A New Methodology for Determination of Layered Injection Allocation in Highly Deviated Wells Drilled in Low-Permeability Reservoirs
Abstract
:1. Introduction
2. Geological Background
3. Sand Body Connectivity Evaluation
3.1. Identification of a Single Sand Body
3.1.1. Vertical Interface Characteristics and Stacking Patterns of Single Sand Body
- (1)
- Single-period channel vertical isolated type
- (2)
- Multiperiod channel vertical separation type
- (3)
- Multiperiod channel vertical superposition type
- (4)
- Multiperiod channel cutting and stacking type
3.1.2. Lateral Contact Relationship and Identification Mark of a Single Sand Body
- (1)
- Inter-bay contact
- (2)
- Levee contact
- (3)
- Side-cut contact
- (4)
- Substitutive contact
- (5)
- Docking contact
3.2. Single Sand Body Division Method
3.2.1. Flow Unit Division of Single Sand Body
3.2.2. Quantitative Prediction of a Single Sand Body Boundary
3.2.3. Example of the Flow Unit Correction and Single Sand Body Contact Relationship
3.3. Quantitative Evaluation of the Sand Body Connectivity
- (1)
- Connectivity coefficient
- (2)
- Transition coefficient
4. Calculation of Real Displacement Distance
5. Layered Injection Allocation Calculation Method
5.1. Layered Injection Allocation Calculation Formula
5.2. Calculation Example of Layered Injection Allocation in Highly Deviated Wells
6. Production Dynamic Verification of the Layered Water Injection Adjustment Results
7. Conclusions
- (1)
- Four types of vertical stacking patterns and five types of lateral contact relationships of single sand body were identified using sand body configuration. The deposition rate and sediment recharge rate of the turbidite sedimentary system are fast, and the vertical multiperiod channel cutting and stacking type and lateral docking contact of the single sand body are the most developed. Through the combination of three methods of sand body configuration, seepage unit, and single sand body boundary identification, the accuracy of single sand body identification in the target area was 12.6% higher than that of sand body configuration identification.
- (2)
- The connectivity coefficient characterizes the connectivity ratio of sand bodies, the transition coefficient characterizes the connectivity strength of sand bodies, and the connectivity degree coefficient characterizes the real connectivity of sand bodies. The connectivity degree coefficient can be used as a standard for the quantitative evaluation of single sand body connectivity to determine the connectivity of single sand bodies.
- (3)
- In order to calculate the reasonable layered injection allocation of water injection reservoirs in highly deviated wells, it is suggested that three to five typical wells under different fracturing scales should be selected for microseismic monitoring, and the correlation between the fracture parameters and the ground fluid volume should be regressed to provide the basis for the lack of microseismic monitoring wells. According to the calculation results of the correlation formula, the fracture network is described, and the real displacement distance of oil and water wells in each layer of highly deviated wells is obtained.
- (4)
- The new methodology for determination of layered injection allocation in highly deviated wells drilled in low-permeability reservoirs clarifies the connectivity relationship between single sand bodies, describes the method for obtaining the real displacement distance of different layers in highly deviated wells, and determines the reasonable stratified injection allocation between layers. The method’s implementation resulted in a 3.6% reduction in the water cut by a year, and a significant increase in the daily oil production of individual wells by 0.5 t/d in the target area. The impact of the injection allocation adjustment was notably significant. This method is scientific and reasonable, simple and practical, convenient and fast, and has a good application value for the same type of highly deviated wells’ water injection development reservoirs.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parametric Classification | Factor Classification | Method Classification | Method Characteristics | Limitations |
---|---|---|---|---|
Single parameter | Geological factors | Effective thickness method [3] | The layered injection allocation is determined with the ratio of the oil-producing section to the total oil-producing section. | This method considers incomplete parameters. |
Connected thickness method [4] | The layered injection allocation is determined with the ratio of the connected thickness of the sand body to the thickness of the oil well. | |||
Development factors | Injection–production ratio method [3] | The layered injection allocation is ascertained by dividing the injection production ratio of each individual layer by the total injection production ratio. | The injection–production ratio of each layer is difficult to obtain. | |
Liquid production intensity method [4] | The layered injection allocation is quantitatively analyzed through the multiplication of three factors: the perforation thickness, the connectivity coefficient, and the intensity of water injection in each layer. | The connectivity coefficient and water injection intensity data of each layer are difficult to obtain. | ||
Multiparameter | Geological factors | Stratigraphic coefficient method [3] | Using the weighted formation coefficient, the water injection splitting coefficient of each layer is obtained. | This method considers incomplete parameters. |
Sand body connectivity evaluation Stratigraphic coefficient method [5] | The sand body connectivity is divided into three categories by sedimentary facies, and the water injection is divided by combining the physical parameters. | The reason for sand body connectivity is not sufficient. | ||
Remaining oil distribution method [6] | The layered injection allocation is obtained using the quantitative relationship between the recovery degree and the cumulative injection pore volume multiples. | The water absorption data acquisition is difficult. | ||
Revised coefficient method [4] | Considering the injection–production balance, water cut rise rate, pump efficiency, and other factors, the empirical formula is obtained. | 1. This method considers many parameters and it is difficult to obtain some data. 2. There are many artificial coefficients, which easily cause errors. | ||
Comprehensive factors | Splitting coefficient method [2] | According to geological conditions, oil displacement conditions, and mining conditions of the reservoir, the water injection volume is allocated according to the weight of the relevant parameters. | ||
Balanced displacement method | Considering the physical properties and utilization status of each layer, the layered injection allocation relationship is established [7]. | 1. It is difficult to obtain layered recovery degree data. 2. The connectivity of sand bodies in each layer is not considered. | ||
The layered injection allocation is obtained using the quantitative relationship between the recovery degree and the injection pore volume multiples [8]. | 1. This method requires a lot of experimental support, which cannot be replicated. 2. The connectivity of sand bodies in each layer is not considered. | |||
The layered injection allocation is obtained using the idea of displacement flux equalization [9]. | This method does not consider the connectivity of sand bodies in each layer. | |||
Seepage resistance coefficient method [10] | The vertical splitting coefficient of water injection well is determined using seepage resistance coefficient. | |||
Mathematical model | Development factors | Multiple regression method [11] | A multiple sequence regression model is formulated, drawing from the dynamic production data of both the central water injection well and the surrounding production wells within the well group. | These methods are aimed at the optimization of well group injection allocation and cannot realize the optimization of layered injection allocation. |
Neural network method [12] | The relationship model between liquid production and water injection is established using neural network technology. | |||
Conductivity method [13] | The connectivity of the unit is characterized by its electrical conductivity and connected volume. | |||
Capacitance–resistance method [14] | The formula for estimating water flooding in layered reservoirs is derived from the capacitance–resistance model equation. | This method considers many parameters, and it is difficult to obtain oil production and water injection of each layer. | ||
Economic factors | Net present value method [15] | The water injection cycle, injection volume, and production should be adjusted based on the maximum net present value of production. | This method from the economic evaluation does not consider formation and development factors. |
Evaluation Type | Evaluation Method | Method Characteristics | Limitations |
---|---|---|---|
Inter-well connectivity | Dynamic monitoring method | The connectivity between oil and water wells is judged using dynamic monitoring data [39,40]. | This method cannot judge the inter-well connectivity without dynamic monitoring data or seismic data interpretation. |
inter-sand body connectivity | Seismic method | Based on seismic data, the connectivity of the sand body is analyzed [41]. | |
Sand–ground ratio method | The connectivity of sand body is judged using the sedimentary structure model of sand body described by outcrop [42]. | 1. The accuracy of this method is low; 2. The method cannot effectively identify the sedimentary interface; 3. The method cannot clarify the connectivity between different sand bodies. | |
Sandstone amalgamation method | Through the identification of sand mud facies, the degree of sandstone amalgamation is described. The higher the amalgamation rate, the better the connectivity [43]. | ||
Sand body configuration method | By analyzing the contact relationship of sand bodies, the connectivity of sand bodies is determined [44]. | This method cannot clarify the connectivity and degree of connectivity between different sand bodies. |
Contact Type | Contact Relationship | Flow Unit Type | Elevation Difference (Yes/No) | Contact Mode | Connectivity | Transition Coefficient |
---|---|---|---|---|---|---|
Lateral | Connection (same sand body) | Same kind | No | Perfect | 1.0 | |
Lateral cut type or substitution type (different sand body) | Same kind | No | Good | 0.9 | ||
Yes | Medium-good | 0.8 | ||||
Class I and II or Class II and III | Yes | Medium | 0.6 | |||
Lateral | Lateral cut type or substitution type (different sand body) | Class I and II or Class II and III | Yes | Medium-poor | 0.4 | |
Class I and III | No | Medium-poor | 0.4 | |||
Yes | Poor | 0.2 | ||||
Vertical | Cut and stack type | Same Kind | Good | 1.0 | ||
Class I and II or Class II and III | Medium | 0.6 | ||||
Class I and III | Poor | 0.4 | ||||
Section | Fracture Length (m) | Fracture Width (m) | Fracture Height (m) | Fracture Strike (Northeast) | ||
---|---|---|---|---|---|---|
West Flank | East Flank | Overall Length | ||||
The first section | 128 | 46 | 174 | 41 | 76 | 57° |
The second section | 159 | 131 | 290 | 62 | 49 | 61° |
The third section | 220 | 126 | 346 | 58 | 59 | 63° |
The fourth section | 267 | 134 | 401 | 60 | 60 | 66° |
The fifth section | 128 | 96 | 224 | 44 | 43 | 58° |
Well Name | Fracturing Section | Real Displacement Distance (m) | Sand Body Connectivity (Yes/No) | Connectivity Degree Coefficient | Permeability (mD) | Effective Thickness (m) | Displacement Coefficient | Water Injection Section | Displacement Coefficient of Water Injection Section | Layered Injection Ratio | Layered Injection Calculation (m3) |
---|---|---|---|---|---|---|---|---|---|---|---|
B192-101x | 1 | 85 | Yes | 0.9 | 0.23 | 1.56 | 27.4 | Upper section | 199.5 | 0.72 | 14.5 |
2 | 150 | Yes | 0.8 | 0.23 | 4.77 | 131.7 | |||||
3 | 110 | Yes | 0.8 | 0.14 | 3.28 | 40.4 | |||||
4 | 150 | No | 0.16 | 0.91 | 0 | Lower section | 76.6 | 0.28 | 5.5 | ||
5 | 110 | Yes | 1.0 | 0.2 | 3.48 | 76.6 | |||||
6 | 140 | No | 0.16 | 2.98 | 0 | ||||||
B194-101x | 1 | 120 | Yes | 0.8 | 0.25 | 2.71 | 65.0 | Upper section | 261.8 | 0.62 | 12.4 |
2 | 140 | Yes | 1.0 | 0.18 | 5.66 | 142.6 | |||||
3 | 160 | Yes | 0.9 | 0.33 | 1.14 | 54.2 | |||||
4 | 160 | Yes | 0.8 | 0.3 | 4.17 | 160.1 | Lower section | 160.1 | 0.38 | 7.6 | |
5 | 190 | No | 0.24 | 3.29 | 0 | ||||||
B193-100x | 1 | 160 | Yes | 1.0 | 0.19 | 2.15 | 65.4 | Upper section | 228.2 | 0.72 | 14.4 |
2 | 160 | Yes | 1.0 | 0.22 | 3.71 | 130.6 | |||||
3 | 100 | Yes | 0.8 | 0.21 | 1.92 | 32.3 | |||||
4 | 110 | No | 0.24 | 1.79 | 0 | Lower section | 87.7 | 0.28 | 5.6 | ||
5 & 6 | 90 | Yes | 1.0 | 0.22 | 4.43 | 87.7 | |||||
B193-102x | 1 | 150 | Yes | 0.8 | 0.18 | 2.12 | 45.8 | Upper section | 144.4 | 0.63 | 12.7 |
2 | 150 | Yes | 1.0 | 0.18 | 1.24 | 33.5 | |||||
3 | 165 | Yes | 0.8 | 0.21 | 2.35 | 65.1 | |||||
4 | 180 | No | 0.24 | 1.68 | 0 | Lower section | 83.1 | 0.37 | 7.3 | ||
5 | 180 | Yes | 0.9 | 0.18 | 2.85 | 83.1 |
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Li, M.; Qu, Z.; Ji, S.; Bai, L.; Yang, S. A New Methodology for Determination of Layered Injection Allocation in Highly Deviated Wells Drilled in Low-Permeability Reservoirs. Energies 2023, 16, 7764. https://doi.org/10.3390/en16237764
Li M, Qu Z, Ji S, Bai L, Yang S. A New Methodology for Determination of Layered Injection Allocation in Highly Deviated Wells Drilled in Low-Permeability Reservoirs. Energies. 2023; 16(23):7764. https://doi.org/10.3390/en16237764
Chicago/Turabian StyleLi, Mao, Zhan Qu, Songfeng Ji, Lei Bai, and Shasha Yang. 2023. "A New Methodology for Determination of Layered Injection Allocation in Highly Deviated Wells Drilled in Low-Permeability Reservoirs" Energies 16, no. 23: 7764. https://doi.org/10.3390/en16237764
APA StyleLi, M., Qu, Z., Ji, S., Bai, L., & Yang, S. (2023). A New Methodology for Determination of Layered Injection Allocation in Highly Deviated Wells Drilled in Low-Permeability Reservoirs. Energies, 16(23), 7764. https://doi.org/10.3390/en16237764