Patent Grant
10746017
This disclosure relates to determining one or more dynamic processes for a reservoir in a geological formation occurring over geological time and reservoir characterization.
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as an admission of any kind.
Reservoir fluid analysis may be used to better understand a hydrocarbon reservoir in a geological formation. Indeed, reservoir fluid analysis may be used to measure and model fluid properties within the reservoir to determine a quantity and/or quality of formation fluids—such as liquid and/or gas hydrocarbons, condensates (e.g., gas condensates), formation water, drilling muds, and so forth—that may provide much useful information about the reservoir. This may allow operators to better assess the economic value of the reservoir, obtain reservoir development plans, and identify hydrocarbon production concerns for the reservoir. Numerous possible reservoir models may be used to describe the reservoir. For a given reservoir, however, different possible reservoir models may have varying degrees of accuracy. The accuracy of the reservoir model may impact plans for future well operations, such as enhanced oil recovery, logging operations, and dynamic formation analyses. As such, the more accurate the reservoir model, the greater the likely value of future well operations to the operators producing hydrocarbons from the reservoir.
This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the subject matter described herein, nor is it intended to be used as an aid in limiting the scope of the subject matter described herein. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below.
In one example, a method includes receiving first fluid property data from a first location in a hydrocarbon reservoir and receiving second fluid property data from a second location in the hydrocarbon reservoir. The method includes performing a plurality of realizations of models of the hydrocarbon reservoir according to a respective plurality of one or more plausible dynamic processes to generate one or more respective modeled fluid properties. The method includes selecting the one or more plausible dynamic processes based at least in part on a relationship between the first fluid property data, the second fluid property data, and the modeled fluid properties obtained from the realizations to identify potential disequilibrium in the hydrocarbon reservoir.
In another example, a method includes acquiring well logs using a well-logging device in a wellbore in a geological formation, wherein the wellbore or the geological formation, or both, contain a reservoir fluid. The method includes performing downhole fluid analysis using a downhole acquisition tool in the wellbore to determine a plurality of fluid properties associated with the reservoir fluid. The method includes generating a first fluid geodynamic model representative of the plurality of fluid properties based on the downhole fluid analysis. The method includes generating a second fluid geodynamic model based on the first fluid geodynamic model and the well logs.
In another example, a system includes a downhole acquisition tool comprising a plurality of sensors configured to measure fluid properties of a reservoir fluid within a geological formation of a hydrocarbon reservoir. The system includes a data processing system configured to predict one or more dynamic processes from a plurality of dynamic processes that depend at least in part on the measured fluid properties; wherein the data processing system comprises one or more tangible, non-transitory, machine-readable media comprising instructions. The instructions are configured to identify plausible dynamic processes from the plurality of dynamic processes. The instructions are configured to utilize models of the plausible dynamic processes to determine at least one likely realization scenario.
Various refinements of the features noted above may be undertaken in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination. The brief summary presented above is intended to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter.
Various aspects of this disclosure may be better understood upon reading the following detailed description and upon reference to the drawings in which:
One or more specific embodiments of the present disclosure will be described below. These described embodiments are examples of the presently disclosed techniques. Additionally, in an effort to provide a concise description of these embodiments, features of an actual implementation may not be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another. Moreover, it should be appreciated that such a development effort might be complex and time consuming, but would still be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure.
When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Additionally, it should be understood that references to “one embodiment” or “an embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.
The present disclosure relates to systems and methods for reservoir characterization and reservoir modeling, including identification of particular realization scenarios. Acquisition and analysis representative of formation fluids downhole in delayed or real time may be used in reservoir modeling. A reservoir model based on downhole fluid analysis may predict or explain reservoir characteristics such as, but not limited to, connectivity, productivity, lifecycle stages, type and timing of hydrocarbon, hydrocarbon contamination, reservoir fluid dynamics, composition, and phase. Over the life of the reservoir, reservoir fluids such as oil, gas, condensates may behave dynamically in the reservoir. This may result in spatial variations in the reservoir fluids throughout the reservoir, which may appear as fluid gradients in the composition characteristics of the reservoir fluids. For example, a concentration of compositional components of the reservoir fluid (e.g., gas, condensates, asphaltenes, etc.) may or may not vary along a vertical depth of the reservoir.
Different realization scenarios may be used to model the reservoir. In particular, realizations of equation of state (EOS) models that represent the fluid behavior of the reservoir fluids associated with dynamic processes may be used to predict how a fluid composition gradient may respond to various dynamic processes within the reservoir. Some EOS models are described in U.S. Pat. No. 8,271,248, which is assigned to Schlumberger Technology Corporation and is hereby incorporated by reference in its entirety for all purposes. The EOS model may include cubic equilibrium EOS models, the Flory-Huggins-Zuo (FHZ) equation, and/or dynamic EOS models, which include the FHZ model and a diffusive or convection model associated with the realization scenario (e.g., biodegradation, gas diffusion, convective currents, flow barriers/obstructions, pressure driven oil or gas flow, thermochemical sulfate reduction reactions, etc.). The equilibrium and dynamic EOS models may predict fluid interactions (e.g., gas-to-liquid and solid-to-liquid interactions) and compositions of the reservoir fluids through the reservoir by modeling factors such as, for example, gas-to-oil ratio (GOR), condensate-gas ratio (CGR), density, volumetric factors and compressibility, heat capacity, and saturation pressure.
The reservoir models that may most likely accurately describe the reservoir may be based on certain particular realization scenarios. There may be a wide range of possible realization scenarios, so the most plausible realization scenarios from among these may be selected. For example, by combining measured fluid gradients from the downhole acquisition tool with empirical historical data relating to reservoirs where the realization scenario is known, the more plausible realization scenarios likely to be occurring within the reservoir may be determined. Understanding the dynamic processes affecting a particular reservoir may facilitate reservoir planning development and selecting appropriate enhanced oil recovery techniques to increase reservoir productivity.
It may be appreciated that the reservoir may be further understood with via downhole analysis. Downhole analysis may provide quantitative information of geological boundaries, 3D orientation of strata intersecting a wellbore, faults, fractures, rock composition, fluid content, etc. For example, borehole image logs may be used to provide information associated with the formation geometry and identify zone of interest within the reservoir. Additionally, the borehole image logs may identify sedimentary deposits that may impact reservoir productivity. For example, over the life of the reservoir, sedimentary deposits (e.g., turbidites) may form that may decrease reservoir productivity. For example, certain sedimentary deposits may decrease the permeability of fluid channels within the reservoir, thereby changing the reservoir's connectivity such that the reservoir fluids are unable to flow into wellbores for extraction
As discussed above, the spatial variations (e.g., fluid gradients) in a composition of the reservoir fluids may change over time, and may also decrease the reservoir's productivity (e.g., change reservoir connectivity). For example, a concentration of components of the reservoir fluid (e.g., gas, liquid hydrocarbons, asphaltenes, etc.) may vary along a vertical depth of the reservoir. The variation or lack of variation in the concentration of these components may indicate that the reservoir is in disequilibrium or equilibrium. In the case of disequilibrium, the reservoir may be understood to be undergoing—albeit over geologic time—one or more dynamic processes known as realization scenarios. In the case of equilibrium, the reservoir may be understood to have undergone one or more realization scenarios to achieve equilibrium. In either case, the realization scenarios may explain reservoir features that affect reservoir productivity by decreasing reservoir permeability due, in part, to the formation of tar mats and or bitumen deposits within the reservoir. Downhole fluid analysis (DFA) may be used to evaluate fluid behaviors (e.g., by identifying spatial variations) in reservoirs. Data generated from the DFA and/or data from additional sources, may be used to identify the realization scenario that may be causing or have caused fluid gradients or a lack of fluid gradients within the reservoir. By way of example, some realization scenarios that may enable fluid gradients within the reservoir include biodegradation, continuous and/or discontinuous gas diffusion (e.g., gas and/or carbon dioxide (CO2)), fault block migration, subsidence, convective currents, combinations of these, or any other suitable realization scenarios. In essence, the DFA data may be used to shed light on gross-scale reservoir architecture.
This gross-scale reservoir architecture may be further refined with other well logging information. Indeed, the DFA data and/or data from additional sources (e.g., borehole image logs) may be used for reservoir exploration and development, such as, but not limited to, reservoir delineation (e.g., boundaries), connectivity, fluid equilibrium, and identification of dynamic processes affecting reservoir productivity and/or connectivity. The DFA and borehole image logs may be used as inputs for reservoir modeling systems (e.g., geological process models, petroleum systems models, and/or reservoir fluid geodynamics models) to identify the geological setting and fluid distribution of the reservoir, and refine the gross-scale reservoir architecture to generate a fine-scale reservoir architecture. The fine-scale reservoir architecture may provide reservoir details that may not be resolved in the gross-scale reservoir architecture. The DFA and borehole image logs may be compared to reservoir modeling systems (e.g., geological process models, petroleum system models, and/or reservoir fluid geodynamics models) to further constrain geological and reservoir elements for exploration and production of the reservoir. The information generated by analyzing the reservoir architecture may be used to identify areas of low permeability, such as areas containing baffles. As such, operators may increase productivity of a reservoir of interest.
Drilling fluid or mud 32 (e.g., oil base mud (OBM)) is stored in a pit 34 formed at the well site. A pump 36 delivers the drilling fluid 32 to the interior of the drill string 16 via a port in the swivel 30, inducing the drilling mud 32 to flow downwardly through the drill string 16 as indicated by a directional arrow 38. The drilling fluid exits the drill string 16 via ports in the drill bit 18, and then circulates upwardly through the region between the outside of the drill string 16 and the wall of the wellbore 14, called the annulus, as indicated by directional arrows 40. The drilling mud 32 lubricates the drill bit 18 and carries formation cuttings up to the surface as it is returned to the pit 34 for recirculation.
The downhole acquisition tool 12, sometimes referred to as a bottom hole assembly (“BHA”), may be positioned near the drill bit 18 and includes various components with capabilities, such as measuring, processing, and storing information, as well as communicating with the surface. A telemetry device (not shown) also may be provided for communicating with a surface unit (not shown). As should be noted, the downhole acquisition tool 12 may be conveyed on wired drill pipe, a combination of wired drill pipe and wireline, or other suitable types of conveyance.
In certain embodiments, the drilling acquisition tool 12 includes a downhole fluid analysis system. For example, the downhole acquisition tool 12 may include a sampling system 42 including a fluid communication module 46 and a sampling module 48. The modules may be housed in a drill collar for performing various formation evaluation functions, such as pressure testing and fluid sampling, among others. As shown in
The downhole acquisition tool 12 may evaluate fluid properties of reservoir fluid 50. Accordingly, the sampling system 42 may include sensors that may measure fluid properties such as gas-to-oil ratio (GOR), mass density, optical density (OD), asphaltene content, composition of carbon dioxide (CO2), C1, C2, C3, C4, C5, and C6+, formation volume factor, viscosity, resistivity, fluorescence, and combinations thereof of the reservoir fluid 50. The fluid communication module 46 includes a probe 60, which may be positioned in a stabilizer blade or rib 62. The probe 60 includes one or more inlets for receiving the formation fluid 52 and one or more flow lines (not shown) extending into the downhole acquisition tool 12 for passing fluids (e.g., the reservoir fluid 50) through the tool. In certain embodiments, the probe 60 may include a single inlet designed to direct the reservoir fluid 50 into a flowline within the downhole acquisition tool 12. Further, in other embodiments, the probe 60 may include multiple inlets that may, for example, be used for focused sampling. In these embodiments, the probe 60 may be connected to a sampling flow line, as well as to guard flow lines. The probe 60 may be movable between extended and retracted positions for selectively engaging the wellbore wall 58 of the wellbore 14 and acquiring fluid samples from the geological formation 20. One or more setting pistons 64 may be provided to assist in positioning the fluid communication device against the wellbore wall 58.
In certain embodiments, the downhole acquisition tool 12 includes a logging while drilling (LWD) module 68. The module 68 includes a radiation source that emits radiation (e.g., gamma rays) into the formation 20 to determine formation properties such as, e.g., lithology, density, formation geometry, reservoir boundaries, among others. The gamma rays interact with the formation through Compton scattering, which may attenuate the gamma rays. Sensors within the module 68 may detect the scattered gamma rays and determine the geological characteristics of the formation 20 based on the attenuated gamma rays.
The sensors within the downhole acquisition tool 12 may collect and transmit data 70 (e.g., log and/or DFA data) associated with the characteristics of the formation 20 and/or the fluid properties and the composition of the reservoir fluid 50 to a control and data acquisition system 72 at surface 74, where the data 70 may be stored and processed in a data processing system 76 of the control and data acquisition system 72.
The data processing system 76 may include a processor 78, memory 80, storage 82, and/or display 84. The memory 80 may include one or more tangible, non-transitory, machine readable media collectively storing one or more sets of instructions for operating the downhole acquisition tool 12, determining formation characteristics (e.g., geometry, connectivity, etc.) calculating and estimating fluid properties of the reservoir fluid 50, modeling the fluid behaviors using, e.g., equation of state models (EOS), and identifying dynamic processes within the reservoir that may be associated with observed fluid behaviors. The memory 80 may store reservoir modeling systems (e.g., geological process models, petroleum systems models, reservoir dynamics models, etc.), mixing rules and models associated with compositional characteristics of the reservoir fluid 50, equation of state (EOS) models for equilibrium and dynamic fluid behaviors, reservoir realization scenarios, (e.g., biodegradation, gas/condensate charge into oil, CO2 charge into oil, fault block migration/subsidence, convective currents, among others), and any other information that may be used to determine geological and fluid characteristics of the formation 20 and reservoir fluid 50, respectively. In certain embodiments, the data processing system 76 may apply filters to remove noise from the data 70.
To process the data 70, the processor 78 may execute instructions stored in the memory 80 and/or storage 82. For example, the instructions may cause the processor to compare the data 70 (e.g., from the logging while drilling and/or downhole fluid analysis) with known reservoir properties estimated using the reservoir modeling systems, use the data 70 as inputs for the reservoir modeling systems, and identify geological and reservoir fluid parameters that may be used for exploration and production of the reservoir. As such, the memory 80 and/or storage 82 of the data processing system 76 may be any suitable article of manufacture that can store the instructions. By way of example, the memory 80 and/or the storage 82 may be ROM memory, random-access memory (RAM), flash memory, an optical storage medium, or a hard disk drive. The display 84 may be any suitable electronic display that can display information (e.g., logs, tables, cross-plots, reservoir maps, etc.) relating to properties of the well/reservoir as measured by the downhole acquisition tool 12 and plausible realization scenarios associated with the reservoir. It should be appreciated that, although the data processing system 76 is shown by way of example as being located at the surface 74, the data processing system 76 may be located in the downhole acquisition tool 12. In such embodiments, some of the data 70 may be processed and stored downhole (e.g., within the wellbore 14), while some of the data 70 may be sent to the surface 74 (e.g., in real time). In certain embodiments, the data processing system 76 may use information obtained from petroleum system modeling operations, ad hoc assertions from the operator, empirical historical data (e.g., case study reservoir data) in combination with or lieu of the data 70 to determine certain parameters of the reservoir 8.
As shown in
As discussed above, the data 70 from the downhole tool 10 may be analyzed with the equation of state (EOS) models to determine how gradients in reservoir fluid compositions are affected by various dynamic processes occurring within the reservoir 8. The dynamic processes for the reservoir 8 may include gas/condensate charge, biodegradation, convective currents, fault block migration, and subsidence, among others.
Over time, the low molecular weight aliphatic hydrocarbons (e.g., gas 184) may be expelled from the source rock and travel through a high-permeability streak in the formation to the top of the reservoir unit. As shown in the middle diagram in
As such, the asphaltenes 194 may accumulate at an oil-water-contact (OWC) 198, thereby forming the tar mat 196, as shown in the far right diagram in
Similarly, realization scenarios associated with biodegradation of hydrocarbons at the OWC 198 may increase a concentration of the asphaltenes 194 toward the bottom 192 of the reservoir 8.
A method for identifying dynamic processes for hydrocarbon reservoirs (e.g., the reservoir 8) is illustrated in flowchart 220 of
Reservoirs having fluid behaviors similar to the reservoir 8 may have similar behaviors due to similar dynamic processes. As such, the data 70 may be compared to fluid behavior information that may be obtained from PSM of the reservoir 8, the operator, and/or empirical historical data 226 to identify plausible dynamic processes for the reservoir 8 from among a range of possible dynamic processes (block 228). Indeed, as discussed above, the data 70 from the DFA may provide information regarding the gas-to-oil ratio (GOR), viscosity, density, and/or composition (e.g., asphaltene content) of the reservoir fluid at different depths (e.g., the depth) of the reservoir 8. Any changes in the measured data 70 and/or reservoir productivity from the routine sequence and behavior may indicate to the operator that the reservoir 8 may be in disequilibrium and/or one or more dynamic processes have occurred or are currently occurring. The DFA information generated from the data 70 may identify one or more gradients (e.g., viscosity gradients, density gradients, GOR gradients, asphaltene concentration gradients, etc.) in the reservoir fluid that may be associated with one or more dynamic processes (e.g., one of the dynamic processes discussed above with reference to
Following identification of the plausible dynamic processes based on the initial data 224 and empirical historical data 226, the method 220 includes modeling the one or more plausible realization scenarios associated with those dynamic processes (block 230). Each plausible realization scenario from the one or more plausible realization scenarios, identified according to block 228, may be modeled using the respective equilibrium and/or dynamic equation of state (EOS) models. By way of example, if biodegradation was identified as one of the plausible dynamic processes, the equilibrium and dynamic EOS model for biodegradation is used to model the realization scenario. Having identified the one or more plausible realization scenarios according to block 228 may increase the robustness of the method 220 compared to modeling each dynamic process from the range of dynamic processes that may or may not be affecting the reservoir 8.
The method also includes comparing the measured fluid gradients (e.g., obtained from the data 70) with the EOS models for the one or more plausible realization scenarios (block 232). By comparing (e.g., fitting) the measured fluid gradients and the EOS models, the method disclosed herein may determine if the reservoir 8 is in equilibrium or disequilibrium, and may predict the one or more dynamic processes causing the gradients based on the realization scenario EOS model that fits the data 70. For example, if the measured fluid gradient fits the equilibrium EOS, the data processing system 76 may determine that the reservoir 8 is in equilibrium. Conversely, if the measured fluid gradient does not fit the dynamic EOS, the data processing system 76 may determine that the reservoir 8 is in disequilibrium. Similarly, if the measured fluid gradient fits the EOS model for a respective realization scenario (e.g., gas diffusion, biodegradation, pressure driven oil or gas flow, thermochemical sulfate reduction reactions, etc.), the data processing system 76 may predict that the observed fluid gradient is a result of the realization scenario associated with that particular EOS model. As should be noted, the EOS models may be compared to data from other sources. For example, the EOS models may be compared to the petroleum system models for the reservoir 8, ad hoc assertions from the operator, or combinations thereof.
In certain embodiments, the one or more dynamic processes identified as likely for the reservoir 8 may be validated via geochemical analyses. The geochemical analyses may include measuring biomarker ratios known to be sensitive to identified dynamic processes. The biomarker ratios may be measured with single- or multi-dimensional gas chromatography or any other suitable analytical technique. Additionally, the geochemical analysis may include measuring asphaltene composition, which may also be used to determine certain parameters in the equation of state (EOS) models.
The combination of the data 70 from the downhole fluid analysis (DFA) and the EOS models may also provide information as to where in the reservoir 8 certain events associated with the identified one or more dynamic processes are located. For example, the depth at which the measured asphaltene content (e.g., determined via DFA) of the reservoir fluid 50 increases more than predicted by the equilibrium EOS may be the depth at which the viscosity of the reservoir fluid 50 increases precipitously, and the location where biodegradation is likely occurring. Similarly, gas diffusion (e.g., continuous or discontinuous) may result in various fluid gradients (e.g., GOR, bubble point, API gravity, and asphaltene onset pressure) that may affect reservoir productivity. The location of the gas diffusion may be located at depths where the gas content (e.g., GOR determined from DFA) is higher and the asphaltene content (e.g., measured using DFA) is lower than predicted by the equilibrium EOS. As described in further detail below, knowing the location of the events (e.g., dynamic processes) may facilitate oil recovery and reservoir production operations.
Returning to the method of
The predicted dynamic processes within the reservoir 8 may be used to plan logging measurements that are used to characterize reservoirs and mitigate potential problems that may be associated with the reservoirs. By way of example, the information obtained from the predicted dynamic processes may provide information as to where potential problems may occur within the reservoir 8. As such, the operator may plan where in the reservoir 8 logging measurements are acquired. The logging measurements may also be used to validate the prediction of the dynamic process. For example, the logging measurements may be fitted to the predicted models employing varying realization scenarios. In certain embodiments, lab data for the reservoir 8 may be compared to the predicted realization scenario to validate and determine the accuracy of the predicted realization scenario generated from the acts of the method 220. Furthermore, the dynamic EOS models for the predicted realization scenarios may be used in the formation analyses to collect data from other reservoirs and/or wellbores within the reservoir 8 in a way that may increase the accuracy of the realization scenarios identified.
As discussed above, reservoir fluid geodynamics may be used to model dynamic fluid behaviors, and provide accurate and reliable information associated with hydrocarbon timing (e.g., age), type (e.g., light oil, heavy oil), fluid distributions (e.g., gradients), and volume of the reservoir fluid. This information may be used to identify and locate realization scenarios (e.g., dynamic processes) within a reservoir that may affect reservoir productivity. By knowing the dynamic processes affecting the reservoir productivity, operators may determine which enhance oil recovery (EOR) techniques may increase reservoir productivity rather, than choosing the EOR based on, for example, trial and error. Moreover, the information from the predicted realization scenarios may be used to develop future formation analyses for reservoir characterization, thereby decreasing costs generally associated with extensive formation analyses.
It may be appreciated the above techniques relating to identification and locating of realization scenarios affecting the reservoir 8 and its productivity may be utilized with logging and DFA information to provide an understanding of the architecture of the elements of the reservoir 8.
Borehole log (e.g., imaging, resistivity, etc.) and downhole fluid analysis (DFA) data (e.g., the data 70) obtained from the downhole acquisition tool 12 may facilitate characterization of the permeability and geometric characteristics (e.g., lateral reservoir correlations and continuity) of the sheets 150 and channels 152, 156. In addition, the logs and DFA data may provide information associated with the connectivity of the sheets 150 (e.g., whether all the sheets 150 feed into a single or multiple channels 152) and location of the leveed channels 156. This information may be used to model the reservoir 8, and facilitate planning and developing the reservoir 8 (e.g., determine location of the wellbores 14 within the reservoir).
For example, based on the borehole logs and DFA, hydrocarbon permeable regions 260 and hydrocarbon non-permeable regions 262 within the reservoir 8 may be identified with increased accuracy compared to techniques that do not use DFA. Knowing where in the reservoir 8 permeable and non-permeable regions 260, 262, respectively, are located, the operator may determine optimal locations for additional wellbores 14 within the reservoir to maximized extraction of the reservoir fluid 50. As discussed above, the leveed channels 256 may have sedimentary deposits 268 (e.g., turbidites). The sedimentary deposits 268 may form the non-permeable regions 262, thereby decreasing the productivity of a wellbore receiving the reservoir fluid 50 from the leveed channels 256, rather, than from the sheets 250 and the main channel 252.
As discussed above, the data 70 from the downhole tool 10 may be analyzed with the equation of state (EOS) models to determine how gradients in reservoir fluid compositions respond to various dynamic processes (e.g., realization scenarios) occurring within the reservoir 8. The method 300 includes acquiring well logs (block 304) of the reservoir 8 using the downhole acquisition tool 12. The well logs may provide information about the geological boundaries (e.g., where the reservoir starts and ends), three dimensional orientation of strata intersecting the wellbore 14, faults, fractures in the formation 20, rock composition and texture, fluid content (e.g., presence of water and/or liquid/gas hydrocarbon), geological facies classifications (e.g., sedimentary, metamorphic, shale facies, channel sand, levee, marine siltstone, etc.), and identification of depositional environments. In addition to the well logs, the downhole acquisition tool 12 may determine pressure and temperature parameters of the reservoir 8. The downhole acquisition tool 12 may collect data from various stations along a depth of the wellbore 14.
Following well log acquisition according to the acts of block 304, the method 300 includes performing an initial downhole fluid analysis (DFA) (block 308). The DFA analysis may provide information associated with a state of fluid equilibrium (e.g., whether the fluid is in equilibrium or non-equilibrium (e.g., undergoing a dynamic process)) and/or the connectivity of the reservoir.
It may be appreciated that realization scenarios associated with biodegradation of hydrocarbons at the OWC 198 may increase a concentration of the asphaltenes 194 toward the bottom 192 of the reservoir 8. The increased concentration of asphaltenes 194 at the bottom 192 may result in a viscosity gradient in the immature oil 182 along the depth 188 and enable formation of the tar mat 196. As such, the reservoir fluid 50 in the formation 20 may be difficult to extract, decreasing reservoir productivity. Therefore, it may be advantageous to identify the dynamic process causing the gradient within the reservoir, and determine where in the reservoir (e.g., along the depth) the dynamic processes are occurring such that appropriate treatment techniques may be used to mitigate the effects of the dynamic processes and increase reservoir productivity. In addition, by knowing the type and location of the dynamic processes occurring within the reservoir, dynamic formation analyses may be customized for development of the reservoir 8 and any other reservoirs having similar realization scenarios.
Returning to the method 300 of
As discussed above, the data 70 from the DFA may provide information regarding the gas-to-oil ratio (GOR), viscosity, density, composition (e.g., asphaltene content), and combinations thereof of the reservoir fluid 50 at different depths (e.g., the depth) of the reservoir 8. The data 70 may be compared to routine sequence and behavior information associated with the reservoir 8 that may be obtained from PSM, the operator, and empirical historical data 370. Any changes in the measured data 70 and/or reservoir productivity from the routine sequence and behavior may indicate to the operator that the reservoir 8 may be in disequilibrium and/or one or more realization scenarios have or are currently occurring. The DFA information generated from the data 70 may identify one or more gradients (e.g., viscosity gradients, density gradients, GOR gradients, asphaltene concentration gradients, etc.) in the reservoir fluid 50 that may be associated with one or more realization scenarios (e.g., the dynamic process discussed above). Once the one or more gradients have been identified, the empirical historical data from block 370 may be used to determine one or more plausible scenarios from a range of realization scenarios (block 372) that may be causing the one or more gradients.
Following identification of the one or more gradients, the method 364 includes modeling the one or more plausible realization scenarios (block 374). Each plausible realization scenario from the one or more plausible realization scenarios, identified according to block 372, may be modeled using the respective equilibrium and/or dynamic equation of state (EOS) models. By way of example, if biodegradation was identified as one of the plausible realization scenarios, the equilibrium and dynamic EOS model for biodegradation is used to model the realization scenario. Having identified the one or more plausible realization scenarios according to block 372 may increase the robustness of the method 364 compared to modeling each realization scenario from the range of realization scenarios that may or may not be affecting the reservoir 8.
The method 364 also includes comparing the measured fluid gradients (e.g., obtained from the data 70) with the EOS models (e.g., from block 374) for the one or more plausible realization scenarios (block 378). By comparing (e.g., fitting) the measured fluid gradients and the EOS models, the method 364 disclosed herein may determine if the reservoir 8 is in equilibrium or disequilibrium, and may predict the one or more realization scenario causing the gradients based on the realization scenario EOS model that fits the data 70 from block 368. For example, if the measured fluid gradient fits the equilibrium EOS, the data processing system 76 may determine that the reservoir 8 is in equilibrium. Conversely, if the measured fluid gradient fits the dynamic EOS, the data processing system 76 may determine that the reservoir 8 is in disequilibrium. Similarly, if the measured fluid gradient fits the EOS model for a respective realization scenario (e.g., gas diffusion, biodegradation, pressure driven oil or gas flows, etc.), the data processing system 76 may predict that the observed fluid gradient is a result of the realization scenario associated with that particular EOS model. As should be noted, the EOS models may be compared to data from other sources. For example, the EOS models may be compared to the petroleum system models for the reservoir 8, ad hoc assertions from the operator, or combinations thereof.
In certain embodiments, the one or more realization scenarios concluded, according to the acts of block 378, may be validated via geochemical analyses. The geochemical analyses may include measuring biomarker ratios known to be sensitive to identified realization scenarios. The biomarker ratios may be measured with single- or multi-dimensional gas chromatography or any other suitable analytical technique. Additionally, the geochemical analysis may include measuring asphaltene composition, which may also be used to determine certain parameters in the equation of state (EOS) models.
The combination of the data 70 from the downhole fluid analysis (DFA) and the EOS models may also provide information as to where in the reservoir 8 the identified one or more realization scenarios are located. For example, the depth at which the measured asphaltene content (e.g., determined via DFA) of the reservoir fluid 50 increases more than predicted by the equilibrium EOS may be the depth at which the viscosity of the reservoir fluid 50 increases precipitously, and the location where a biodegradation realization scenario is likely occurring. Similarly, gas diffusion (e.g., continuous or discontinuous) may result in various fluid gradients (e.g., GOR, bubble point, gravity, and asphaltene onset pressure) that may affect reservoir productivity. The location of the gas diffusion may be located at depths where the gas content (e.g., GOR determined from DFA) is higher and the asphaltene content (e.g., measured using DFA) is lower than predicted by the equilibrium EOS. As described in further detail below, knowing the location of the realization scenarios may facilitate oil recovery and reservoir production operations.
Once the one or more realization scenarios for the measured fluid gradients have been determined, the information obtained from the acts of blocks 368, 370, 372, 374, and 378 may be used to define future dynamic formation analysis (block 380). Information associated with the type and location of the realization scenario may be used as input parameters for the dynamic formation analysis. The dynamic formation analysis may then be used to investigate future logging campaigns, models in reservoir simulators, models in reservoir simulators, and petroleum system modeling. Additionally, the identified realization scenarios may suggest potential issues, and the location of the potential issues, within the reservoir 8 that may impact reservoir productivity. As such, an operator may plan where and how to implement reservoir drilling operations that may recover a desirable amount of hydrocarbons (e.g., the reservoir fluid) from the reservoir 8, and plan surface facility design. Moreover, the realization scenarios predicted, according to block 378, may be used to determine enhanced oil recovery (EOR) techniques to increase productivity of the reservoir 8 that may be affected by the realization scenario. For example, in the case of a gas diffusion realization scenario, an operator may manage the gas diffusion by keeping fluid pressure above a saturation pressure of the gas. The operator may also design the facilities at surface to accommodate the volume of gas that may be produced as a result of the gas diffusion. If the realization scenario indicates the presence of bitumen deposits upstructure, the operator may use organic scale treatments (e.g., xylene washes) to improve the reservoir productivity during reservoir development operations and/or EOR. Therefore, the data processing system 76 may use the information generated from the acts of the method 364 to predict the realization scenarios occurring within the reservoir 8 and identify potential issues, and their location, that may impact reservoir productivity for wellbores within the reservoir 8 and/or other reservoirs having fluid behaviors similar to that of reservoir 8.
The predicted realization scenarios within the reservoir 8 may be used to plan logging measurements that are used to characterize reservoirs and mitigate potential problems that may be associated with the reservoirs. By way of example, the information obtained from the predicted realization scenarios may provide information as to where potential problems may occur within the reservoir 8. As such, the operator may plan where in the reservoir 8 logging measurements are acquired. The logging measurements may also be used to validate the prediction of the realization scenarios. For example, the logging measurements may be fitted to the predicted realization scenarios. In certain embodiments, lab data for the reservoir 8 may be compared to the predicted realization scenario to validate and determine the accuracy of the predicted realization scenario generated from the acts of the method 364. Furthermore, the dynamic EOS models for the predicted realization scenarios may be used in the formation analyses to collect data from other reservoirs and/or wellbores within the reservoir 8 in a way that may increase the accuracy of the realization scenarios identified.
Returning to
For example, the refined fluid geodynamic model from block 384 may enable identification of continuous fluid columns in thermodynamic equilibrium and geological continuity (e.g., vertical fractures/depositional system elements) with a suitable degree of accuracy compared to techniques that do not use a model that receives input parameters from both DFA and borehole imaging logs. In addition, the refined fluid geodynamics model may identify continuous fluid column that are not in thermodynamic equilibrium (e.g., are in disequilibrium) due to, for example, impermeable layers (e.g., the impermeable region) and/or fractures in the reservoir 8. Other reservoir features that may be identified by the fluid geodynamic model include discontinuous fluid columns resulting from flow barriers (e.g., the impermeable region) that are in thermodynamic equilibrium or disequilibrium. The borehole imaging logs may provide an input parameter to the fluid geodynamic model that may estimate lateral dimensions of the discontinuous fluid column. For example, the fluid geodynamic model may receive information associated with the depositional system and/or location of the depositional system within the architecture of the reservoir. In certain embodiments, the fluid geodynamic model may also receive reservoir architectural information generated from a geological process model (GPM). In this way, fine scale well logging information (e.g., the borehole images) may be used to accurately identify the fine-scale reservoir architecture. Knowing the fine-scale reservoir architecture may facilitate reservoir planning and development such that the operator may optimize hydrocarbon extraction. As such, costs associated with exploratory drilling operations, which may result in non-producing wells due to a lack of reservoir architecture information, may be decreased.
The refined fluid geodynamic model may be validated by comparing the model data with known reservoir properties (e.g., obtained from seismic, core sample analysis, empirical historical data from other wells within the reservoir and/or nearby reservoirs) and/or comparing the model data with petroleum systems models (PSM). If the fluid geodynamic model data fits the know reservoir properties and/or the PSM, the fluid geodynamic model provided an accurate representation of the fine-scale reservoir architecture, and the reservoir is properly understood. However, if the fluid geodynamic model data does not fit the known reservoir properties and/or PSM, the fluid geodynamic model did not provide an accurate representation of the fine-scale reservoir architecture, and the reservoir is not properly understood. As such, additional logging and DFA data may be collected from the wellbore 14 and/or other wellbores within the reservoir 8 to continue refining the fluid geodynamic model.
In certain embodiments, the borehole imaging logs and the DFA data may be used to refine the geologic process model (GPM) and/or the equation of state (EOS) models used to determine the dynamic processes (e.g., realization scenarios) of the reservoir according to the acts of the method 364. This may facilitate estimating reservoir formation and fluid characteristics in other spatial locations within the reservoir 8.
In an alternative embodiment, the fine-scale reservoir architecture is estimated before drilling into the reservoir. For example,
In addition to modeling the fine-scale reservoir architecture, the method 400 of
As discussed above, reservoir fluid geodynamics from the downhole fluid analysis (DFA) and borehole logging information may be used to model dynamic fluid behaviors and reservoir depositional characteristics to provide accurate and reliable information associated with the fine-scale architecture of a reservoir of interest. For example, the DFA and logging information (e.g., borehole imaging data) may provide information associated with hydrocarbon timing (e.g., age), type (e.g., light oil, heavy oil), fluid distributions (e.g., gradients), volume of the reservoir fluid, permeable and/or non-permeable regions, faults, fractures, 3D orientation of strata traversing the wellbore, and so forth. This information may be used to identify and locate realization scenarios (e.g., dynamic processes) and reservoir geometries that may affect reservoir productivity. By knowing the fine-scale reservoir architecture (e.g., dynamic fluid processes and reservoir geometries), operators may better assess the economic value of the reservoir, obtain reservoir development plans, and identify hydrocarbon production concerns for the reservoir. Moreover, the information from the fine-scale reservoir architecture may be used to develop future reservoirs.
As may be appreciated, the above techniques for identifying and generating information pertaining to reservoir architecture may be used to identify areas of low permeability, such as by identifying the presence of baffles, shale, or other obstructions that may reduce flow. In other words, baffles are low permeability flow barriers that restrict the flow of fluids in a reservoir. The presence of baffles may be challenging to identify by pressure or seismic surveys. As described herein, a method 600 of log analysis may help identify the presence and location of baffles.
A principle well log is involved in DFA, which provides measurements of spatial gradients in fluid composition such as asphaltene content. Other techniques such as NMR logging and core analysis may be optionally integrated. Baffles 620 (see
The method 600 includes modeling a second realization of the modern fluid compositional gradient (block 610). This realization will be similar to the first realization, except in this realization there are no baffles 620 present in the reservoir. As the result, this modeled modern gradient will be relatively different from the gradient resulting from the initial charge. The method 600 includes comparing both realizations to the measured fluid gradient (block 612). If the realization including the baffles 620 matches the measured gradient, an interpretation is made that the reservoir contains baffles (block 614). If the realization omitting the baffles 620 matches the measured gradient, an interpretation is made that the reservoir does not contain baffles (block 616).
In some embodiments, the fluid gradient assessment of baffles may be integrated with an independent assessment of the baffles 620 from interval pressure transient testing (IPTT). The results of the fluid gradient analysis could be used to identify candidate locations for IPTT analysis. In some embodiments, the assessment of baffles may be integrated with an independent assessment of baffles from petrophysical logging. Petrophysical logs investigate the reservoir the near wellbore region, which may suggest the presence of baffles more extensively in the reservoir. The petrophysical analysis could include NMR logging. The petrophysical logging could be used to identify candidate locations for fluid gradient analysis, or vice versa.
In some embodiments, the assessment of baffles may be integrated with an independent assessment of baffles from core analysis. Core analysis investigates the near wellbore region, which may suggest the presence of fluid obstructions (e.g., the baffles 620) more extensively in the reservoir. The core analysis could include analysis of deformation bands, where low permeability baffles appear as powderized rock layers. This analysis and the other analyses could be used to identify candidate locations for each other.
In some embodiments, the assessment of baffles may be integrated with an independent assessment of baffles from geologic analysis. The geologic analysis could include analysis of faults, stress, tilt and could involve the depositional setting such as distal sheet sands that can contain shales breaks that act as baffles. The current reservoir setting could include distortion of original sediments such as deformation bands that occur as a result of stress and strain post deposition. These reservoir settings can yield baffling.
With knowledge of the time since filling (e.g., from the petroleum system model), two realizations of the modern asphaltene gradient can be created. In both realizations, the total amount of asphaltenes in the reservoir is unchanged from the initial state. However, the distribution of asphaltenes within the reservoir varies. In this example, the initial gradient is less steep than the equilibrium gradient (e.g., due to a large number of charge multiples or the presence of asphaltenes in the form of clusters).
In this example, multiple DFA measurements are made over a laterally and vertically extensive region. The measured fluid gradients are then compared with the different realizations of the modeled modern gradient. If the measurements match the realization including baffles, the presence of baffles is suggested. If the measurements match the realization omitting baffles, the absence of baffles is suggested.
The foregoing outlines features of several embodiments so that those skilled in the art may better understand the aspects of the present disclosure. Those skilled in the art should appreciate that they may readily use the present disclosure as a basis for designing or modifying other processes and structures for carrying out the same purposes and/or achieving the same advantages of the embodiments introduced herein. Those skilled in the art should also realize that such equivalent constructions do not depart from the spirit and scope of the present disclosure, and that they may make various changes, substitutions and alterations herein without departing from the spirit and scope of the present disclosure.
This disclosure claims the benefit of and priority to U.S. Provisional Patent Application No. 62/168,379, titled “Reservoir Fluid Geodynamics System and Method,” filed May 29, 2015; U.S. Provisional Patent Application No. 62/168,404, titled “Reservoir Characterization System and Method,” filed May 29, 2015; and U.S. Provisional Patent Application No. 62/208,323, titled “Systems and Methods for Reservoir Modeling,” filed Aug. 21, 2015, which are incorporated by reference herein in their entireties for all purposes.
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