability determination for petrophysical properties values.
  • the method is for determining, based on at least one 4D seismic attribute, a detectability probability of subsurface evolutions related to a reservoir.
  • This detection probability outputted by the method is a detection probability for a given value of a petrophysical property (i.e. of or related to the subsoil), that is it represents a probability that such value will be detected by seismic measurements for this property of the subsoil, given the repeatability value.
  • the method may be for gas storage (e.g. CO 2 , H 2 S and/or H storage) and/or hydrocarbon production, "for gas storage (e.g.
  • CO2, H2S and/or H storage and/or hydrocarbon production means that the output of the method has an implied further use for gas storage and/or hydrocarbon production.
  • the method may be included in, or may be an upstream stage of, a gas storage (e.g. C0 2 , H 2 S and/or H storage) and/or hydrocarbon production process, as discussed hereinbefore.
  • a subsurface evolution designates any geological time event related to the reservoir (e.g. a past seism, for example materialized by the presence of a crack) and that modified the subsoil that includes the reservoir and even for example the reservoir itself.
  • the reservoir may be a hydrocarbon reservoir and/or a gas reservoir.
  • Such an evolution can be detected based on values of petrophysical properties of the subsoil as measured by seismic measurements, and thus, by determining detection probabilities for such values, the method provides detection probabilities for subsurface evolutions related to the subsoil that includes the reservoir.
  • the reservoir is included in a subsoil, which is a portion of the earth sub-surface.
  • the method comprises providing a 4D seismic model of the subsoil that includes the reservoir.
  • the concept of 4D model is known per se from geology and geosciences.
  • the 4D model corresponds to a time evolution of a 3D seismic model of the subsoil.
  • the 3D model represents seismic data (e.g. represents a 3D seismic signal) describing the subsoil at a given point of time, so that the 4D model represents the time evolution of a description of the subsoil by seismic data.
  • the seismic data may in the case of the method stem from simulations, e.g. based on a geomodel as discussed hereinafter.
  • the 4D seismic model involved in the method may be a simulated model, i.e. results from simulations (e.g. fluid flow simulations based on a geomodel), and not a 4D seismic model that corresponds to a measured 4D seismic signal.
  • the method may thereby be performed upstream to a seismic data acquisition process.
  • Providing the 4D seismic model may comprise providing a geomodel of the subsoil, and determining the 4D seismic model by performing simulations, for example flow simulations, using the geomodel and based on variations of petrophysical properties for the subsoil.
  • the petrophysical properties may include at least the petrophysical property of which the given value's detection probability is determined by the method.
  • the petrophysical properties may include one or more of pressure, pore size, fluid content, rock intrinsic property, and/or rock frame.
  • the concept of geomodel is known per se from geology. As known per se, the geomodel represents geological structures of the subsoil, such as horizons, fault surfaces, channels, and/or any geological unit.
  • the geomodel may optionally comprise geological a simulation grid.
  • the geological simulation grid may comprise a geometrical grid which represents the subsoil.
  • the geometrical grid may conform to shapes of the geological structures. For example, horizons may correspond to layer structures of the geometrical grid and fault surfaces may correspond to stair-stepped structures of the geometrical grid.
  • the geological simulation grid may further comprise parameters which represent the petrophysical properties, such as flow parameters, and which are assigned to geometrical structures of the geometrical grid.
  • the geological simulation grid may be inputted to a fluid flow simulator which performs a fluid flow simulation, according to the shapes of the geometrical grid and/or to the values of the petrophysical properties conveyed by the parameters.
  • the geomodel may be an existing geomodel, for example determined based on past knowledge and/or data about the subsoil.
  • providing the 4D seismic model may comprise retrieving (e.g. downloading) an existing and already created 4D seismic model, e.g. from a (e.g. distant) memory or database or server.
  • the method further comprises providing a seismic repeatability value associated with a seismic acquisition (i.e. with a seismic acquisition performed for the subsoil), e.g. with the acquisition survey design (i.e. the seismic acquisition), the geometry of the subsoil (e.g. and its property) and the environment conditions during the survey.
  • the seismic repeatability value is a value that represents a disparity (e.g. a noise) between two seismic data (e.g. measures) that are supposed to be identical. Such a disparity or noise may be due to material conditions of the seismic data acquisitions not being perfectly repeatable, like seismic source position and power and/or as receivers' position.
  • the seismic repeatability value may thus be associated with and/or associated with the equipment to perform a seismic data acquisition for the subsoil.
  • the seismic repeatability value may be associated with the geology and/or geography of the subsoil.
  • Providing the seismic repeatability value may comprise computing or retrieving (e.g. downloading from a (e.g. distant) memory or database or server) a seismic repeatability value associated with knowledge about the subsoil and/or previously acquired seismic data about the subsoil.
  • the method further comprises determining a graph representation of the 4D seismic model.
  • the graph represents values of a petrophysical property change based on a 4D seismic attribute change obtained throughout the modeling process.
  • the graph represents values of the petrophysical properties evolution as a function of the 4D seismic attribute evolution.
  • the graph may comprise a Y-Axis that represents values of the petrophysical property, and an X-Axis that represents values of the 4D seismic attribute.
  • the petrophysical property may be any one of pressure, pore size, fluid content, rock intrinsic property, and/or rock frame.
  • the 4D seismic attribute may be any one of an amplitude difference or a time-shift, which are both known per se in geology and geosciences.
  • the time-shift is a difference of seismic propagation travel-time in a geologic environment, such as the subsoil.
  • the graph includes a representation of the seismic repeatability value, for example as a line perpendicular to the seismic amplitude attribute axis, thus which represents a constant X value.
  • the graph also includes a representation of a cumulative density function of the 4D seismic attribute.
  • the function may be computed for/on the axis of the 4D seismic attribute in the graph, that is the function has the 4D seismic attribute as variable and represents the cumulative density of points along the X-Axis.
  • the cumulative density function may also be referred to as "cumulative distribution function", which is known per se in mathematics. For each value of the property, the function at x (belonging to the X-axis) is integral from — oo to x of the points having value less or equal to this value of the property as a function of the 4D seismic variable.
  • p(y) represents the distribution of points having value less or equal to this value of the property as a function of the 4D seismic variable.
  • the method further comprises determining, based on the representation of the cumulative density function and of the seismic repeatability value on the graph, a detection probability for a given value (e.g. represented as a line parallel to the X-axis on the graph) of the petrophysical property.
  • a detection probability for a given value e.g. represented as a line parallel to the X-axis on the graph
  • the method determines, i.e. using the representation of the cumulative density function and of the seismic repeatability value on the graph, a probability of detecting this given value. That is, a probability of detecting this value (e.g. in a seismic data acquisition process) given the repeatability value associated with the subsoil and the 4D seismic model associated with the subsoil.
  • the method may further comprise, during the determining of the detection probability, displaying the graph.
  • the graph may for example be displayed automatically, or automatically upon the user launching a display functionality, upon the determination.
  • Determining the density value given by the cumulative density function at the intersection between the seismic repeatability value and the given value may be carried out graphically, in this case where the graph is displayed.
  • This determination may for example comprise graphically reading the value of the function at the intersection, and/or clicking or touching or placing the mouse cursor at the intersection to trigger an automatic display of the value of the function at the intersection.
  • Displaying the graph may comprise positioning the seismic repeatability value on the graph and representing the cumulative density function on the graph.
  • the seismic repeatability value and the cumulative density function may be represented graphically on the graph during the display.
  • the repeatability value may for example be displayed as line parallel to the Y-Axis, thus corresponding to a fixed value of the 4D seismic parameter in the X-Axis.
  • the cumulative density function may be represented by color stripes, for readability point of view, each color stripe consisting of the graph points for which the function's value is comprised between two percentages.
  • the color stripes may correspond to an increment of a certain percentage, denoted for example p%.
  • the first stripe consists of the points for which the function's value is comprises between 0% and p%
  • the second stripe consists of the points for which the function's value is between p% and 2p%, and so on.
  • p may in examples equal 5, 10, 15, 20, or 25.
  • Each stripe has a different color (/.e. different from the colors of the other stripes). It may also be a continuous color scaled representation, without stripe.
  • the determining of the graph representation and the determining of the detection probability may be performed for one or more other petrophysical properties and/or one or more given values. Additionally, or alternatively, the method may be repeated for different seismic repeatability values. This allows to obtain several detection probabilities for several and different repeatability values. The method may then further comprise, based on these several obtained results, determining an uncertainty relative the seismic repeatability value.
  • the modeling of seismic data about a reservoir may be performed using a computation code for computing elastic properties of the subsoil (e.g. wave propagation velocities and/or density) based on a geometric model (e.g. a geomodel) that includes the rocks properties, in particular proportions of fluids that occupy the porosity (i.e. the non-rock part).
  • a geometric model e.g. a geomodel
  • fluid dynamics including physical property changes of these fluids, for example pressure or temperature.
  • the seismic signal may be modeled for the initial state, also referred to as "base”, and for the various subsequent states, also referred to as “monitors”, so as to represent the process executed during the seismic monitoring of the reservoir (referred to as “4D seismic” or “time-lapse seismic”).
  • 4D seismic modeling produces attributes, in particular the amplitude relative difference between seismic images. This difference may be confronted to the quality of the seismic monitoring process, as described in PCT patent application PCT/FR2017/052481, where a repeatability value (denoted "R") is obtained.
  • the present implementation aims at replying to the four following questions (which may be involved in the computation of a Value of Information):
  • the present implementation uses a 4D model that models a seismic monitoring for one or more several dates (monitors), which provides seismic attributes that quantify the amplitude relative difference between the base seismic and each monitor seismic.
  • the present implementation also uses a model of the reservoir (e.g. the previously discussed geomodel) consisting of a geometrical set of properties describing a porous subsoil reservoir and its content into different fluids with a fluid pressure, the proportion and physical properties of each fluid in each position in this model are available at the base and monitors date, as well as pressures.
  • the implementation may thereby compute at every position the product of the useful thickness of reservoir porous medium (Hr) by the porosity "Phi" and by the change in relative proportion of a fluid (deltaSATU RATION).
  • the fluids may be water (either rich or poor in dissolved salts), liquid or gas hydrocarbon, CO2, H2S, or H2.
  • HrPhi_deltaSATURATION liquid or gas hydrocarbon
  • CO2, H2S liquid or gas hydrocarbon
  • H2S liquid or gas hydrocarbon
  • H2S liquid or gas hydrocarbon
  • 4D_Seismic_Amplitude_rel_ratio which are volume or map objects.
  • the implementation displays a sub-set of these quantities in the graph, which is 2-Axis graph, with:
  • HrPhi_deltaSATURATION or HrPhi_deltaPRESSURE one of the quantities HrPhi_deltaSATURATION or HrPhi_deltaPRESSURE, with: o
  • Y-Axis HrPhi_deltaSatGas with HuPhi_deltaSatWater ⁇ limit_Sat_Water o
  • Y-Axis HrPhi_deltaSatWater with HrPhi_deltaSatGas ⁇ limit_Sat_Gas o
  • Y-Axis deltaPRESS with HrPhi_deltaSatWater & HrPhi_deltaSatGas ⁇ limit.
  • FIG. 1 illustrates an example of a screenshot of the graph. As can be seen on FIG. 1:
  • the repeatability value R is displayed on the graph, as well as its symmetric -R for the negative values of the abscissa axis.
  • the detectability value D is the value of CFD-x at the intersection of the repeatability line R with the dotted line D (that corresponds to a given value of the property on the ordinate axis).
  • the detection probability P is a function consisting of the points along the vertical axis and through R (or -R) and equals 100%-CFD-x.
  • the thickness limit of the change of saturation that ensures that the defined seismic monitoring (type of apparatus for acquiring the measurements) will allow to detect with a probability larger than the limit given by the operator, by integrating a large set of modeled values. - The most probable thickness and its variability so that the saturation effect and the pressure effect cancel each other, making the seismic monitoring incapable of detection.
  • the implementation allows an explicit measure of the minimal detectable thickness and of the thickness compensated by the pressure effect, depending on the statistics desired by the user, by a simple graph description.
  • the method is computer-implemented. This means that steps (or substantially all the steps) of the method are executed by at least one computer, or any system alike. Thus, steps of the method are performed by the computer, possibly fully automatically, or, semi-automatically. In examples, the triggering of at least some of the steps of the method may be performed through user-computer interaction.
  • the level of user-computer interaction required may depend on the level of automatism foreseen and put in balance with the need to implement user's wishes. In examples, this level may be user-defined and/or pre-defined.
  • a typical example of computer-implementation of a method is to perform the method with a system adapted for this purpose.
  • the system may comprise a processor coupled to a memory and a graphical user interface (GUI), the memory having recorded thereon a computer program comprising instructions for performing the method.
  • GUI graphical user interface
  • the memory may also store a database.
  • the memory is any hardware adapted for such storage, possibly comprising several physical distinct parts (e.g. one for the program, and possibly one for the database).
  • FIG. 2 shows an example of the system, wherein the system is a client computer system, e.g. a workstation of a user.
  • the system is a client computer system, e.g. a workstation of a user.
  • the client computer of the example comprises a central processing unit (CPU) 1010 connected to an internal communication BUS 1000, a random access memory (RAM) 1070 also connected to the BUS.
  • the client computer is further provided with a graphical processing unit (GPU) 1110 which is associated with a video random access memory 1100 connected to the BUS.
  • Video RAM 1100 is also known in the art as frame buffer.
  • a mass storage device controller 1020 manages accesses to a mass memory device, such as hard drive 1030.
  • Mass memory devices suitable for tangibly embodying computer program instructions and data include all forms of nonvolatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks.
  • a network adapter 1050 manages accesses to a network 1060.
  • the client computer may also include a haptic device 1090 such as cursor control device, a keyboard or the like.
  • a cursor control device is used in the client computer to permit the user to selectively position a cursor at any desired location on display 1080.
  • the cursor control device allows the user to select various commands, and input control signals.
  • the cursor control device includes a number of signal generation devices for input control signals to system.
  • a cursor control device may be a mouse, the button of the mouse being used to generate the signals.
  • the client computer system may comprise a sensitive pad, and/or a sensitive screen.
  • the computer program may comprise instructions executable by a computer, the instructions comprising means for causing the above system to perform the method.
  • the program may be recordable on any data storage medium, including the memory of the system.
  • the program may for example be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them.
  • the program may be implemented as an apparatus, for example a product tangibly embodied in a machine-readable storage device for execution by a programmable processor. Method steps may be performed by a programmable processor executing a program of instructions to perform functions of the method by operating on input data and generating output.
  • the processor may thus be programmable and coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device.
  • the application program may be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired. In any case, the language may be a compiled or interpreted language.
  • the program may be a full installation program or an update program. Application of the program on the system results in any case in instructions for performing the method.
  • the computer program may alternatively be stored and executed on a server of a cloud computing environment, the server being in communication across a network with one or more clients. In such a case a processing unit executes the instructions comprised by the program, thereby causing the method to be performed on the cloud computing environment.

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Abstract

The disclosure notably relates to a computer-implemented method for determining, based on at least one 4D seismic attribute, a detectability probability of subsurface evolutions related to a reservoir. The method comprises providing a 4D seismic model of a subsoil that includes the reservoir. The method further comprises providing a seismic repeatability value associated with a seismic acquisition. The method further comprises determining a graph representation of the 4D seismic model. The graph represents values of a petrophysical property based on a 4D seismic attribute associated with the 4D seismic model. The graph includes a representation of the seismic repeatability value and a representation of a cumulative density function of the 4D seismic attribute. The method further comprises determining, based on the representation of the cumulative density function and of the seismic repeatability value on the graph, a detection probability for a given value of the petrophysical property.

Description

DETERMINING A DETECTABILITY PROBABILITY OF SUBSURFACE EVOLUTIONS RELATED TO A RESERVOIR
TECHNICAL FIELD
The disclosure relates to the field of computer programs and systems, and more specifically to a method, system and program for determining, based on at least one 4D seismic attribute, a detectability probability of subsurface evolutions related to a reservoir.
BACKGROUND
4D seismic data processing relates to techniques that allow monitoring the evolution of a subsoil over a period based on the acquisition of a 3D seismic signal of the subsoil at different times covering the period. This is particularly useful in the context of extraction and/or injection of any type of material out of and/or into a zone of the subsoil, for example oil and/or gas production, CO2 sequestration, or any waste disposal. In such a context, 4D seismic data offer an understanding of the impact of operation on subsoil evolution and may thus be used, for example, to enhance the process.
Seismic data may typically be acquired during a seismic campaign. During this campaign, it may be desired or required to assess and/or quantify the confidence in the measurements by designing record specificities in agreement with the detectability objective
Within this context and other contexts, there is still a need for an improved method for determining, based on at least one 4D seismic attribute, a detectability probability of subsurface evolutions related to a reservoir.
SUMMARY
It is therefore provided a computer-implemented method for determining, based on at least one 4D seismic attribute, a detectability probability of subsurface evolutions related to a reservoir. The method comprises providing a 4D seismic model of a subsoil that includes the reservoir. The method further comprises providing a seismic repeatability value associated with a seismic acquisition. The method further comprises determining a graph representation of the 4D seismic model. The graph represents values of a petrophysical property based on a 4D seismic attribute associated with the 4D seismic model. The graph includes a representation of the seismic repeatability value and a representation of a cumulative density function of the 4D seismic attribute. The method further comprises determining, based on the representation of the cumulative density function and of the seismic repeatability value on the graph, a detection probability for a given value of the petrophysical property.
The method may comprise one or more of the following:
- determining the detection probability comprises determining a density of points given by the cumulative density function at the intersection between the seismic repeatability value and the given value;
- the detection probability equals 100% minus the determined density of points at the intersection;
- the method further comprises, during the determining of the detection probability, displaying the graph;
- displaying the graph comprises positioning the seismic repeatability value on the graph and representing the cumulative density function on the graph;
- providing the 4D seismic model comprises: o providing a geomodel of the subsoil; and o determining the 4D seismic model by performing simulations using the geomodel and based on variations of petrophysical properties for the subsoil;
- the 4D seismic attribute is an amplitude difference or a time shift;
- the method is repeated for different seismic repeatability values; and/or
- the method further comprises determining an uncertainty relative the seismic repeatability value.
It is further provided a computer program comprising instructions for performing the method.
It is further provided a computer readable storage medium having recorded thereon the computer program. It is further provided a system comprising a processor coupled to a memory and a graphical user interface, the memory having recorded thereon the computer program.
BRIEF DESCRIPTION OF THE DRAWINGS
Non-limiting examples will now be described in reference to the accompanying drawings, where:
FIG. 1 illustrates the method; and
FIG. 2 shows an example of the system.
DETAILED DESCRIPTION
It is therefore provided a computer-implemented method for determining, based on at least one 4D seismic attribute, a detectability probability of subsurface evolutions related to a reservoir. The method comprises providing a 4D seismic model of a subsoil that includes the reservoir. The method further comprises providing a seismic repeatability value associated with a seismic acquisition. The method further comprises determining a graph representation of the 4D seismic model. The graph represents values of a petrophysical property based on a 4D seismic attribute associated with the 4D seismic model. The graph includes a representation of the seismic repeatability value and a representation of a cumulative density function of the 4D seismic attribute. The method further comprises determining, based on the representation of the cumulative density function and of the seismic repeatability value on the graph, a detection probability for a given value of the petrophysical property.
This constitutes an improved solution for determining a detectability probability of subsurface evolutions related to a reservoir.
Notably, the method allows to determine a detection probability for a given value of a petrophysical property. In other words, the method allows to quantify a certainty/un certainty of detection of a given value of the petrophysical property, i.e. to provide a confidence quantification for the detection of this given value. This may for example be done for several such given values, several petrophysical properties and/or several 4D seismic properties. The method thus provides an objective quantification of detection uncertainties for petrophysical property values. The method may thereby be used upstream to a seismic campaign, the provide one or more confidence predictions (i.e. in the form of or based on one or more determined detection probabilities) for one or more values of one or more petrophysical properties, thereby providing confidence predictions in the subsurface evolutions to be detected by the downstream seismic campaign. The method may thus be referred to as a method for computing success probability of a 4D seismic integrating geological risks and seismic risks.
The method may thereby be included in a seismic data acquisition process, comprising:
- performing one or more executions of the method, thereby providing one or more confidence predictions (i.e. in the form of or based on one or more determined detection probabilities) for one or more values of one or more petrophysical properties;
- performing a seismic campaign to acquire seismic measurements, the campaign acquisition parameters being set and optimized based on the prediction(s) given by the method; and
- optionally, determining a 3D seismic model and/or a 4D seismic model based on the measurements, and optionally based on the prediction(s) given by the method.
The seismic data acquisition process may itself be included in, or be an initial/upstream stage of, a hydrocarbon (e.g. oil and/or gas) production and/or gas (e.g. CO2, H2S and/or H) storage process, which comprises, besides and/or after the seismic data acquisition process, performing one or more physical actions for hydrocarbon production and/or gas storage based on the results of the seismic data acquisition process. Such actions may include one or more of: determining locations of one or more exploration or exploitation wells to be drilled, drilling the well(s), determining locations for gas storage and/or storing the gas.
Furthermore, the method allows the determination of the detection probability using the graph determined by the method. This graph is a representation of a provided 4D seismic model of a subsoil that includes the reservoir and represents values of a petrophysical property based on a 4D seismic attribute associated with the 4D seismic model. The graph includes a representation of the seismic repeatability value and a representation of a cumulative density function of the 4D seismic attribute, based on which the determination of a detection probability is performed by the method. The graph thus includes the objective physical data and values that allow determining the detection probability, and thereby forms a compact and ergonomic graphical tool that allows objective determination of the detection probability. In other words, a user needs only reading the graph to objectively obtain an objective detection probability. The method thus provides for an ergonomically improved solution for detection probability determination for petrophysical properties values.
The method is for determining, based on at least one 4D seismic attribute, a detectability probability of subsurface evolutions related to a reservoir. This means that the method uses at least one 4D seismic attribute and outputs data representing the detectability probability, i.e. the determined detection probability that is determined by the method. This detection probability outputted by the method is a detection probability for a given value of a petrophysical property (i.e. of or related to the subsoil), that is it represents a probability that such value will be detected by seismic measurements for this property of the subsoil, given the repeatability value. The method may be for gas storage (e.g. CO2, H2S and/or H storage) and/or hydrocarbon production, "for gas storage (e.g. CO2, H2S and/or H storage) and/or hydrocarbon production" means that the output of the method has an implied further use for gas storage and/or hydrocarbon production. For example, the method may be included in, or may be an upstream stage of, a gas storage (e.g. C02, H2S and/or H storage) and/or hydrocarbon production process, as discussed hereinbefore. A subsurface evolution designates any geological time event related to the reservoir (e.g. a past seism, for example materialized by the presence of a crack) and that modified the subsoil that includes the reservoir and even for example the reservoir itself. The reservoir may be a hydrocarbon reservoir and/or a gas reservoir. Such an evolution can be detected based on values of petrophysical properties of the subsoil as measured by seismic measurements, and thus, by determining detection probabilities for such values, the method provides detection probabilities for subsurface evolutions related to the subsoil that includes the reservoir. The reservoir is included in a subsoil, which is a portion of the earth sub-surface.
The method comprises providing a 4D seismic model of the subsoil that includes the reservoir. The concept of 4D model is known per se from geology and geosciences. As known per se, the 4D model corresponds to a time evolution of a 3D seismic model of the subsoil. The 3D model represents seismic data (e.g. represents a 3D seismic signal) describing the subsoil at a given point of time, so that the 4D model represents the time evolution of a description of the subsoil by seismic data. The seismic data may in the case of the method stem from simulations, e.g. based on a geomodel as discussed hereinafter. In other words, the 4D seismic model involved in the method may be a simulated model, i.e. results from simulations (e.g. fluid flow simulations based on a geomodel), and not a 4D seismic model that corresponds to a measured 4D seismic signal. The method may thereby be performed upstream to a seismic data acquisition process.
Providing the 4D seismic model may comprise providing a geomodel of the subsoil, and determining the 4D seismic model by performing simulations, for example flow simulations, using the geomodel and based on variations of petrophysical properties for the subsoil. The petrophysical properties may include at least the petrophysical property of which the given value's detection probability is determined by the method. The petrophysical properties may include one or more of pressure, pore size, fluid content, rock intrinsic property, and/or rock frame. The concept of geomodel is known per se from geology. As known per se, the geomodel represents geological structures of the subsoil, such as horizons, fault surfaces, channels, and/or any geological unit. The geomodel may optionally comprise geological a simulation grid. The geological simulation grid may comprise a geometrical grid which represents the subsoil. The geometrical grid may conform to shapes of the geological structures. For example, horizons may correspond to layer structures of the geometrical grid and fault surfaces may correspond to stair-stepped structures of the geometrical grid. The geological simulation grid may further comprise parameters which represent the petrophysical properties, such as flow parameters, and which are assigned to geometrical structures of the geometrical grid. The geological simulation grid may be inputted to a fluid flow simulator which performs a fluid flow simulation, according to the shapes of the geometrical grid and/or to the values of the petrophysical properties conveyed by the parameters. The geomodel may be an existing geomodel, for example determined based on past knowledge and/or data about the subsoil.
Alternatively, providing the 4D seismic model may comprise retrieving (e.g. downloading) an existing and already created 4D seismic model, e.g. from a (e.g. distant) memory or database or server.
The method further comprises providing a seismic repeatability value associated with a seismic acquisition (i.e. with a seismic acquisition performed for the subsoil), e.g. with the acquisition survey design (i.e. the seismic acquisition), the geometry of the subsoil (e.g. and its property) and the environment conditions during the survey. The seismic repeatability value is a value that represents a disparity (e.g. a noise) between two seismic data (e.g. measures) that are supposed to be identical. Such a disparity or noise may be due to material conditions of the seismic data acquisitions not being perfectly repeatable, like seismic source position and power and/or as receivers' position. The seismic repeatability value may thus be associated with and/or associated with the equipment to perform a seismic data acquisition for the subsoil. Additionally, or alternatively, the seismic repeatability value may be associated with the geology and/or geography of the subsoil. Providing the seismic repeatability value may comprise computing or retrieving (e.g. downloading from a (e.g. distant) memory or database or server) a seismic repeatability value associated with knowledge about the subsoil and/or previously acquired seismic data about the subsoil.
The method further comprises determining a graph representation of the 4D seismic model. The graph represents values of a petrophysical property change based on a 4D seismic attribute change obtained throughout the modeling process. In other words, the graph represents values of the petrophysical properties evolution as a function of the 4D seismic attribute evolution. For example, the graph may comprise a Y-Axis that represents values of the petrophysical property, and an X-Axis that represents values of the 4D seismic attribute. The petrophysical property may be any one of pressure, pore size, fluid content, rock intrinsic property, and/or rock frame. The 4D seismic attribute may be any one of an amplitude difference or a time-shift, which are both known per se in geology and geosciences. The time-shift is a difference of seismic propagation travel-time in a geologic environment, such as the subsoil. The graph includes a representation of the seismic repeatability value, for example as a line perpendicular to the seismic amplitude attribute axis, thus which represents a constant X value. The graph also includes a representation of a cumulative density function of the 4D seismic attribute. The function may be computed for/on the axis of the 4D seismic attribute in the graph, that is the function has the 4D seismic attribute as variable and represents the cumulative density of points along the X-Axis. The cumulative density function may also be referred to as "cumulative distribution function", which is known per se in mathematics. For each value of the property, the function at x (belonging to the X-axis) is integral from — oo to x of the points having value less or equal to this value of the property as a function of the 4D seismic variable.
The cumulative density function of a random variable X is defined by F(x) = P(X < x).
The cumulative density function is monotone increasing, meaning that xr < x2 implies F(x1) < F(x2). Thisfollows simplyfrom the fact that {X < x2] = {X < x u {x2 < X < x2] and the additivity of probabilities for disjoint events. Furthermore, if X takes values between — oo and oo, like the Gaussian random variable, then F(— oo ) = 0 and F(oo) = 1 . If the random variable X is continuous and possesses a density p(x), like the Gaussian random variable does, it follows immediately from the definition of F, and since F(— oo) = 0, that dy.
In the case of the method, for each value of the property, p(y) represents the distribution of points having value less or equal to this value of the property as a function of the 4D seismic variable.
The method further comprises determining, based on the representation of the cumulative density function and of the seismic repeatability value on the graph, a detection probability for a given value (e.g. represented as a line parallel to the X-axis on the graph) of the petrophysical property. In other words, for a given value of the petrophysical property, the method determines, i.e. using the representation of the cumulative density function and of the seismic repeatability value on the graph, a probability of detecting this given value. That is, a probability of detecting this value (e.g. in a seismic data acquisition process) given the repeatability value associated with the subsoil and the 4D seismic model associated with the subsoil. Determining the detection probability may comprise determining a cumulative density value (i.e. the density of points given by the cumulative density function value) at the intersection between the seismic repeatability value (e.g. given by a line parallel to the Y-axis on the graph) and the given value (i.e. given by a line parallel to the X-axis on the graph). Determining the density of points may comprise assessing the value of the cumulative density function at the intersection. The detection probability may equal 100% minus the determined density of points at the intersection.
The method may further comprise, during the determining of the detection probability, displaying the graph. This means that the graph is persistently displayed during the determination of the detection probability. The graph may for example be displayed automatically, or automatically upon the user launching a display functionality, upon the determination. Determining the density value given by the cumulative density function at the intersection between the seismic repeatability value and the given value may be carried out graphically, in this case where the graph is displayed. This determination may for example comprise graphically reading the value of the function at the intersection, and/or clicking or touching or placing the mouse cursor at the intersection to trigger an automatic display of the value of the function at the intersection. Displaying the graph may comprise positioning the seismic repeatability value on the graph and representing the cumulative density function on the graph. In other words, the seismic repeatability value and the cumulative density function may be represented graphically on the graph during the display. The repeatability value may for example be displayed as line parallel to the Y-Axis, thus corresponding to a fixed value of the 4D seismic parameter in the X-Axis. The cumulative density function may be represented by color stripes, for readability point of view, each color stripe consisting of the graph points for which the function's value is comprised between two percentages. For example, the color stripes may correspond to an increment of a certain percentage, denoted for example p%. The first stripe consists of the points for which the function's value is comprises between 0% and p%, the second stripe consists of the points for which the function's value is between p% and 2p%, and so on. p may in examples equal 5, 10, 15, 20, or 25. Each stripe has a different color (/.e. different from the colors of the other stripes). It may also be a continuous color scaled representation, without stripe.
The determining of the graph representation and the determining of the detection probability may be performed for one or more other petrophysical properties and/or one or more given values. Additionally, or alternatively, the method may be repeated for different seismic repeatability values. This allows to obtain several detection probabilities for several and different repeatability values. The method may then further comprise, based on these several obtained results, determining an uncertainty relative the seismic repeatability value.
An implementation of the method is now discussed.
As known per se from seismic and geosciences, the modeling of seismic data about a reservoir may be performed using a computation code for computing elastic properties of the subsoil (e.g. wave propagation velocities and/or density) based on a geometric model (e.g. a geomodel) that includes the rocks properties, in particular proportions of fluids that occupy the porosity (i.e. the non-rock part). During operations for production and/or injection of fluid, in parts of the reservoir an existing fluid is replaced by another one, as a consequence of fluid dynamics (including physical property changes of these fluids, for example pressure or temperature). The seismic signal may be modeled for the initial state, also referred to as "base", and for the various subsequent states, also referred to as "monitors", so as to represent the process executed during the seismic monitoring of the reservoir (referred to as "4D seismic" or "time-lapse seismic").
4D seismic modeling produces attributes, in particular the amplitude relative difference between seismic images. This difference may be confronted to the quality of the seismic monitoring process, as described in PCT patent application PCT/FR2017/052481, where a repeatability value (denoted "R") is obtained.
The present implementation aims at replying to the four following questions (which may be involved in the computation of a Value of Information):
- given R and the 4D seismic model, what is the minimal thickness that can be detected?
- Given R and the 4D seismic model, what is the proportion of detectable signal (in terms of percentage of the surface of study)?
- What is the thickness of saturation change compensated by the pressure change? (i.e. situation for which the change in 4D seismic is 0.0.
- Given the possible compensation of fluid change effects which overlap the pressure change effects, what is the translation of the induced amplitude relative difference? This value may be exploited for a preprocessing of ground data so as to make the effects more accessible, by correcting the compensation phenomenon.
The present implementation uses a 4D model that models a seismic monitoring for one or more several dates (monitors), which provides seismic attributes that quantify the amplitude relative difference between the base seismic and each monitor seismic. The present implementation also uses a model of the reservoir (e.g. the previously discussed geomodel) consisting of a geometrical set of properties describing a porous subsoil reservoir and its content into different fluids with a fluid pressure, the proportion and physical properties of each fluid in each position in this model are available at the base and monitors date, as well as pressures. The implementation may thereby compute at every position the product of the useful thickness of reservoir porous medium (Hr) by the porosity "Phi" and by the change in relative proportion of a fluid (deltaSATU RATION). The fluids may be water (either rich or poor in dissolved salts), liquid or gas hydrocarbon, CO2, H2S, or H2. This results in usable quantities HrPhi_deltaSATURATION, deltaPRESSURE, 4D_Seismic_Amplitude_rel_ratio, which are volume or map objects. The implementation displays a sub-set of these quantities in the graph, which is 2-Axis graph, with:
- In abscissa, the amplitude relative variation of the modeled 4D seismic, for a given date.
- In ordinate, one of the quantities HrPhi_deltaSATURATION or HrPhi_deltaPRESSURE, with: o For a gas analysis, Y-Axis = HrPhi_deltaSatGas with HuPhi_deltaSatWater < limit_Sat_Water o For a water analysis, Y-Axis = HrPhi_deltaSatWater with HrPhi_deltaSatGas < limit_Sat_Gas o For a pressure analysis, Y-Axis = deltaPRESS with HrPhi_deltaSatWater & HrPhi_deltaSatGas < limit.
Based on the represented points, the present implementation computes the cumulative density function along the abscissa axis, the function being referred to as CFD-x. The implementation may use a color scale with 20 levels adapted to a reading step every 5% to reach 100% Different levels and steps may be used. FIG. 1 illustrates an example of a screenshot of the graph. As can be seen on FIG. 1:
- The repeatability value R is displayed on the graph, as well as its symmetric -R for the negative values of the abscissa axis.
- The detectability value D is the value of CFD-x at the intersection of the repeatability line R with the dotted line D (that corresponds to a given value of the property on the ordinate axis).
- The compensation thickness for the effect of the change of saturation with the pressure change is read at point C, at 50% of CFD-x.
- The detection probability P is a function consisting of the points along the vertical axis and through R (or -R) and equals 100%-CFD-x.
The above-discussed implementation allows a simultaneous evaluation of:
- The thickness limit of the change of saturation that ensures that the defined seismic monitoring (type of apparatus for acquiring the measurements) will allow to detect with a probability larger than the limit given by the operator, by integrating a large set of modeled values. - The most probable thickness and its variability so that the saturation effect and the pressure effect cancel each other, making the seismic monitoring incapable of detection.
- The effect of the change of amplitude related only to the pressure change. This value may be then used as model to modify the data before its analysis, and increases the detectability in saturation change.
- The variation of detection probability as a function of the thickness.
The implementation allows an explicit measure of the minimal detectable thickness and of the thickness compensated by the pressure effect, depending on the statistics desired by the user, by a simple graph description.
The method is computer-implemented. This means that steps (or substantially all the steps) of the method are executed by at least one computer, or any system alike. Thus, steps of the method are performed by the computer, possibly fully automatically, or, semi-automatically. In examples, the triggering of at least some of the steps of the method may be performed through user-computer interaction. The level of user-computer interaction required may depend on the level of automatism foreseen and put in balance with the need to implement user's wishes. In examples, this level may be user-defined and/or pre-defined.
A typical example of computer-implementation of a method is to perform the method with a system adapted for this purpose. The system may comprise a processor coupled to a memory and a graphical user interface (GUI), the memory having recorded thereon a computer program comprising instructions for performing the method. The memory may also store a database. The memory is any hardware adapted for such storage, possibly comprising several physical distinct parts (e.g. one for the program, and possibly one for the database).
FIG. 2 shows an example of the system, wherein the system is a client computer system, e.g. a workstation of a user.
The client computer of the example comprises a central processing unit (CPU) 1010 connected to an internal communication BUS 1000, a random access memory (RAM) 1070 also connected to the BUS. The client computer is further provided with a graphical processing unit (GPU) 1110 which is associated with a video random access memory 1100 connected to the BUS. Video RAM 1100 is also known in the art as frame buffer. A mass storage device controller 1020 manages accesses to a mass memory device, such as hard drive 1030. Mass memory devices suitable for tangibly embodying computer program instructions and data include all forms of nonvolatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks. Any of the foregoing may be supplemented by, or incorporated in, specially designed ASICs (application-specific integrated circuits). A network adapter 1050 manages accesses to a network 1060. The client computer may also include a haptic device 1090 such as cursor control device, a keyboard or the like. A cursor control device is used in the client computer to permit the user to selectively position a cursor at any desired location on display 1080. In addition, the cursor control device allows the user to select various commands, and input control signals. The cursor control device includes a number of signal generation devices for input control signals to system. Typically, a cursor control device may be a mouse, the button of the mouse being used to generate the signals. Alternatively, or additionally, the client computer system may comprise a sensitive pad, and/or a sensitive screen.
The computer program may comprise instructions executable by a computer, the instructions comprising means for causing the above system to perform the method. The program may be recordable on any data storage medium, including the memory of the system. The program may for example be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The program may be implemented as an apparatus, for example a product tangibly embodied in a machine-readable storage device for execution by a programmable processor. Method steps may be performed by a programmable processor executing a program of instructions to perform functions of the method by operating on input data and generating output. The processor may thus be programmable and coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. The application program may be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired. In any case, the language may be a compiled or interpreted language. The program may be a full installation program or an update program. Application of the program on the system results in any case in instructions for performing the method. The computer program may alternatively be stored and executed on a server of a cloud computing environment, the server being in communication across a network with one or more clients. In such a case a processing unit executes the instructions comprised by the program, thereby causing the method to be performed on the cloud computing environment.

Claims

1. A computer-implemented method for determining, based on at least one 4D seismic attribute, a detectability probability of subsurface evolutions related to a reservoir, the method comprising:
- providing: o a 4D seismic model of a subsoil that includes the reservoir; and o a seismic repeatability value associated with a seismic acquisition;
- determining a graph representation of the 4D seismic model that represents values of a petrophysical property based on a 4D seismic attribute associated with the 4D seismic model, the graph including a representation of the seismic repeatability value and a representation of a cumulative density function of the 4D seismic attribute; and
- determining, based on the representation of the cumulative density function and of the seismic repeatability value on the graph, a detection probability for a given value of the petrophysical property.
2. The method of claim 1, wherein determining the detection probability comprises determining a density of points given by the cumulative density function at the intersection between the seismic repeatability value and the given value.
3. The method of claim 2, wherein the detection probability equals 100% minus the determined density of points at the intersection.
4. The method of any one of claims 1 to 3, wherein the method further comprises, during the determining of the detection probability, displaying the graph.
5. The method of claim 4, wherein displaying the graph comprises positioning the seismic repeatability value on the graph and representing the cumulative density function on the graph.
6. The method of any one of claims 1 to 5, wherein providing the 4D seismic model comprises:
- providing a geomodel of the subsoil; and
- determining the 4D seismic model by performing simulations using the geomodel and based on variations of petrophysical properties for the subsoil.
7. The method of any one of claims 1 to 6, wherein the 4D seismic attribute is an amplitude difference or a time shift.
8. The method of any one of claims 1 to 7 , wherein the method is repeated for different seismic repeatability values.
9. The method of claim 8, wherein the method further comprises determining an uncertainty relative the seismic repeatability value.
10. A computer program comprising instructions for performing the method of any one of claims 1 to 9.
11. A computer-readable data storage medium having recorded thereon the computer program of claim 10.
12. A computer system comprising a processor coupled to a memory, the memory having recorded thereon the computer program of claim 10.
EP23707457.0A 2023-01-09 2023-01-09 Determining a detectability probability of subsurface evolutions related to a reservoir Pending EP4649339A1 (en)

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3685190B1 (en) * 2017-09-18 2022-01-26 TotalEnergies SE Processing a 4d seismic signal based on noise model

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3685190B1 (en) * 2017-09-18 2022-01-26 TotalEnergies SE Processing a 4d seismic signal based on noise model

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