System and method for assessing the presence of hydrocarbons in a subterranean reservoir based on time-lapse seismic data

A method is described for a manner of geologic analysis using time-lapse seismic data. The method includes steps to produce improved amplitude versus angle (AVA) information that may be used for analysis of geologic features of interest including estimation of pore fluid content and changes in the pore fluid content. The method assesses the probability of hydrocarbons in a subterranean reservoir based on seismic amplitude variations along offsets or angles for portions of a seismic horizon. The method may be executed by a computer system.

CROSS-REFERENCE TO RELATED APPLICATIONS

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TECHNICAL FIELD

The present disclosure relates generally to methods and systems for probabilistic analysis of geologic features using seismic data and, in particular, methods and systems for assessing the probability of hydrocarbons in a subterranean reservoir based on seismic amplitude variations along offsets or angles for portions of a seismic horizon identified on two or more seismic images generated from two or more seismic surveys performed at different times.

BACKGROUND

Seismic exploration involves surveying subterranean geological media for hydrocarbon deposits. A survey typically involves deploying seismic sources and seismic sensors at predetermined locations. The sources generate seismic waves, which propagate into the geological medium creating pressure changes and vibrations. Variations in physical properties of the geological medium give rise to changes in certain properties of the seismic waves, such as their direction of propagation and other properties.

Portions of the seismic waves reach the seismic sensors. Some seismic sensors are sensitive to pressure changes (e.g., hydrophones), others to particle motion (e.g., geophones), and industrial surveys may deploy one type of sensor or both. In response to the detected seismic waves, the sensors generate corresponding electrical signals, known as traces, and record them in storage media as seismic data. Seismic data will include a plurality of “shots” (individual instances of the seismic source being activated), each of which are associated with a plurality of traces recorded at the plurality of sensors.

In some cases, it is desirable to analyze the recorded seismic amplitudes. This may be done in many ways. One step in conventional processing of seismic reflection data involves adding multiple seismic traces that share a common mid-point, but have different source-receiver offsets. This is commonly called “stacking”. Stacking generally improves the signal to noise ratio, but can result in ambiguity surrounding the cause of the seismic amplitudes. For example, a high seismic amplitude could indicate either the presence of fluids or the presence of a particular lithology.

One conventional technique that can provide an improved method of delineating between lithology and fluids is employment of amplitude versus offset (AVO) or angle (AVA) for a representative offset/angle gather. Those of skill in the art would be aware that amplitude versus angle (AVA) is often used interchangeably with amplitude versus offset (AVO).

During processing, this type of AVA data may not be stacked thereby to preserve information that can be used to distinguish indicators of fluids from indicators of lithology. For example, considering a seismic trace, in one scenario, a hydrocarbon-bearing sand may generally have an increasingly negative seismic amplitude at further source-receiver offsets compared to a water-bearing sand which may be indicated by a decrease in positive seismic amplitude at further source-receiver offsets.

The production of hydrocarbons causes changes in the elastic parameters of the earth. These changes may occur due to water displacing oil (or vice versa), water displacing gas (or vice versa), or gas displacing oil (or vice versa), within the reservoir interval. In other cases, the changes in the elastic parameters may occur due to enhanced hydrocarbon recovery operations, CO2 injection, or clathrate dissociation from solid to gas. Time-lapse (4D) seismic data is acquired to compare seismic data at different times via two or more seismic surveys, a seismic survey at time one (T1) and another seismic survey from time two (T2), conducted months or years apart. The differences in the seismic responses for T1and T2are at least partially due to fluid movement and/or pressure changes due to production or injection of water or gas. Conventionally, these differences in seismic response are qualitatively interpreted relative to modeled response behaviors due to fluid and pressure changes. Typically, the seismic survey from T1is referred to as the baseline survey, and the seismic survey from T2is referred to as the monitor survey. However, in the case for more than one monitor survey we could be analyzing two monitor surveys, where the seismic survey from T1is an early monitor survey and the seismic survey from T2is another monitor survey recorded at some time T2where T2is months or years after T1.

The above methods may however often be biased and may not truly represent the geologic features. In addition, conventional methods may fail where seismic data quality is low, such as where random and/or coherent noise is prevalent, or where seismic gathers are not flat. The ability to define the location of rock and fluid property changes in the subsurface is crucial to our ability to make the most appropriate choices for purchasing materials, operating safely, and successfully completing projects. Project cost is dependent upon accurate prediction of the position of physical boundaries and fluid content within the Earth. Decisions include, but are not limited to, budgetary planning, obtaining mineral and lease rights, signing well commitments, permitting rig locations, designing well paths and drilling strategy, preventing subsurface integrity issues by planning proper casing and cementation strategies, and selecting and purchasing appropriate completion and production equipment.

There exists a need for seismic processing methods capable of producing improved time-lapse AVA information that may be used for analysis of geologic features of interest.

SUMMARY

In accordance with some embodiments, a method of time-lapse fluid assessment in a subterranean volume of interest including receiving a digital seismic dataset recorded at a first time representative of a subsurface volume of interest, a digital seismic dataset recorded at a second time representative of the subsurface volume of interest, and a range of geological and geophysical parameters possible in the subsurface volume of interest; identifying at least one spatial area of interest; calculating measured baseline seismic amplitude versus angle (AVA) responses from the digital seismic dataset recorded at the first time and measured monitor seismic AVA responses from the seismic dataset recorded at the second time in the at least one spatial area; computing amplitude difference AVA responses from the measured baseline seismic AVA responses and measured monitor seismic AVA responses; performing probabilistic amplitude analysis of at least two of the measured baseline seismic AVA responses, the measured monitor seismic AVA responses, and the amplitude difference AVA responses using the range of geological and geophysical parameters; and estimating time-lapse reservoir properties within the at least one spatial area of interest based on the probabilistic amplitude analysis of the measured seismic AVA responses is disclosed.

In another aspect of the present invention, to address the aforementioned problems, some embodiments provide a non-transitory computer readable storage medium storing one or more programs. The one or more programs comprise instructions, which when executed by a computer system with one or more processors and memory, cause the computer system to perform any of the methods provided herein.

In yet another aspect of the present invention, to address the aforementioned problems, some embodiments provide a computer system. The computer system includes one or more processors, memory, and one or more programs. The one or more programs are stored in memory and configured to be executed by the one or more processors. The one or more programs include an operating system and instructions that when executed by the one or more processors cause the computer system to perform any of the methods provided herein.

DETAILED DESCRIPTION OF EMBODIMENTS

Described below are methods, systems, and computer readable storage media that provide a manner of geologic analysis using seismic data. These embodiments are designed to calculate probabilities of hydrocarbons (i.e. fluid property estimation) in subsurface geologic features and changes in those probabilities after production and/or injection. Industry standard techniques use deterministic estimation of the underlying geologic and geophysical parameters which contribute to the amplitude versus angle response utilizing forward modeling or inversion. The subsurface parameters of interest are the thickness, pore fluid (brine, oil, gas), hydrocarbon saturation, porosity, etc. The present method combines probabilistic AVA/AVO (amplitude versus angle/amplitude versus offset) and spatial summation of amplitude versus offset gathers with a Bayesian analysis to determine the range of geologic and geophysical parameters that will fit a user-selected range of measured field responses with selected areas. The probabilistic estimation builds a model space with a regular grid, then a singular bin is located for a given seismic trace and the property estimation is based on counting models in that singular bin. The present invention allows boxes based on the seismic data to be defined in the model space based on the probabilistic analysis from which the property estimation is done by counting models in the boxes.

Seismic imaging of the subsurface is used to identify potential hydrocarbon reservoirs. Seismic data is acquired at a surface (e.g. the earth's surface, ocean's surface, or at the ocean bottom) as seismic traces which collectively make up the seismic dataset. The seismic dataset may be processed and imaged via a pre-stack method in order to analyze the seismic amplitude versus angle (AVA) or offset (AVO). Seismic surveys, generally called a baseline survey and one or more monitor surveys, conducted at different times (months or years apart) are used to monitor changes in the subsurface and are processed and imaged to create images that will show differences in seismic amplitudes.

The present invention includes embodiments of a method and system for assessing changes in reservoir properties over a period of time in a subterranean reservoir to determine the probability of hydrocarbons remaining after production and/or injection, in some embodiments estimating the probability of various saturation changes and/or pressure changes. Saturation changes are used to describe fluid changes in reservoirs that contain more than one type of fluid or gas or reservoirs where one fluid is partially replacing another as a result of hydrocarbon production or injection. Reservoir properties may include at least one of pore fluid content, porosity, brine composition, hydrocarbon composition, pressure, temperature, or any combination thereof. Determining the most probable changes in reservoir properties in a geologic feature and a range of possible changes in reservoir properties allows strategic planning around budgetary planning, obtaining mineral and lease rights, signing well commitments, permitting rig locations, designing well paths and drilling strategy, preventing subsurface integrity issues by planning proper casing and cementation strategies, selecting and purchasing appropriate completion and production equipment, and enhanced production strategies such as water or steam injection, as well as ultimately drilling into an optimum location to produce the hydrocarbons.

FIG. 1illustrates a flowchart of a method100for time-lapse geologic analysis of a subsurface volume of interest. At operation10, at least two seismic datasets that were recorded at different times (i.e., baseline and monitor datasets), generally months or years apart, are received. The earlier seismic dataset is generally referred to as the baseline dataset and the subsequent datasets are monitor datasets. As previously described, a seismic dataset includes a plurality of traces recorded at a plurality of seismic sensors. Due to changes in the reservoir properties caused by hydrocarbon production and/or injection, the seismic responses recorded at the two different times will be different in affected areas.

Method100moves on to process the seismic datasets11using substantially similar processing flows to create digital seismic images. These datasets may be subjected to a number of seismic processing steps, such as deghosting, multiple removal, spectral shaping, and the like, before undergoing a pre-stack seismic imaging process. These examples are not meant to be limiting. Those of skill in the art will appreciate that there are a number of useful seismic processing steps that may be applied to seismic data. The processing should preserve the seismic signal and reduce noise. The resultant digital seismic images may be, for example, a pre-stack seismic image, one or more seismic angle stacks, or one or more digital seismic horizon amplitude maps. The seismic horizon amplitude maps may have been computed at a series of angles (or summation of adjacent angles) in place of migrated seismic gathers. The seismic amplitude maps are computed by extracting the seismic amplitude from the migrated seismic gathers (either exact amplitude, or a computation of seismic amplitude at times around the horizon computed as average, absolute, rms, maximum, minimum, or other computational method) at the interpreted horizon time. The seismic horizons may be represented in time or depth, being optionally flattened. As is known, flattening of seismic data is used to remove the influence of geological processes such as folding and faulting in one or more the lithological interfaces from the data, enabling images produced from the seismic data to be processed into horizontal layers, e.g., for easier interpretation. The flattening of seismic data is an optional step. The seismic image and seismic horizons may be two-dimensional (2-D) (e.g., a horizontal dimension “x” and a time or depth dimension “z”) or three-dimensional (3-D) data sets (e.g., two perpendicular horizontal dimensions “x” and “y” and a time or depth dimension “z”). In some embodiments, the seismic horizon may be representative of the top of a hydrocarbon reservoir (top sand) and/or the base of the hydrocarbon reservoir (base sand).

At operation12, the seismic images are interpreted to identify at least one spatial area on a seismic horizon that has differing amplitudes between the two seismic images. The seismic horizon should be representative of the reservoir that is being monitored. An example of this can be seen inFIG. 3.FIG. 3shows map-view panels of a seismic horizon from a first time30and the same seismic horizon from a second time32. In order to create these map views30and32, a full range of seismic amplitude data has been stacked, which in this example embodiment is seismic amplitude data between angles 4° and 60°, as part of a data preprocessing step. The map indicates different regions of varying seismic amplitudes (indicated in differing shades) mostly correlating with the distribution of lithology, as well as liquids and gas, e.g., hydrocarbons. Interpretation of the seismic horizons shows that most of the amplitudes do not change between the two surveys. In one spatial area33, the amplitudes do change, so this area is selected for analysis by the rest of method100. The amplitude difference in this area is calculated at operation13. In an embodiment, one or more areas of interest are identified on the seismic horizons. In an embodiment using 3-D data, the areas of interest may be identified on a map view of the one or more seismic horizons, e.g., as polygons, wherein the map view may be colored (or shaded or contoured) to indicate the seismic amplitudes along the particular horizon.

At operation14, a probabilistic analysis is performed for the seismic amplitude versus angle (AVA) responses in at least one spatial area identified in the seismic image on at least one seismic horizon. An example of a method for doing this probabilistic analysis is shown inFIG. 2as method200. This method may include, for example, using the method of US 2016/0209531, System and Method for Analyzing Geologic Features Using Seismic Data, which is incorporated herein in its entirety. A pre-stack seismic image contains multiple seismic horizons that represent seismic events identified or selected, in an embodiment, by a user as being of interest. These seismic horizons may represent a single thin lithology, such as a sand layer or a shale layer, or an interface within one or between two or more lithologies.

In some embodiments, each area of interest may encase a large number of seismic trace locations. In terms of the present disclosure, it is important to include a sufficient number of seismic trace locations (resulting in a sufficient number of seismic traces or data sets to be processed) thereby to ensure statistical stability of the resulting AVA curves. By way of example and not limitation, a sufficient number of seismic trace locations may be on the order of thousands of trace locations.

The statistical data ranges are influenced and determined by a range of geology enclosed in the selected area of interest (i.e. polygon) and noise. The range of geology may include, for example, changes in thickness, porosity, grain size, cementation, mineralogical composition, or the like. Statistical stability of the data is ensured by making the area of interest (polygon) sufficiently large to ensure that the noise is averaged out, as well as large enough to contain a representative sampling of the geology.

Referring again toFIG. 1, in operation14statistical data ranges are computed for the seismic amplitudes in each of the areas of interest, shown in the example ofFIG. 4. These computations and calculations may be performed by reading seismic angle gathers, i.e. all of the seismic traces at a particular angle for an area of interest, identifying a time gate centered on the seismic horizon, and computing the aggregated amplitudes at each angle. The time gate has the effect of isolating a portion of each selected trace around a feature of interest in time. This process of computing the statistical data ranges for the seismic amplitudes in each of the areas of interest is computationally expensive.

A person skilled in the art would appreciate that the computation and calculations of statistical data ranges can be performed using pre-stack seismic data in depth coordinates, rather than time coordinates, and identifying a depth gate centered on the seismic horizon.

In terms of the present method it is advantageous to calculate the probability of various seismic amplitudes within the area of interest, thereby allowing the statistical data ranges of seismic amplitudes to be determined. In some embodiments, the statistical data ranges may be represented by P50 and an upper and a lower probabilistic value for seismic amplitudes, each of the upper and lower values being similarly offset from the P50 value. For example, the upper and lower probabilistic values may respectively be selected as a P10 and a P90 probabilistic value, a P20 and a P80 probabilistic value, a P30 and a P70 probabilistic value, or the like. These values are provided by way of example only and are not meant to be limiting.

Typically, the P50 probabilistic value represents the underlying signal, while the upper and lower probabilistic values are indicative of a probabilistic range which represents the variable geology and/or noise. A variety of statistics may be computed from the aggregated seismic amplitudes, i.e. in addition, or alternatively, to the probabilistic values mentioned above. For example, the statistical data ranges may include one or more of an average or mean (such as an average absolute amplitude), a mode, or a standard deviation such as RMS amplitude. It will be appreciated that other statistical measures may also be used. The use of many seismic trace locations from the areas of interest may assist in obtaining statistically significant data, in that the data may be more stable and distinct.

In addition, in another embodiment, angle stacks may be created by summing the seismic traces for each time or depth sample at two or more angles, e.g., adjacent angles. The angle stacks may be narrow, summing over a few adjacent angles, or broad, summing over many angles such as 10°-20°. Additionally, the ranges of angles summed over may overlap (e.g., 10°-20° and 15°-25°). A normalization based on the number of traces summed may be used in order to obtain an optimum presentation of the results. In other words, these narrow angle stacks may in some instances stabilize the trend of the AVA curves produced. It will however be appreciated that in many cases there may be no need for this type of stacking. As an alternative to using the AVA responses at particular angles or angle stacks, the statistical data ranges may be based on other criteria such as the gradient or rate of change of the seismic amplitude response with angle or other industry-recognized measurements in the field (e.g., fanfar, grenv).

FIG. 2shows an embodiment of a method200for performing operation14ofFIG. 1. Operation20receives the baseline, monitor, amplitude difference datasets from the previous operations of method100. Operation21of method200determines possible ranges of geological and geophysical parameters expected in the reservoir zone being analyzed that affect the seismic amplitude versus angle response. The expected ranges of geological and geophysical parameters are determined by the user based on nearby known information (e.g., previously drilled wells), estimated from theoretical equations, or other such information sources to provide results which may best characterize the expected geological and geophysical parameters expected in the reservoir zone. Geophysical parameters may include elastic properties such as P-wave velocity (Vp), S-wave velocity (Vs), and density. Geological parameters may include brine composition, hydrocarbon composition, pressure, temperature, porosity, reservoir thickness, mineralogical composition, and other factors. These determinations may be done by regional analysis, geologic inference or analogs, petrophysical analysis from analog well logs, or other means. In one embodiment, those of skill in the art will be aware that there are a number of ways of determining reasonable ranges of geological and geophysical parameters for a particular subterranean volume.

Geological parameters may be determined, for example, for a situation in which there is advance knowledge of the deposition environment of the material. In this case, that knowledge may allow the user to determine information regarding what types of materials are likely to be present as well as what relationship various layers are likely to have. By way of example, an eolian deposition environment would tend to include sandstones that are relatively free of clay and relatively well-sorted. In contrast, deltaic sandstones would tend to be higher in clay content. In order to render the hypothetical physical properties more relevant to the analysis of the acquired seismic data, the types of sandstone generated would depend, at least in part, on whether the region under investigation includes wind-deposited or river delta deposited material and could be further differentiated based on specifics of the deposition environment. Geophysical parameters may be determined, for example, where there is local information available, such as from well cores or well logs from nearby wells.

At operation22, the seismic amplitude versus angle (AVA) responses are calculated in at least one spatial area for each of the baseline, monitor, and amplitude difference datasets. This may be done, for example, using the method of US 2016/0209531, System and Method for Analyzing Geologic Features Using Seismic Data, which is incorporated herein in its entirety. A pre-stack seismic image contains multiple seismic horizons that represent seismic events identified or selected, in an embodiment, by a user as being of interest. These seismic horizons may represent a single thin lithology, such as a sand layer or a shale layer, or an interface within one or between two or more lithologies. At operation24, AVA probabilities are calculated for the baseline, monitor, and amplitude difference AVA responses. These computations and calculations may be performed by reading seismic angle gathers, i.e. all of the seismic traces at a particular angle for an area of interest, identifying a time gate centered on the seismic horizon, and computing the aggregated amplitudes at each angle. The time gate has the effect of isolating a portion of each selected trace around a feature of interest in time. This process of computing the statistical data ranges for the seismic amplitudes in each of the areas of interest is computationally expensive. A person skilled in the art would appreciate that the computation and calculations of statistical data ranges can be performed using pre-stack seismic data in depth coordinates, rather than time coordinates, and identifying a depth gate centered on the seismic horizon.

In terms of the present method it is advantageous to calculate the probability of various seismic amplitudes within the area of interest, thereby allowing the statistical data ranges of seismic amplitudes for each of the datasets to be determined. In some embodiments, the statistical data ranges may be represented by P50 and an upper and a lower probabilistic value for seismic amplitudes, each of the upper and lower values being similarly offset from the P50 value. For example, the upper and lower probabilistic values may respectively be selected as a P10 and a P90 probabilistic value, a P20 and a P80 probabilistic value, a P30 and a P70 probabilistic value, or the like. These values are provided by way of example only and are not meant to be limiting.

Once the ranges of possible geological and geophysical parameters are determined, operation23proceeds to perform a full range of 2-layer or 3-layer forward modeling with all combinations of the geological and geophysical parameters. This may be done, for example, using a method such as that described in U.S. Pat. No. 7,869,955, Subsurface Prediction Method and System, which is incorporated herein in its entirety. By way of example and not limitation, pseudo-wells including multiple types of synthetic well logs may be generated. Pseudo-wells may include physical properties such as Vp, Vs, density, porosity, shale volume (Vshale), saturation, pore fluid type or other properties. In an embodiment, seismic models for the reservoir response first at conditions represented by the first seismic survey and then at a range of conditions representing expected changes in the reservoir properties that encompass the expected or measured properties represented by the time of the second seismic survey. These properties can be fluid saturation (brine, oil, gas), pressure, temperature, etc. These property changes should be represented by a number of discrete changes. In an embodiment, this may be a small number of discrete changes such as 2-5. The modeling of the reservoir at the initial state may include variations in reservoir thickness, porosity, and other properties. Using the forward modeling, this operation may also construct a series of results of the amplitude difference between the reservoir properties corresponding to the first (i.e., baseline) seismic data set and the suspected discrete parameter changes represented by the second (i.e., monitor) seismic data set. Once the synthetic AVA responses have been calculated at operation23, the AVA probabilities are calculated at operation25.

The pseudo-wells may be generated using a partially random approach. Rather than using a simple stochastic approach, in which any particular physical model is equally likely, the generation of the pseudo-wells may be constrained by physical constraints. The constraining may take place prior to the generating, or alternately, purely stochastic pseudo-wells may be later constrained (e.g., by eliminating wells having characteristics outside the constraints). As will be appreciated, it is likely to be more efficient to first constrain, then generate, the wells, but either approach should be considered to be within the scope of the present invention.

The forward modeling of operation23will produce modeled (i.e. synthetic) seismic gathers containing AVA effects for the various combinations of geological and geophysical parameters. Forward modeling may be done, for example, using some form of the Zoeppritz equation, full waveform modeling, or other such seismic modeling method that may be appropriate including that explained by U.S. Pat. No. 7,869,955. Then at operation25, these synthetic seismic gathers are used to calculate the probability of various seismic amplitudes within the area of interest, thereby allowing the statistical data ranges of seismic amplitudes to be determined. For example,FIG. 4shows AVA curves for three different fluid contents (brine/wet, fizz, and gas), including the P50 values (the triangle, star, and square symbols) with range bars indicating the P20-P80 ranges. Fizz is generally considered to be a low saturation, non-commercial amount of hydrocarbon gas (1% to 15% gas saturation) contained in the rock pores along with formation water. The seismic amplitude responses should be determined separately for brine, low and high hydrocarbon saturation, and different hydrocarbon fluids. The measured response ranges may also be segregated by different geological assessment of the mineralogical composition of the reservoir and non-reservoir rocks (i.e. facies) simulated in the forward modeling step. Other examples of the forward modeled responses can be seen inFIG. 5. InFIG. 5, the different grayscale dots indicate amplitudes as very-far-stack vs. near-stack for different fluid contents. Boxes defining the baseline amplitudes (amplitudes at the earlier time), monitor amplitudes (amplitudes at the later time), and difference amplitudes are based on the AVA probabilities calculated in operation24, calculated from the input digital seismic images, are shown.FIG. 6shows a similar plot of the baseline and monitor boxes but the forward modeled results have been simplified to the modeled fluid vector rather than the grayscale dots. To one skilled in the art, it would obvious that instead of defining a box around the P50 amplitudes at each measured angle to represent the range of possible models, one could also use an ellipse or other such shape to represent the spatial distribution of the data about the central value. Alternatively, a mathematical distribution characterizing the distribution of the data around the P50 amplitude could be estimated and used from operation24and forward in the analysis. Moreover, althoughFIG. 6shows the box in two dimensions, the box (or ellipse or mathematical distribution) may be multi-dimensional. For example, if statistical data ranges are found for four different angles, the box would have four dimensions.

Method200can now proceed to operation26, estimating the probability for changes in pore fluid saturation based on comparison of the calculated AVA probabilities from operation24and the calculated modeled AVA probabilities from operation25. This estimation is done by comparing the amplitude difference AVA responses and the baseline and monitor AVA responses. By way of example, this may be done by using a two-box or three-box test, to estimate the change in reservoir properties in each polygon separately by considering the difference in the measured seismic amplitude versus angle responses between the first (baseline) and second (monitor) seismic survey within a single spatial polygon. This may be done by first determining the number of forward model responses which represent a reservoir in the initial state of the time of the first (i.e., baseline) seismic survey that have responses which fit into a box centered on the P50 response at each measured parameter and an extent determined by statistical measurements (e.g., P20-P80, standard deviation, etc.). The successful seismic models must have a calculated response which fits all of the measured response ranges. Next determine from this sub-class of forward model responses, those models which have a calculated response in the box centered on the P50 response from the monitor seismic survey at each measured parameter and an extent determined by statistical measurements (e.g., P20-P80, standard deviation, etc.). Next determine from this smaller sub-class of forward model responses, those models which have a calculated response in the box centered on the P50 response from the amplitude difference (baseline minus monitor or vice versa, as long as the order of subtraction is the same for the recorded seismic dataset sand the modeled/synthetic seismic datasets) at each measured parameter and an extent determined by statistical measurements (e.g., P20-P80, standard deviation, etc.). Any two or all 3 of the tests above can be used to determine the final subset of successful forward models. When doing only a two-box test, one of the tests should be using the amplitude difference data set. Analyze the total number of successful forward model responses which fit either the two or three tests used. The probability of each reservoir property change (e.g. saturation case) at the time of the second (i.e., monitor) seismic survey according to this hypothesis is the number of successful responses for that saturation case divided by the total number of successful responses.

Referring again to method100ofFIG. 1, at operation15the result of method200can be used to estimate changes in reservoir properties. Seismic models can be used to estimate other reservoir properties. The estimated reservoir properties may include pore fluid content, porosity, brine composition, hydrocarbon composition, pressure, temperature, or any combination thereof. These estimated reservoir properties are estimates of the average geology in the spatial area of interest. Reservoir properties such as porosity, thickness, and Vshale can be estimated from these seismic models. Statistical measurements can be computed and summarized.

Although the embodiments above describe a method based on seismic horizons, a similar method may be used to allow interval analysis, which would include a probabilistic analysis of the reservoir thickness as well as other reservoir properties. This may be done by including a seismic attribute for an interval. For example, time thickness is a candidate for the seismic attribute, or average amplitude for an interval. Time thickness could be used as a seismic attribute to assure that the model and seismic thickness are broadly matched. Alternatively, time thickness for the seismic traces could be used to adjust the priors for the reservoir thickness of the models (greater than tuning thickness, less than tuning thickness, a mixture). Average amplitude for an interval could be useful for estimation of NTG (net to gross).

FIG. 7is an example of the steps of method100using method200. The structure maps show the baseline map71A and the monitor map71B. Diagram72shows AVA probability curves created at operation24of method200. Diagram73shows the AVA curves for the baseline seismic data, the monitor seismic data, and the amplitude difference between the baseline and monitor seismic data with angles selected for use in subsequent steps of method200. Diagram74shows a box for known baseline oil, meaning that at the time of the baseline survey, the reservoir contained oil. Seismic models generated by method200at operations23and25. Diagram75shows the three-box test described above. Diagram76shows the probabilities calculated as a result of method100. Although these results are displayed graphically, this is not meant to be limiting. Other methods of presenting the results, such as in a spreadsheet format, are possible.

FIG. 8is a block diagram illustrating a time-lapse reservoir property assessment system500, in accordance with some embodiments. While certain specific features are illustrated, those skilled in the art will appreciate from the present disclosure that various other features have not been illustrated for the sake of brevity and so as not to obscure more pertinent aspects of the embodiments disclosed herein.

To that end, the reservoir property assessment system500includes one or more processing units (CPUs)502, one or more network interfaces508and/or other communications interfaces503, memory506, and one or more communication buses504for interconnecting these and various other components. The reservoir property assessment system500also includes a user interface505(e.g., a display505-1and an input device505-2). The communication buses504may include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Memory506includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory506may optionally include one or more storage devices remotely located from the CPUs502. Memory506, including the non-volatile and volatile memory devices within memory506, comprises a non-transitory computer readable storage medium and may store seismic data, velocity models, seismic images, and/or geologic structure information.

In some embodiments, memory506or the non-transitory computer readable storage medium of memory506stores the following programs, modules and data structures, or a subset thereof including an operating system516, a network communication module518, and a reservoir property module520.

The operating system516includes procedures for handling various basic system services and for performing hardware dependent tasks.

The network communication module518facilitates communication with other devices via the communication network interfaces508(wired or wireless) and one or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks, and so on.

In some embodiments, the time-lapse module520executes the operations of method100. Time-lapse module520may include data sub-module525, which handles the seismic image including seismic gathers525-1through525-N. This seismic data is supplied by data sub-module525to other sub-modules.

AVA (amplitude versus angle) sub-module522contains a set of instructions522-1and accepts metadata and parameters522-2that will enable it to execute operation11,12,13, and parts of14of method100. The forward modeling function sub-module523contains a set of instructions523-1and accepts metadata and parameters523-2that will enable it to execute parts of operation14of method100. The fluid content sub-module524contains a set of instructions524-1and accepts metadata and parameters524-2that will enable it to execute at least operation15of method100. Although specific operations have been identified for the sub-modules discussed herein, this is not meant to be limiting. Each sub-module may be configured to execute operations identified as being a part of other sub-modules, and may contain other instructions, metadata, and parameters that allow it to execute other operations of use in processing seismic data and generate the seismic image. For example, any of the sub-modules may optionally be able to generate a display that would be sent to and shown on the user interface display505-1. In addition, any of the seismic data or processed seismic data products may be transmitted via the communication interface(s)503or the network interface508and may be stored in memory506.

Method100is, optionally, governed by instructions that are stored in computer memory or a non-transitory computer readable storage medium (e.g., memory506inFIG. 8) and are executed by one or more processors (e.g., processors502) of one or more computer systems. The computer readable storage medium may include a magnetic or optical disk storage device, solid state storage devices such as flash memory, or other non-volatile memory device or devices. The computer readable instructions stored on the computer readable storage medium may include one or more of: source code, assembly language code, object code, or another instruction format that is interpreted by one or more processors. In various embodiments, some operations in each method may be combined and/or the order of some operations may be changed from the order shown in the figures. For ease of explanation, method100is described as being performed by a computer system, although in some embodiments, various operations of method100are distributed across separate computer systems.