Process-based diagenesis modeling for clastic rock reservoir quality prediction

Abstract

A method for predicting the quality of a reservoir includes the steps of drilling, penetrating into the reservoir, obtaining one-dimensional (1D) input data (1204) from the well, where the input data (1204) includes: deposition temperature and depth of burial as a function of deposition time, and lithofacies and compaction data (1208), adding the deposition time to a time interval, inputting the 1D input data (1204) into a diagenetic model, and predicting the quality of the reservoir. Wherein the diagenesis model includes a compaction model that outputs a predicted compaction of the deposition time and a cementation model that outputs a predicted cementation of the deposition time, repeating the first two steps until the deposition time is the current time, and the diagenesis model outputs a predicted compaction curve having a porosity as a function of depth.

Description

Background Art

[0001] Reservoir characterization is used to simulate the behavior of hydrocarbons in a reservoir under different circumstances and find the best production technology to maximize production. Reservoir characterization includes reservoir characteristics related to the reservoir's ability to store and produce hydrocarbons. Reservoir quality predicts the commercial quantity of hydrocarbons in the reservoir and is a geological, engineering, and economic evaluation of the reservoir.

[0002] Reservoir quality is one of the most important uncertainties in reservoir characterization. However, current methods for predicting reservoir quality are generally limited in applicability or require input data that is poorly constrained or difficult to obtain or whose accuracy has not been verified. The accuracy of these traditional methods is limited by well logs (measurements of physical quantities in oil wells relative to depth) and lithofacies data (data from the examination of thin pieces of rock). Therefore, it is difficult to apply these methods to frontier areas with limited wells.

[0003] Therefore, a method for predicting reservoir quality is needed. Summary of the invention

[0004] This Abstract is intended to introduce a selection of concepts that are further elaborated in the Detailed Description below. This Abstract is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

[0005] In one aspect, an embodiment disclosed herein relates to a method for predicting the quality of a reservoir, comprising the following steps: drilling a well, going deep into the reservoir, obtaining one-dimensional (1D) input data from the well, wherein the input data includes: deposition temperature and burial depth as a function of deposition time, as well as facies data and compaction data, adding a time interval to the deposition time, inputting the 1D input data into a diagenetic model, wherein the diagenetic model includes a compaction model that outputs a predicted compaction of the deposition time, and a cementation model that outputs a predicted cementation of the deposition time, repeating the first two steps until the deposition time is the current time, and the diagenetic model outputs a predicted compaction curve, and the porosity is a function of depth.

[0006] Other aspects and advantages of the claimed subject matter will become apparent from the following description and the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Now the specific embodiments of the disclosed technology will be described in detail with reference to the accompanying drawings. For consistency, the same elements in each figure are represented by the same reference numerals.

[0008] Figure 1 A flow chart illustrating method steps for predicting the quality of a reservoir using ID input data, according to one or more embodiments.

[0009] Figure 2 Compaction curves for five deposition phases are shown according to one or more embodiments.

[0010] Figure 3 Prediction curves of compacted porosity, quartz cementation, illite cementation, and final porosity versus target well burial depth are shown according to one or more embodiments.

[0011] Figure 4 The permeability of a sample well with sand dune lithofacies is shown as a function of burial depth according to one or more embodiments.

[0012] Figure 5 A flowchart 500 is shown illustrating method steps for predicting the quality of a reservoir using 2D input data, according to one or more embodiments.

[0013] Figure 6 A depositional facies diagram for the current time for 2D modeling is shown in accordance with one or more embodiments.

[0014] Figure 7 Depth maps for 2D modeling are shown in accordance with one or more embodiments.

[0015] Figure 8 A predicted porosity map of a region of interest of a reservoir is shown in accordance with one or more embodiments.

[0016] Fig. 9 A predicted permeability map of a region of interest of a reservoir is shown in accordance with one or more embodiments.

[0017] Fig.10 A plot of predicted residual porosity due to compaction is shown according to one or more embodiments.

[0018] Fig.11 A graph showing predicted reduced porosity due to quartz cementation according to one or more embodiments.

[0019] Fig.12 A flow chart illustrating method steps for predicting the quality of a reservoir using ID and 2D input data, according to one or more embodiments.

[0020] Fig.13 A computer for performing the steps of a method for predicting the quality of a reservoir according to one or more embodiments is shown. DETAILED DESCRIPTION

[0021] In the following detailed description of the disclosed embodiments, many specific details are listed to provide a more thorough understanding of the present disclosure. However, it is apparent to those skilled in the art that the present disclosure can be implemented without these specific details. In other cases, well-known features are not described in detail to avoid unnecessarily complicating the description.

[0022] In this application, ordinal numbers (e.g., first, second, third, etc.) may be used as adjectives for elements (i.e., any noun in this application). The use of ordinal numbers does not imply or create any particular order of elements, nor does it limit any element to a single element, unless expressly stated, such as using the terms "first," "last," "single," and other such terms. Instead, ordinal numbers are used to distinguish elements. For example, a first element is different from a second element, and a first element may contain multiple elements and is subsequent to (or precedes) a second element in the order of elements.

[0023] The embodiments disclosed herein relate to a method for predicting the quality of a reservoir, comprising the following steps: obtaining 1D input data, the data comprising deposition temperature and burial depth as a function of deposition time, as well as facies data and compaction data, adding a time interval to the deposition time, inputting the 1D input data into a diagenetic model, wherein the diagenetic model comprises a compaction model that outputs a predicted compaction of the deposition time and a cementation model that outputs a predicted cementation of the deposition time, repeating the first two steps until the deposition time is the current time, and the diagenetic model outputs a predicted porosity curve, a predicted permeability curve, a predicted compaction porosity curve, and a predicted cementation curve that vary with depth.

[0024] On the other hand, an embodiment disclosed herein relates to a method for predicting the quality of a reservoir, comprising the following steps: obtaining 2D input data of deposition time (including a depth map and a phase map), adding a time interval to the deposition time, inputting the 2D input data into a diagenetic model, wherein the diagenetic model includes a compaction model that outputs a predicted compaction of the deposition time and a cementation model that outputs a predicted cementation of the deposition time, repeating the first two steps until the deposition time is the current time, and the diagenetic model outputs a predicted porosity map, a predicted permeability map, a predicted compaction porosity map, and a predicted cementation map.

[0025] Embodiments of the present disclosure may provide at least one of the following advantages. The method for predicting reservoir quality is an advanced method for determining the evolutionary history of clastic rocks taking into account the cumulative effects of compaction and quartz cementation. Based on sedimentary facies and burial depth, the method can provide compacted reservoir porosity, quartz cementation, final porosity and permeability of the target reservoir from initial deposition to current burial depth. The predicted porosity and permeability can be used as indicators of non-reservoirs, average reservoirs and good reservoirs, which is helpful for stratigraphic traps (sealed geological containers that preserve hydrocarbons) or diagenetic identification in relevant clastic reservoirs.

[0026] Methods for predicting reservoir quality are used to evaluate reservoir quality in clastic reservoirs with limited sample data, including well burial and temperature history data, 2D phase maps, and depth maps, thereby helping to assess the economic viability of potential oil and gas reservoirs in frontier areas. Methods for predicting reservoir quality improve the understanding of reservoir distribution in the area of ​​interest. For example, areas with high porosity have good reservoir quality, while areas with low porosity have no reservoir or are laterally sealed.

[0027] 1D diagenetic models simplify predictions because only sedimentary facies, thermal history, and burial depth are required to obtain reservoir quality at a location in the area of ​​interest.

[0028] Figure 1 A flow chart 100 showing method steps for predicting the quality of a reservoir using ID input data.

[0029] In step 102, 1D input data is acquired, including: deposition temperature and burial depth as a function of deposition time, as well as facies data and compaction data.

[0030] The diagenetic model requires two types of 1D input data. The first is deposition temperature and burial depth, and the second is lithofacies (the characteristics that distinguish a rock from adjacent rocks) and compaction data. The 1D input data is stored in an excel sheet and entered separately.

[0031] Table 1 lists the thermal history (deposition temperature) and burial depth of a well as a function of deposition time. In one or more embodiments, the burial and thermal history curves are obtained from the well. In other embodiments, the burial and thermal history curves are obtained by basin modeling. Table 1 is input into a diagenetic model to predict the evolution of reservoir quality.

[0032]

[0033] Table 1: 1D input data including burial and thermal history.

[0034] Deposition time is the burial time in millions of years, deposition temperature is the burial temperature in °C, and burial depth is the burial depth in feet. From top to bottom, deposition time is from the initial deposition period 274 million years ago to the present (deposition time is 0 years).

[0035] The 1D input data in Table 1 shows that 274 million years ago, a rock at the surface of the reservoir (buried at a depth of 0 feet) had a temperature of 3.11°C. One million years later (273 million years ago), the same rock was buried at a depth of 160.60 feet and had a temperature of 17.78°C. The initial time of the sample well according to Table 1 is 274 million years, the temperature is 3.11°C, and the depth is 0 feet. The current (0 year) temperature of the sample well according to Table 1 is 127.46°C and the depth is 13856.9 feet. Table 1 consists of an excel sheet. The 1D input data in Table 1 are obtained through basin modeling or geological reconstruction. Since the reconstruction of the history curve is estimated using the current stratigraphic thickness and sedimentary age of the study well, the burial and thermal history evolution of the area of ​​interest is represented by the evolution of the well.

[0036] The petrographic and compaction data are listed in Table 2. The petrographic data were used for the quartz cementation model, and the compaction data were used for the compaction model.

[0037]

[0038] Table 2: 1D input data including petrographic data and compaction data.

[0039] V qtz is the abundance (fraction) of quartz grains in the initial sediment, layer is the surface area (fraction) covered by quartz, D is the average diameter of the initial quartz grains, initialPorUpper and initialPorLower are the compaction porosity pre-exponential constants (%) of the upper and lower depth parts, aUpper and aLower are the compaction porosity exponential constants of the upper and lower depth parts, and initialPer is the permeability in mD.

[0040] In step 104, the deposition time is added to the time interval. In one or more embodiments, the time interval is 1 million years.

[0041] In step 106, the ID input data is input into a diagenetic model, wherein the diagenetic model includes a compaction model that outputs a predicted compaction of a deposition time, and a cementation model that outputs a predicted cementation of a deposition time.

[0042] 1D diagenetic model

[0043] The 1D diagenetic model includes diagenetic compaction and cementation modeling. The 1D diagenetic model requires lithofacies data, compaction data, burial data, and thermal history as input data. In one or more embodiments, the lithofacies data is embedded in the diagenetic model. Only the parameters of the diagenetic model are corrected (calibrated). When running the 1D diagenetic model, the input data needs to be entered and the lithofacies of the target well and the area of ​​interest need to be selected.

[0044] Taking the dune lithofacies as an example, the 1D modeling process includes compaction and quartz cementation modeling. The modeling is calculated from the initial deposition to the present.

[0045] Its initial time is 274 million years and the initial temperature is 3.11°C. Its calculated compaction porosity is 40.2% and the quartz and illite cementation is 0%. After a period of time, the compaction porosity and final porosity decrease, while the cementation increases.

[0046] At the current time, the burial depth is 13856.9 feet and the temperature is 127.46°C. The predicted compacted porosity is 23.96%, quartz cementation is 8.08%, and illite cementation is 2.02%. The ultimate porosity is 13.85% and the permeability is 998.30mD.

[0047] Compaction Model

[0048] The compaction model calculates the residual porosity caused by compaction as a function of depth (compaction curve). Considering the different porosity-depth gradients at different depth intervals, the compaction curve is divided into two equations at a depth of 2 km:

[0049] φ com_final,f =φ 0,f -φ compaction,f

[0050] For z < 2 km (6561.68 feet)

[0051] For z ≥ 2 km (6561.68 feet)

[0052] where φ com_final,f is the residual porosity related to lithofacies caused by compaction, in %, φ 0,f is the initial porosity of the sedimentary phase f, in %, φ compaction,f is the reduced porosity caused by compaction, in %, φ com_final_upper,f is the residual porosity related to lithofacies at depths less than 2 km, φ com_final_lower,f is the residual porosity related to lithofacies at depths greater than 2 km, A f and B f is the compaction porosity pre-exponential constant, in %, a fand b f is the compaction porosity exponential constant, and z is the burial depth in feet.

[0053] Parameter A f ,B f ,a f and b f It is also controlled by the sedimentary facies and can be estimated using the least squares regression method. Due to the influence of sedimentary phase on compaction, this method requires the specification of a compaction curve for each sedimentary facies.

[0054] Consolidated sediments are sediments that have been compacted and cemented until the sediment becomes solid. Consolidation causes density to increase and porosity to decrease. At shallow depths, the porosity (pore volume or percentage of pore space, or the volume of the rock that can hold fluid) of sandstone decreases with depth, primarily due to effective stresses (mechanical compaction). Mechanical compaction is the physical process of sediment consolidation, which packs particles more closely together, thereby reducing pore space.

[0055] During burial, the pressure of the overburden on the sediment increases, leading to compaction of the sediment, loss of pore fluids, and accumulation of layers of sediment as the particles cement together to form rock. Compaction consolidates the sediment, reducing the pore space in the sediment because the particles of the sediment are squeezed more tightly together. Cementation is the process by which ions carried in groundwater chemically precipitate between the sediment particles to form crystalline material. The crystalline material fills the pores of the minerals and forms "bridges" between the original sediment particles, bonding the particles together. In this way, sand becomes sandstone. Sandstone is a clastic sedimentary rock (sediment composed of broken pieces) whose particles are primarily the size of sand. Sandstone includes either consolidated sand (sediment that has been compacted and cemented so that it becomes tightly bound together like hard rock) or rock composed primarily of quartz sand (an abundant rock-forming mineral composed of silicon and oxygen SiO2). Cementation is part of the diagenesis, or lithification, of sediments. Diagenesis is the transformation of sediments into sedimentary rocks through physical, chemical, or biological transformation at relatively low temperatures and pressures, resulting in changes in the original mineralogy and texture of the rock.

[0056] The compaction curve can be expressed as an exponential relationship between porosity and depth. Reservoir porosity decreases steadily at depths less than 2 km (6561.68 ft), which is mainly controlled by mechanical compaction. In contrast, porosity decreases significantly at depths greater than 2 km (6561.68 ft), which is controlled by complex diagenesis such as quartz cementation. The degree of compaction is also affected by sedimentary facies, such as texture and compositional properties.

[0057] Bonding model

[0058] Quartz is a rock-forming mineral composed of silicon and oxygen (silicon dioxide). Sandstone and other clastic sedimentary rocks contain sand grains composed of quartz. The cementation model calculates the residual porosity caused by quartz cementation.

[0059] Quartz cementation is an important process for reducing the porosity of clastic reservoirs. The precipitation of quartz cementation occurs after the effective burial and compaction temperature exceeds about 80°C, because temperature controls the solubility of quartz and the most important silica release reaction. Quartz cementation usually appears in the form of quartz cover or pore-filling microcrystalline quartz and occupies the reservoir pores.

[0060] Regarding the method for predicting reservoir quality, the prediction process of clastic reservoir quality includes the following steps:

[0061] The method used to predict reservoir quality uses the kinetically controlled quartz cementation model proposed by Lander and Walderhaug (1999). The quartz cementation model is modified according to the sedimentary facies, which means that the input data are different for different sedimentary facies. The modified function calculates the total amount of quartz cement precipitated during the time increment by taking into account the surface area of ​​quartz in the sandstone available for precipitation of quartz cement and the temperature range experienced by the relevant clastic reservoir interval:

[0062]

[0063] where φ cementation,f is the volume of quartz precipitated in the current time step associated with the lithofacies, in cubic centimeters, m is the molar weight of quartz (60.08 g / mol), and ρ is the density of quartz (2.65 g / cm3) t,f is the surface area of ​​quartz in the current time step, in cm 2 , a is the quartz precipitation rate pre-exponential constant, unit is mol / cm 2 s, t is the duration of the time step in millions of years, Ea is the activation energy of quartz in kcal / mol, R is the gas constant, T t is the temperature in °C.

[0064] The surface area of ​​quartz is an important factor controlling the net rate of quartz cementation. It is a function of the abundance of detrital quartz grains in the initial sediment, the average grain size of the quartz grains, and the change in porosity over time:

[0065]

[0066] Where V qtz,f is the abundance (fraction) of quartz grains related to the lithofacies in the initial sediments, v 0,f is the initial rock volume, in cm 3 , D fis the average diameter of the primary quartz grains from facies f, in cm, φ final,t is the porosity of the current time step, in %, φ 0,f is the initial porosity from lithofacies f, in %, and coat f is the surface area (fraction) of quartz coated. The detailed parameter values ​​are mainly obtained from petrological studies.

[0067] The 1D diagenetic model separates the compaction model from the cementation model. The compaction model and cementation model are controlled by sedimentary facies.

[0068] In step 108, the first two steps are repeated until the deposition time is the current time, and the diagenetic model outputs a predicted compaction curve in which the porosity is a function of depth.

[0069] 1D data output

[0070] Figure 2 The relationship between porosity and lithofacies depth (compaction curves) for five sedimentary facies according to one or more embodiments is shown. The five sedimentary facies are dune, interdune, dry salt lake, ephemeral channel, and aeolian reworked by river floods. Figure 2 The compaction curve in illustrates the effect of burial history on the porosity of compacted sediments. The compaction curve is an exponential function.

[0071] Figure 3 The predicted curves of compaction porosity, quartz cementation, illite cementation, and final porosity versus target well burial depth are shown.

[0072] Figure 4 The variation of permeability with burial depth for the sample well with sand dune as lithofacies is shown.

[0073]

[0074] Table 3: 1D output data for sample wells

[0075] The 1D output data is listed in Table 3. Referring to Table 3, Time indicates the deposition time in millions of years, Temperature is the deposition temperature in °C, Depth is the burial depth in ft., Poro Mech is the compacted porosity in %, Reactive Surface Area is the effective reactive surface area, Quartz Cement is the volume of quartz cement in %, Illite Cement is the volume of illite cement in %, Poro Final is the final porosity at each deposition time from the initial time to the present in %, and PermFinal is the final permeability in %.

[0076] Final Output: Reservoir Quality

[0077] The reservoir quality of clastic reservoirs is the cumulative product of sedimentation and diagenesis. The initial reservoir quality is mainly controlled by the sedimentary environment. The reduction in original porosity and permeability is due to early mechanical compaction, followed by cementation and further compaction. The reservoir porosity is calculated as follows:

[0078] φ final =φ 0,f -φ compaction,f -φ cementation,f ,

[0079] where φ final is the final porosity, in %. Final porosity φ final It can also be expressed as:

[0080] φ final =φ com_final,f -φ cementation,f

[0081] The matrix permeability is calculated using the Kozeny-Carman formula. Its value is calculated based on the porosity, ignoring the changes in grain size, tortuosity and specific surface area:

[0082]

[0083] Where k is the reservoir porosity, in %, φ is the permeability, in mD, k n,f is the initial permeability of lithofacies f, in mD.

[0084] Figure 5 A flow chart 500 showing method steps for predicting the quality of a reservoir using 2D input data.

[0085] In step 502, 2D input data of deposition time, including depth map and phase map, is acquired. In one or more embodiments, the deposition time is the current time.

[0086] 2D Input Data

[0087] For 2D diagenetic modeling, the phase map and burial depth map of the reservoir area of ​​interest are required as input data of the 2D diagenetic model. Figure 6 , Figure 7 2D input data for the diagenetic model is shown.

[0088] Figure 6The sedimentary facies map for the current time used for 2D modeling is shown. The calculated final reservoir quality is calibrated by the measured data of the well. The sedimentary facies map shows the area of ​​interest of the reservoir, where different local areas of the area of ​​interest are displayed in different colors. The colors include orange, yellow, green, light blue and dark blue. Each color represents a facies. Orange represents ephemeral channels, yellow represents aeolian reworked by river floods, green represents dunes, dark blue represents inter-dune areas, and dark blue represents depressions. Figure 6 The distribution of lithofacies in the area of ​​interest is shown.

[0089] Table 4 lists Figure 6 Data from the phase diagram of the fluvial-eolian sedimentary system.

[0090] X Y Mutually 741800 2606700 5 741800 2606800 5 741800 2606900 5 741800 2607000 5 741800 2607100 4 741800 2607200 4 741800 2607300 3 741800 2607400 3 741800 2607500 3 741800 2607600 3 741800 2607700 3 741800 2607800 3 741800 2607900 3 741800 2608000 3 741800 2608100 3 741800 2608200 3 741800 2608300 4 741800 2608400 4 741800 2608500 4 741800 2608600 4 741800 2608700 4 741800 2608800 4 741800 2608900 4 741800 2609000 3 741800 2609100 3 741800 2609200 3 741800 2609300 3 741800 2609400 3 741800 2609500 3 741800 2609600 3 741800 2609700 3 741800 2609800 3

[0091] Table 4: Current time according to the fluvial-eolian sedimentary system Figure 6 Phase diagram data.

[0092] Table 4 lists the data for a small portion of the graph. The X and Y values ​​of the graph are imaged as the longitude and latitude of the region of interest. Thus, the data indicates the latitude lines of the region of interest, with changes in latitude and no changes in longitude.

[0093] The first column in Table 4 shows Figure 6 The x-axis of the phase diagram in Table 4 indicates Figure 6 The y-axis of the phase diagram in , and the third column according to Figure 6 The colors of the phase diagram indicate the phase type. Taking the fluvial-eolian depositional system as an example, 1 is a transient channel, 2 is aeolian reconstructed by river floods, 3 is a dune, 4 is between dunes, and 5 is a depression.

[0094] Each row represents each grid (a series of vertical sections extracted from the 3D seismic data volume) of the area of ​​interest, which is also the seismic coordinate of the seismic node in the seismic data. During the seismic data acquisition process, since all acquisition units are interconnected, the cable system can access the seismic data in real time during the source production process, and finally transmit the data directly to the central computer system. Nodes are autonomous acquisition units without interconnections. The data volume including the X, Y and seismic attributes of each grid is obtained through seismic interpretation. The x, y coordinates and facies type of each grid are obtained from the seismic data. The facies type is determined by geological analysis and seismic interpretation. The grid is a discretization of the 3D seismic volume. Each grid is defined as the location of each node in the seismic data acquisition system.

[0095] Figure 7 A depth map used for 2D modeling is shown. Figure 7A map showing an area of ​​interest at the surface of a reservoir. The area of ​​interest consists of pixels with colors between dark blue and dark red. The colors represent the burial depth of the rock mass in feet. Dark blue pixels represent a burial depth of 7,000 feet, and dark red pixels represent a burial depth of more than 11,000 feet. Figure 7 The burial depth of each local area within the region of interest is shown.

[0096] Table 5 contains Figure 7 2D depth data of the grid. Each row represents a grid, similar to Table 4. The column order from left to right is the X coordinate, Y coordinate and the burial depth at the current time, in ft. The current burial depth of each grid is also obtained from the seismic data.

[0097]

[0098]

[0099] Table 5: Figure 7 The 2D input data of the depth map.

[0100] Back to Figure 5 In the flowchart of FIG. 5 , in step 504 , the deposition time is added to the time interval.

[0101] In step 506, the 2D input data is input into a 2D diagenetic model, which outputs predicted compaction and predicted cementation at the time of deposition.

[0102] The 2D diagenetic model requires a phase map and a depth map of the area of ​​interest at the current time. The 2D diagenetic model is divided into two parts. The first part is to recover the burial history of the input data, and calculate the burial, heat and depth of each grid through the horizontal refraction of the standard well.

[0103] In the stratigraphic system, the burial history evolution of each depth point is similar, rising or falling at the same historical time. When the well depth data is known, the depth data of other depth points in the area of ​​interest can be calculated using mathematical methods such as horizontal refraction, which means that the entire 2D data can be obtained.

[0104] The second part is to calculate the diagenetic model of each grid using the 1D diagenetic model. After 1D modeling, the values ​​of porosity, permeability, quartz cementation, and illite cementation are calculated for each grid from the initial deposition time to the present, and then a map is generated. For example, a map of compaction, cementation, final porosity, permeability distribution, and cementation in the reservoir of interest is generated.

[0105] In step 508, the first two steps are repeated until the deposition time is the current time, and the diagenetic model outputs a predicted porosity map, a predicted permeability map, a predicted compaction porosity map, and a predicted cementation map.

[0106] 2D output data

[0107] Figure 8 A predicted porosity map for a region of interest in the reservoir is shown. In particular, Figure 8 Regions of interest are shown with different colors representing different partial areas. The colors vary between dark blue and dark red and represent different porosities in %. Dark blue represents partial areas with a porosity of 2%, while dark red represents partial areas with a porosity of 20%. The porosity map is predicted by the diagenetic model.

[0108] Fig. 9 A predicted permeability map for a region of interest in the reservoir is shown. In particular, Fig. 9 The region of interest is shown with different colors representing different partial areas. The colors vary between dark blue and dark red, representing different permeabilities in mD. The dark blue represents a partial area with a permeability of 1 mD, while the dark red represents a partial area with a permeability of 10,000 mD. The permeability map is predicted by the diagenetic model.

[0109] Fig.10 The predicted residual porosity due to compaction (φ0-φ final,t ) (Compacted porosity). The compacted porosity map includes partial regions with porosities of 5% to 26%. Each partial region has a different color depending on the porosity of the partial region. The colors of the partial regions range from dark blue (porosity of 5%) to dark red (porosity of 26%).

[0110] Fig.11 Porosity diagram showing the predicted decrease due to quartz cementation cementation,f (Bonded porosity). The bonded porosity map includes sub-areas with a porosity between 2% and 9%. Each sub-area has a different color depending on the porosity of the sub-area. The colors of the sub-areas range from dark blue (2% porosity) to dark red (9% porosity).

[0111]

[0112]

[0113] Table 6: 2D output data for the region of interest

[0114] The 2D output data are listed in Table 6. The columns of the exported 2D data are from left to right: X coordinate, Y coordinate, phase code, current burial depth in ft, final compaction porosity in %, final porosity in %, final permeability in mD, quartz cementation volume in %, illite cementation volume in %. Each row represents the data for each grid.

[0115] Fig.12 A flowchart 1200 is shown of method steps for predicting reservoir quality using 1D and 2D input data according to one or more embodiments. The method for predicting reservoir quality was tested on a typical well in an area of ​​interest of the reservoir. The well provides historical evolution of cementation, compaction, porosity and permeability over time or depth for use in diagenetic models. 2D modeling easily displays prediction maps for geological time periods. First, input data is obtained from previous research data, basin modeling, etc. Then, the input data is stored in a separate excel file. The input data includes burial depth, thermal history, facies and compaction data. In step 1202, input data for deposition time is obtained. The input data is one-dimensional (1D) 1204 or two-dimensional (2D) 1210. The 1D input data 1204 includes burial depth and thermal history 1206 and sedimentary facies and compaction data 1208. The 2D input data 1210 includes a depth map 1212 and a sedimentary facies map 1214.

[0116] In step 1216, the deposition time is added to the time interval. In one or more embodiments, the time interval is one million years. For example, when the deposition time is -274 million years (274 million years ago) and the time interval is one million years, the deposition time after adding the time interval is -273 million years.

[0117] Before inputting the input data 1202 into the diagenetic model 1220, the input data 1202 needs to be calibrated.

[0118] In step 1218, the input data 1202 is calibrated. Calibration includes adjusting model parameters of the diagenetic model.

[0119] In step 1220, the input data 1202 is input into the diagenetic model. In one or more embodiments, the 1D input data or the 2D input data is input into the diagenetic model 1220. In other embodiments, the 1D data and the 2D data are input into the diagenetic model 1220 simultaneously.

[0120] In step 1222, the diagenetic model 1220 predicts a compaction curve for the reservoir using the compaction model.

[0121] In step 1224, the diagenetic model 1220 uses the quartz cementation model to predict the cementation of the reservoir.

[0122] In step 1226, the predictions of the diagenetic model 1220 are recorded. After the predictions are recorded, another time interval is added and the input data is calibrated again and input into the diagenetic model 1220. In other words, after step 1226, steps 1216, 1218, and 1220 are repeated. Subsequently, the diagenetic model 1220 predicts compaction 1222, cementation 1224, and the data is recorded 1226. Steps 1220, 1222, 1224, 1226, 1216, 1218, and 1220 are repeated until the deposition time is present (0 years).

[0123] In step 1228, the diagenetic model 1220 outputs the modeling results. If it is 1D data, the diagenetic model 1220 outputs a table. If it is 2D data, the diagenetic model 1220 outputs a map.

[0124] In one or more embodiments, the steps of flowchart 1200 may be run in parallel, combined, looped, or in any order.

[0125] Fig.13 A computer 1300 is shown for performing the method steps for predicting the quality of a reservoir. In one or more embodiments, the computer 1300 is a high performance computing (HPC) device, a server, a desktop computer, a notebook / laptop computer, a wireless data port, a smart phone, a personal data assistant (PDA), a tablet device, one or more computer processors in these devices, or any other suitable processing device, including a physical or virtual instance of a computing device (or both). In addition, the computer 1300 includes an input device, such as a keypad, a keyboard, a touch screen, or other device that can accept user information, and an output device for transmitting information related to the operation of the computer 1300, including digital data, visual or audio information (or a combination of information) or a GUI.

[0126] The computer 1300 includes an interface 1304 for receiving input data. The interface 1304 includes software that supports one or more communication protocols. The interface 1304 also includes hardware for receiving physical signals inside and outside the computer 1300 shown in the figure.

[0127] In addition, the computer 1300 includes a software component 1308 that contains equations for executing the method steps for predicting the quality of a reservoir. The equations are stored in the software component of the computer 1300. Although illustrated as an internal part of the computer 1300, in other embodiments, the software component 1308 is an external component of the computer 1300.

[0128] The computer 1300 includes a processor 1306. The processor 1306 executes instructions and processes input data according to the software component 1308 to perform the method steps for predicting the quality of a reservoir according to the software component 1308.

[0129] The computer 1300 also includes a database 1320 for storing output data. Although the database 1320 is shown as a component of the computer 1300, in an alternative embodiment, the database 1320 is external to the computer 1300. The database 1320 can be any repository capable of storing data, including but not limited to data structures such as tables, lists, arrays, etc.

[0130] The interface 1304, the processor 1306, the software components 1308, and the database 1320 communicate via a system bus 1314. In one or more embodiments, any or all of the interface 1304, the processor 1306, the software components 1308, and the database 1320 communicate with each other via the system bus 1314 using an application programming interface (API) 1310 or a service layer 1312 or a combination of the API 1310 and the service layer 1312.

[0131] Although only a few example embodiments have been described in detail above, it will be readily apparent to those skilled in the art that many modifications may be made to the example embodiments without materially departing from the invention. All such modifications are therefore intended to be included within the scope of the present disclosure as defined in the following claims. In the claims, the means plus function phrase is intended to cover structures described herein having the described function, including not only structural equivalents but also equivalent structures. Thus, while nails and screws may not be structural equivalents because nails use a cylindrical surface to fasten wooden parts together and screws use a helical surface, nails and screws may be equivalent structures in the context of fastening wooden parts. It is the express intent of the applicant not to cite 35 U.S.C. §112(f) to impose any limitation on any claim herein unless the term "means" and the associated function are expressly used in the claim.

Claims

1. A method for predicting the quality of a reservoir, comprising the following steps: Drilling, deep into the reservoir, One-dimensional (1D) input data is obtained from the well, where the input data includes: Deposition temperature and burial depth as a function of deposition time, and Lithological and compaction data, Add the deposition time to the time interval, The 1D input data is fed into a diagenetic model, wherein the diagenetic model includes a compaction model that outputs a predicted compaction of a deposition time, and a cementation model that outputs a predicted cementation of a deposition time, The first two steps are repeated until the deposition time is the current time, and the diagenetic model outputs a predicted compaction curve with porosity as a function of depth.

2. The method of claim 1, wherein the diagenetic model predicts the compaction curve using a compaction model having two equations, wherein a first equation is used for depths greater than 2 km (6561.68 feet) and a second equation is used for depths less than 2 km (6561.68 feet).

3. The method of claim 2, wherein the first equation is an exponential relationship between porosity and depth.

4. The method of claim 2, wherein the first equation is governed by mechanical compaction.

5. The method of claim 2, wherein the second equation is an exponential relationship between porosity and depth.

6. The method of claim 2, wherein the second equation is governed by quartz cementation.

7. The method of any one of claims 1 to 6, wherein the cementation model comprises the kinetically controlled quartz cementation model from Lander and Walderhaug.

8. The method according to any one of claims 1 to 7, wherein the diagenetic model further outputs with each deposition time: compacted porosity, effective reactive surface area, volume cemented by quartz, volume cemented by illite, ultimate porosity and ultimate permeability.

9. The method according to any one of claims 1 to 8, wherein the diagenetic model predicts the permeability by means of the Kozeny-Carman equation.

10. The method of any one of claims 1 to 9, wherein the petrographic and compaction data comprises: The abundance of quartz grains in the initial sediments, The surface area of ​​the quartz that is coated, The average diameter of the initial quartz grains, The compaction porosity pre-exponential constant for the upper depth section, The compaction porosity pre-exponential constant for the lower depth section, The compaction porosity exponential constant of the upper depth section, The compaction porosity exponential constant for the lower depth fraction, and Permeability.

11. The method according to any one of claims 1 to 10, wherein the diagenetic model is calibrated by adjusting parameters of the diagenetic model.

12. A method for predicting the quality of a reservoir, comprising the steps of: Drilling, deep into the reservoir, Get 2D input data from wells and seismic interpretations, where the input data includes depth maps and phase maps for the current time, Add the deposition time to the time interval, The 2D input data is fed into a diagenetic model, wherein the diagenetic model includes a compaction model that outputs a predicted compaction of a deposition time, and a cementation model that outputs a predicted cementation of a deposition time, Repeat the first two steps until the deposition time is the current time, and the diagenetic model outputs the predicted porosity map, predicted permeability map, predicted compaction porosity map, and predicted cementation map.

13. The method of claim 12, wherein the diagenetic model predicts the compaction curve using a compaction model having two equations, wherein a first equation is used for depths greater than 2 km (6561.68 feet) and a second equation is used for depths less than 2 km (6561.68 feet).

14. The method of claim 13, wherein the first equation is an exponential relationship between porosity and depth.

15. The method of claim 13, wherein the second equation is an exponential relationship between porosity and depth.

16. The method according to any one of claims 12 to 15, wherein the cementation model uses the kinetically controlled quartz cementation model from Lander and Walderhaug.

17. The method of any one of claims 12 to 16, wherein the diagenetic model further outputs with each deposition time: compacted porosity, effective reactive surface area, volume cemented by quartz, volume cemented by illite, ultimate porosity and ultimate permeability.

18. The method of any one of claims 12 to 17, wherein the diagenetic model predicts a permeability map via the Kozeny–Carman equation.

19. The method of any one of claims 12 to 18, wherein the diagenetic model is calibrated by adjusting parameters of the diagenetic model.

Images