# Numerical Simulation Modeling of Carbonate Reservoir Based on Rock Type.

1. IntroductionT oilfield is in the transitional belt between the Zagros Fold Zone and the Eastern margin of Arab Platform. This field is an elongated anticline which extends about 45 km by North-South direction and about 15 km by East-West direction. K reservoir is the main developed formation in T oilfield, and its mid-depth is 4250 m, with the abnormal high pressure of 9500 psi. The storage space is dominated by secondary interstices and vugs, and there are few fractures in the reservoir. The average porosity is 12.04%, and the average permeability is 5.87 mD.

K reservoir buried depth is very deep and is affected by the strong diagnosis. Its primary pore structure changes dramatically, and the reservoir physical property distribution has strong heterogeneity. Rock typing is an essential step in the process of the carbonate reservoir characterization and geological modeling, and it is the method of classifying reservoir rocks which have the same fluid flow features into groups [1-6]. Rock typing is an effective way to obtain accurate permeability, and it enables us to get better understanding of the fluid movement and to enhance oil recovery [7-11].

There are several well-known models for the rock type discrimination. Leverett introduced the famous / function which could be used for rock typing [12, 13]. Winland [14] used the mercury injection-capillary pressure curves to develop an empirical relationship among porosity, permeability, and pore throat radii to different mercury saturations and found that the 35th percentile gave the best correlation. Amaefule et al. [15, 16] introduced a hydraulic flow unit concept and developed a method to predict the permeability of cored and uncored intervals; they grouped different rock samples based on their pore attributes similarity.

In this paper, we focus on the high heterogeneity of T oilfield and study the rock typing method for carbonate reservoirs. The capillary pressure curves and the relative permeability curves are assigned to each rock type, and different flow units are generated. Based on geological model, the numerical simulation model has been established, and the accuracy of the model is verified by the history production matching.

2. Rock Typing Method

Darcy's law is an equation that describes the flow of a fluid through porous medium. It can be expressed as below:

q = [K[A.sub.p]/[mu]] x [[DELTA]P/[L.sub.p]], (1)

where q is volumetric flow rate, K is permeability, [A.sub.p] is cross-sectional area of plug, [mu] is viscosity, [L.sub.p] is length of plug, and [DELTA]P is pressure drop.

Poiseuille's equation can be used to determine the pressure drop of a constant viscosity fluid exhibiting laminar flow through a rigid pipe.

q = [n[pi][r.sup.4.sub.i]/8[mu]] x [[DELTA]P/[L.sub.a]], (2)

where n is number of capillary tubes, [r.sub.i] is pore throat radius of ith tube, and [L.sub.a] is apparent length of fluid flow path.

For homogenous plug samples, porosity can be calculated by the following equation:

[phi] = [n[pi][r.sup.2.sub.i]/[A.sub.p]] x [[L.sub.a]/[L.sub.p]], (3)

where [phi] is effective porosity.

By applying Darcy's and Poiseuille's Laws, a relationship between porosity and permeability can be derived as the following equation:

K = [r.sup.2.sub.i][phi]/8[[tau].sup.2], (4)

where [tau] is tortuosity, [tau] = [([L.sub.a]/[L.sub.p]).sup.2].

Converting (4) into logarithmic form, we can derive the following equation:

log [r.sub.i] = C + A log K - B log [phi], (5)

where A, B, C are constant parameters.

For K reservoir, porosity, permeability, and mercury injection experiments have been carried out for 206 plugs, and the throat radii with the mercury saturation ranging from 10% to 85% are calculated for each plug. Take [R.sub.10] (throat radius at the mercury saturation of 10%) as an example; each plug has a [R.sub.10] value; porosity, permeability, and [R.sub.10] of 206 plugs consist of three vectors. By adopting the multiple regression method, the coefficients of (5) can be fitted. Using the same method, the equations of porosity, permeability, and pore throat radii under different mercury injection saturations are fitted.

It is shown in Figure 1 that the radius at mercury saturation 30% has the best correlation with porosity and permeability.

The fitting equation at mercury injection saturation of 30% is

log ([R.sub.30]) = 0.611 + 0.509 log (K) - 0.694 log ([phi]). (6)

Based on the pore throat distributions of plugs, we define five typical pore throat radii; they are separately 0.1 [micro]m, 0.3 [micro]m, 1.0 [micro]m, 3.0 [micro]m, and 10.0 [micro]m, and six rock types can be distinguished by five typical pore throat radii. As shown in Figure 2, five typical curves divide the points into six regions. The relationship between porosity and permeability of each region can be fitted, and the correlation coefficients of fitting equations are reasonably high.

(1) Rock type 1: K = 19.076[[phi].sup.1.0431], [R.sup.2] = 0.7341

(2) Rock type 2: K = 1.4015[[phi].sup.1.2853], [R.sup.2] = 0.9334

(3) Rock type 3: K = 0.1749[[phi].sup.1.3659], [R.sup.2] = 0.9021

(4) Rock type 4: K = 0.0176[[phi].sup.1.4183], [R.sup.2] = 0.9011

(5) Rock type 5: K = 0.0022[[phi].sup.1.3722], [R.sup.2] = 0.8569

(6) Rock type 6: K = 0.0004[[phi].sup.1.1005], [R.sup.2] = 0.6858

3. Flow Characteristics of Each Rock Type

3.1. Pore Throat Distribution. Because of the heterogeneity of carbonate reservoir, the pore throat distribution curves are complex, and they may have "bimodal" or "trimodal" characteristics. Figure 3 shows the pore throat distributions of plugs of each rock type, and the horizontal ordinate is the pore throat radius, while the vertical ordinate is the mercury saturation distribution function (MSDF). If there are n measure points in the test, the mercury saturation distribution function can be calculated as follows:

[mathematical expression not reproducible], (7)

where [MSDF.sub.i] is the MSDF value at the ith point; [S.sub.Hg,i+1] is the mercury saturation at (i + 1)th point; [S.sub.Hg,i] is the mercury saturation at ith point; [r.sub.i+1] is the pore throat radius at (i + 1)th point; [r.sub.i] is the pore throat radius at ith point.

Figure 3 also shows that rock type 1 has the best rock quality, and its pore throat radius is large and the peak value is greater than 10 microns. The larger the rock type number is, the worse the rock quality is, and the smaller the pore throat radius is. Compared with other rock types, rock types 3 and 4 have more complex pore throat distribution.

3.2. Capillary Pressure Curve of Each Rock Type. Capillary pressure curve is useful in characterizing rock type, because it is an indication of pore throat distribution within one rock type. According to the principle of rock typing, the plugs are classified into six groups. Figure 4 shows different capillary pressure curves of each rock type in K reservoir. As the rock number becomes larger, the plateau of the capillary pressure curve becomes higher.

We also could find that there are some clear bandings under the [R.sub.30] method; when the mercury saturation is 30 %, there is one turning point on the capillary pressure curve and the "width" of the capillary pressure curve set becomes narrow; this further verifies the reliability of [R.sub.30] method.

Rock type 6 is very tight with low porosity; the fluid cannot flow in this rock type. Therefore, it could be regarded as a barrier. We use J function to make unique dimensionless drainage capillary pressure curves. For mixed-wet conditions, J function does not work well because the value of permeability depends on the capillary pressure [17]. For K reservoir, the average J function value of each rock type is obtained by data regression.

[mathematical expression not reproducible] (8)

[S.sub.wn] = [[S.sub.w] - [S.sub.wc]]/[1 - [S.sub.or] - [S.sub.wc]], (9)

where [P.sub.c,ave], [K.sub.ave], and [[phi].sub.ave] are average capillary pressure, permeability, and porosity of plugs under the same water saturation; [sigma] is interfacial tension; [theta] is wetting angle; [S.sub.wn] is normalized water saturation; [S.sub.w] is water saturation; [S.sub.wc] is connate water saturation; [S.sub.or] is irreducible oil saturation.

An inverse transformation is performed on (8), and the capillary pressure curve of each rock type can be obtained. As shown in Figure 5, we can find that the capillary pressure curves have obvious regular rules except for rock type 1, and as the rock type number increases, the capillary pressure becomes higher under the same water saturation.

3.3. Relative Permeability Curve. We have carried out relative permeability tests for 66 plugs; they cover from rock type 1 to rock type 5. Because of the heterogeneity of carbonate reservoir, we have not found any rules from the endpoint distributions of relative permeability curves, so we calculate the average value of each rock type, as shown in Table 1.

Corey saturation function is derived to fit the average relative permeability curves [19]. Based on the fit results and the average normalized relative permeability, the denormalized relative permeability curves are calculated and plotted for each rock type. As shown in Figure 6, the relative permeability curves show obvious regular rules except for rock type 1. As the rock type number increases, the irreducible water saturation increases, the two-phase flow region becomes narrow, and the relative permeability at isotonic point decreases. The plugs of rock type 1 have fractures, and the permeability curve and the relative permeability curve have different characteristics.

4. Numerical Simulation Based on Rock Type

4.1. 3D Rock Type Model. Each plug has a coring depth, and we can find the logging data at corresponding depth. The rock type number and the logging data of each plug compose a sample. By using these samples, a link is set up between rock types and log responses by the KNN (K Nearest Neighbor) method. Logs are utilized to predict rock types of noncored wells according to this link, and then, the rock type curves of all wells can be obtained [18]. Using this method, a 3D rock type model is established based on well rock type curves and sedimentary facies constraint, as shown in Figure 7(a).

The porosity model is built up based on logging data, seismic attribution, and sedimentary facies control (Figure 7(b)). According to the relationship between porosity and permeability of each rock type, 3D permeability model can be established (Figure 7(c)). The capillary pressure curve and the relative permeability curve are assigned to each rock type, and different flow units are generated.

4.2. 3D Water Saturation Model. The capillary pressure can be transformed into oil column height under formation conditions; (8) can be written into the following form:

[mathematical expression not reproducible], (10)

where [H.sub.owc] is the depth of water/oil contact; H is the height of arbitrary point in transition zone; [[rho].sub.w] and [[rho].sub.o] are the density of water and oil; g is the acceleration of gravity.

According to (10), water saturation, porosity, permeability, and oil column height relationship can be obtained by multiple regressions. The oil column height can be calculated based on the depth difference between water/oil contact and grid depth, and the water saturation is calculated for each grid. There are different J functions for each rock type; with the changes of the physical properties of formation, water saturation will show different characteristics, as shown in Figure 8.

4.3. History Performance Matching. As the numerical simulation model has been established, we have to testify the accuracy of the model. There are 8 wells on production in K reservoir, and the actual production process of the simulation area has reappeared by the history matching. We use fixed oil production rate as the inner boundary condition, and Figure 9 shows the history matching result of the field oil production rate (FOPR) and field water cut (FWCT); the fitting rate is up to 99.5%.

Take wells K2 and K8 as an example (their formation thickness is similar), we calculate the thickness proportion of different rock types of these two wells. As shown in Figure 10, rock types 4 and 5 are the main types for well K2, and for K8, types 2 and 3 are the main. The reservoir property near K8 is better than that of K2.

Figure 11 shows the production performance of two wells, and the daily production rate of K8 is much higher than that of K2. This proves that the rock type classification is in coincidence with the production performance, and the model is reliable for the further design of the development plan.

Based on the rock type model, we design 15 infill wells, which mainly distribute in rock types 1~3. The predicted production rates of these wells are good, as shown in Figure 12. The higher the oil production rate is, the shorter the stabilized production period is. We design four scenarios to optimize the field oil production rate, and when the field oil production rate is 90000 bbl/d (the average oil production rate per well is 4200 bbl/d), the stabilized production period can achieve four years, which can meet the requirement of the development plan.

5. Conclusions

(1) By applying Darcy's and Poiseuille's Laws, the relationships among porosity, permeability, and different pore throat radii have been fitted, and the typical pore throat radius [R.sub.30] has been selected.

(2) [R.sub.30] method is presented to define six petrophysical rock types with different reservoir properties. The capillary pressure curves and the relative permeability curves are assigned to each rock type, and different flow units are generated.

(3) Based on geological model, the numerical simulation model has been established, and it can reflect the heterogeneity and the fluid distribution of formation very well. This model is proved to be accurate by the production history matching and can be used to design the further development plan.

https://doi.org/10.1155/2017/6987265

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors are grateful for the financial support from the National Science & Technology Major Projects of China (Grant no. 2016ZX05033-003), the National Science Fund for Young Scholars (Grant no. 41702359), and the Corporate Science & Technology Projects of Sinopec (Grant no. P15129).

References

[1] M. R. Dernaika, B. Mansour, D. Gonzalez et al., "Upscaled Permeability and Rock Types in a Heterogeneous Carbonate Core from the Middle East," in Proceedings of the SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, UAE.

[2] E. Tillero, "Stepping forward: An automated rock type index and a new predictive capillary pressure function for better estimation of permeability and water saturation. Case study, Urdaneta-01 heavy oil reservoir," in Proceedings of the SPE Latin American and Caribbean Petroleum Engineering Conference 2012, LACPEC2012, pp. 134-153, mex, April 2012.

[3] J. Bingyu, L. Peiqing, T. Xuequn, and D. Taizhong, "Integrated Numerical Simulation for a Giant, Asphaltene Riched, Carbonate Reservoir," in Proceedings of the SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, UAE.

[4] J. Betancourt, "Rock type modeling: An effective method to determine reservoir quality rock to discretize net pay in Lower Guadalupe Formation, Abanico Field Colombia," in Proceedings of the SPE Latin American and Caribbean Petroleum Engineering Conference 2012, LACPEC 2012, pp. 532-541, mex, April 2012.

[5] N. F. Alhashmi, K. Torres, M. Faisal et al., "Rock typing classification and hydraulic flow units definition of one of the most prolific carbonate reservoir in the onshore Abu Dhabi," in Proceedings of the SPE Annual Technical Conference and Exhibition, ATCE 2016, are, September 2016.

[6] A. L. M. Compan, G. C. R. Bodstein, and P. Couto, "A relative permeability rock-typing methodology with a clustering method combined with a heuristic optimization procedure," SPE Journal, vol. 21, no. 5, pp. 1899-1915, 2016.

[7] F. Ben Amor, Z. Kindi, S. Kriplani, A. Panda, C. Darous, and A. H. Akram, "An Innovative Approach for Integrated Characterization and Modeling of a Complex Carbonate Reservoir," in Proceedings of the SPE Middle East Oil & Gas Show and Conference, Manama, Kingdom of Bahrain.

[8] S. A. Purba, A. P. Garcia, and Z. Heidari, "New Method for Rock Classification in Carbonate Formations Using Well-Log-Based Rock Fabric Quantification," in Proceedings of the Paper SPWLA-2017-SSpresented at the SPWLA 58th Annual Logging Symposium, Oklahoma, USA, 2017.

[9] M. T. Al Hamami, P. Bizarro, A. Sousa, and M. T. Ribeiro, "Reservoir Characterization And Modeling Of A Carbonate Reservoir--Case Study," in Proceedings of the Abu Dhabi International Petroleum Conference and Exhibition, Abu Dhabi, UAE.

[10] C. Hollis, V. Vahrenkamp, S. Tull, A. Mookerjee, C. Taberner, and Y. Huang, "Pore system characterisation in heterogeneous carbonates: An alternative approach to widely-used rock-typing methodologies," Marine and Petroleum Geology, vol. 27, no. 4, pp. 772-793, 2010.

[11] X. Tan, P. Lian, J. Zhang et al., "A new method for 3D oil reservoir modeling based on "double seismic constraint," Shiyou Xuebao/Acta Petrolei Sinica, vol. 37, no. 12, pp. 1518-1527, 2016.

[12] N. El-Khatib, "Development of a Modified Capillary Pressure J-Function," in Proceedings of the Middle East Oil Show, Bahrain.

[13] H. Saboorian Jooybari, G. H. Movazi, and R. Jaberi, "A New Approach for Rock Typing Used in One of the Iranian Carbonate Reservoir (A Case Study)," in Proceedings of the International Oil and Gas Conference and Exhibition in China, Beijing, China.

[14] S. Kolodzie, "Analysis of pore throat size and use of the Waxman-Smits equation to determine OOIP in Spindle field. Colorado," in Proceedings of the SPE Annual Technical Conference and Exhibition, Dallas, Texas.

[15] J. O. Amaefule, M. Altunbay, D. Tiab, D. G. Kersey, and D. K. Keelan, "Enhanced reservoir description: using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals/ wells," in Proceedings of the SPE Annual Technical Conference and Exhibition. Part 2(of 5), pp. 205-220, October 1993.

[16] M. Abbaszadeh, H. Fujii, and F. Fujimoto, "Permeability prediction by hydraulic flow units - Theory and applications," SPE Formation Evaluation, vol. 11, no. 4, pp. 263-271, 1996.

[17] D. G. Hatzignatiou, Y. Zhou, and J. O. Helland, "A Dimensionless Capillary Pressure Function for Imbibition Derived from Pore-Scale Modelling in Mixed-Wet Rock Images," in Proceedings of the SPE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA.

[18] P. Q. Lian, X. Q. Tan, C. Y. Ma, R. Q. Feng, and H. M. Gao, "Saturation modeling in a carbonate reservoir using capillary pressure based saturation height function: a case study of the Svk reservoir in the Y Field," Journal of Petroleum Exploration and Production Technology, vol. 6, no. 1, pp. 73-84, 2016.

[19] O. Jin, I. Saeed, F. Mohammad, P. Tian, I. Mostafa, and M. A. Chughtai, "Dynamic Rock Typing Study of a Complex Heterogeneous Carbonate Reservoir in Oil Field, Iraq," in Proceedings of the Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE.

Peiqing Lian, Cuiyu Ma, Bingyu Ji, Taizhong Duan, and Xuequn Tan

Sinopec Petroleum Exploration and Production Research Institute, Beijing, China

Correspondence should be addressed to Peiqing Lian; lianpq.syky@sinopec.com

Received 18 May 2017; Revised 26 July 2017; Accepted 3 August 2017; Published 1 November 2017

Academic Editor: Yingfang Zhou

Caption: Figure 1: Correlation coefficients of fitting equations of porosity, permeability, and pore throat radii under different mercury injection saturations.

Caption: Figure 2: Rock type classification of K reservoir.

Caption: Figure 3: Capillary pressure curves of each rock type.

Caption: Figure 4: Capillary pressure curves of each rock type [18].

Caption: Figure 5: Capillary pressure curves of five rock types.

Caption: Figure 6: Relative permeability curves of five rock types.

Caption: Figure 7: 3D rock type, porosity, and permeability model.

Caption: Figure 8: 3D water saturation model.

Caption: Figure 9: History matching results.

Caption: Figure 11: Daily oil production rates of K2 and K8.

Caption: Figure 12: Field oil production rate optimization.

Table 1: Endpoint values of relative permeability curve of each rock type. Rock type Swi Kro @ Swi Sorw Krw @ Sorw 1 0.33 0.69 0.41 0.42 2 0.20 0.65 0.31 0.31 3 0.23 0.59 0.33 0.28 4 0.26 0.55 0.35 0.25 5 0.30 0.52 0.38 0.20 Figure 10: The thickness proportion of different rock types of wells K2 and K8. (a) K2 Rock type 1 1% Rock type 2 7% Rock type 3 15% Rock type 4 26% Rock type 5 19% Rock type 6 32% (b) K8 Rock type 1 3% Rock type 2 29% Rock type 3 18% Rock type 4 15% Rock type 5 12% Rock type 6 23% Note: Table made from pie chart.

Printer friendly Cite/link Email Feedback | |

Title Annotation: | Research Article |
---|---|

Author: | Lian, Peiqing; Ma, Cuiyu; Ji, Bingyu; Duan, Taizhong; Tan, Xuequn |

Publication: | Journal of Engineering |

Article Type: | Report |

Date: | Jan 1, 2017 |

Words: | 3510 |

Previous Article: | Bending and Shear Experimental Tests and Numerical Analysis of Composite Slabs Made Up of Lightweight Concrete. |

Next Article: | A Field Study on the Indoor Thermal Environment of the Airport Terminal in Tibet Plateau in Winter. |

Topics: |