Peripheral water injection efficiency for material balance applications

2017 ◽  
Vol 149 ◽  
pp. 720-739 ◽  
Author(s):  
Leonardo Patacchini
Keyword(s):  
Water Injection ◽  
Material Balance ◽  
Injection Efficiency

scholarly journals A Novel Data-Driven Model for Dynamic Prediction and Optimization of Profile Control in Multilayer Reservoirs

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wei Liu ◽  
Hui Zhao ◽  
Xun Zhong ◽  
Guanglong Sheng ◽  
Meilong Fu ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Parameter Optimization ◽  
Water Injection ◽  
Material Balance ◽  
Data Driven ◽  
Production Data ◽  
Daily Production ◽  
Injection Efficiency ◽  
Profile Control ◽  
Actual Field

Establishing reservoir numerical simulation and profile control optimization methods considering the mechanism of profile control has always been a difficult research problem at home and abroad. In this paper, firstly, a physics-based data-driven model was established on daily production data of injection and production wells following the principle of material balance. Key parameters including transmissibility, control pore volume, water injection allocation factors, and injection efficiency are derived directly from history matched model, and the dominated flow channels could be quantitatively identified. Then, combined with the evaluation results of the plugging ability of the plugging agent, imaginary well nodes are added to the existing interwell relationship to characterize the heterogeneity of interwell-specific parameters. This process performs flow processing along the interwell control units, forming a new and rapid method for simulation and prediction. Lastly, based on the calculated interwell transmissibility, water injection efficiency, and allocation factors, injection wells with low water injection efficiency can be preferentially selected as profile control wells. In addition, taking the production rates, injection rates, and the amount of plugging agent as optimization variables, we established an optimal control mathematical model and realized the parameter optimization method of the profile control. We demonstrated the results of one conceptual model and two indoor experiments to verify the feasibility of the proposed method and completed two actual field applications. Model validation and actual field application show that the proposed method successfully eliminates the complicated geological modeling procedure and the tedious calculation process associated with the profile control treatment in traditional numerical simulation methods. The calculation speed improves tens or hundreds of times, and water channeling paths are accurately identified. Most importantly, this method realizes the overall decision-making of profile control well selection, dynamic production prediction, and parameter optimization of profile control measures quickly and accurately by mainly using the daily production data of wells. The findings of this study can help for better understanding of the optimization design and application of on-site profile control schemes in large-scale oilfields.

scholarly journals New Waterflooding Characteristic Curves Based on Cumulative Water Injection

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhiwang Yuan ◽  
Zhiping Li ◽  
Li Yang ◽  
Yingchun Zhang
Keyword(s):  
Characteristic Curve ◽  
Water Injection ◽  
Material Balance ◽  
Oil Production ◽  
High Water ◽  
Difficult Problem ◽  
Prediction Errors ◽  
High Water Cut Stage ◽  
High Water Cut ◽  
Water Cut

When a conventional waterflooding characteristic curve (WFCC) is used to predict cumulative oil production at a certain stage, the curve depends on the predicted water cut at the predicted cutoff point, but forecasting the water cut is very difficult. For the reservoirs whose pressure is maintained by water injection, based on the water-oil phase seepage theory and the principle of material balance, the equations relating the cumulative oil production and cumulative water injection at the moderately high water cut stage and the ultrahigh water cut stage are derived and termed the Yuan-A and Yuan-B curves, respectively. And then, we theoretically analyze the causes of the prediction errors of cumulative oil production by the Yuan-A curve and give suggestions. In addition, at the ultrahigh water cut stage, the Yuan-B water cut prediction formula is established, which can predict the water cut according to the cumulative water injection and solve the difficult problem of water cut prediction. The application results show Yuan-A and Yuan-B curves are applied to forecast oil production based on cumulative water injection data obtained by the balance of injection and production, avoiding reliance on the water cut forecast and solving the problems of predicting the cumulative oil production of producers or reservoirs that have not yet shown the decline rule. Furthermore, the formulas are simple and convenient, providing certain guiding significance for the prediction of cumulative oil production and water cut for the same reservoir types.

Reservoir Simulation of Variable Bubble-Point Problems

1976 ◽  
Vol 16 (01) ◽  
pp. 10-16 ◽  
Author(s):  
L.K. Thomas ◽  
W.B. Lumpkin ◽  
G.M. Reheis
Keyword(s):  
Reservoir Simulation ◽  
Water Injection ◽  
Material Balance ◽  
Point Problem ◽  
Time Step ◽  
Bubble Point ◽  
Free Gas ◽  
Flow Equations ◽  
Gas Saturation ◽  
Surrounding Areas

Abstract This paper presents the development of a general beta reservoir simulator that will model conventional (fixed bubble-point) problems as well as problems involving a variable bubble point, such as gas injection projects above the original bubble point and water injection projects resulting in a collapsing gas saturation, Provisions are included for allowing the pressure to cross the bubble point with the same relative ease as in a conventional simulator. Example problems are presented to demonstrate the utility of the model for gas and water injection problems. problems Introduction Many reservoir simulation problems involve treating a variable bubble point throughout the reservoir. For example, when gas is injected into an undersaturated reservoir, gas will go into solution, increasing the bubble point of the oil. As this oil moves away from the injector, the bubble point of surrounding areas also may increase point of surrounding areas also may increase because of mixing. During waterfloods of saturated reservoirs, the gas saturations in regions near the injectors frequently reduce to zero at pressures below the original bubble point. Thus, the bubble point will vary areally throughout the field. point will vary areally throughout the field.Recent publications have discussed certain aspects of the variable bubble-point problem. Most of these papers contain only a brief discussion of this problem. Ridings discusses the need for allowing saturation pressure to vary continuously throughout the reservoir as long as there is free gas associated with the oil. In the model presented by Cook et al., free gas saturation is monitored for saturated systems and the bubble point is set equal to the prevailing reservoir pressure when the gas saturation in a cell disappears. Bubble points for undersaturated cells are allowed to change because of the entrance of free gas and mixing. Spilletta et al. assume that a cell that is saturated or undersaturated at the beginning of a time step will remain so throughout the time step. They then solve their saturation equations for water and gas saturations. The bubble point of any undersaturated cell is adjusted to account for nonzero gas saturation, and the water saturation is modified to conserve oil. Steffensen and Sheffield devote their paper to the reservoir simulation of a collapsing gas saturation during waterflooding. In their model, blocks that have a free gas saturation at the beginning of a time step and have zero or negative gas saturations at the end of a time step are detected, and the bubble points for these cells are set equal to the estimated pressure where Sg reduced to zero. Gas saturation for these blocks is set equal to zero and S is set to 1 - S . The oil saturation in adjacent saturated grid blocks is then adjusted so that oil material balance is maintained. Mixing caused by flow between undersaturated blocks is neglected. This paper presents a comprehensive analysis of modeling variable bubble-point problems.* It treats the specific problems of simulating gas injection above the bubble point as well as waterflooding depleted reservoirs. It differs from previously reported work by accounting for the effect of bubble-point change on computed pressure change during a time step. Also, provisions are included that allow the pressure to cross the bubble point with the same relative ease as in a conventions! simulator In regard to waterflooding, mis paper differs from the work of Steffensen and Sheffield in mat it allows for bubble-point changes caused by mixing. DEVELOPMENT OF FLOW EQUATIONS To simulate the variable bubble-point problem, the expansion of the flow equations above the bubble point must include the effects of pressure and bubble point on fluid properties. Also, special consideration must be given to cells passing through the bubble point if both pressure and material-balance errors are to be eliminated. SPEJ P. 10

scholarly journals Injection-production optimization of fault-karst reservoir—considering high-speed non-Darcy effect

2021 ◽  
Author(s):  
Boying Li ◽  
Yuhui Zhou ◽  
Su Li ◽  
Yiping Ye ◽  
Hongfa Liu
Keyword(s):  
Production Rate ◽  
High Speed ◽  
Water Injection ◽  
Material Balance ◽  
Oil Production ◽  
Production Optimization ◽  
Physical Parameters ◽  
Well Pattern ◽  
Oil Water ◽  
Production Dynamics

AbstractFault-karst reservoirs are featured by complex geological characteristics, and accurate and fast simulation of such kind of reservoirs using traditional simulator and simulation methods is pretty hard. Herein, we tried to discrete the complex fault-karst structures into one-dimensional connected units connecting the well, fracture and cave based on reservoir static physical parameters and injection-production dynamics. Two characteristic parameters, conductivity and connected volume, are proposed to characterize the inter-well connectivity and material basis. Meanwhile, the high-speed non-Darcy seepage term is introduced into the material balance equations for well-fracture-cave connected units to describe the actual seepage characteristics within the fault-karst reservoirs, and to better simulate the oil/water production dynamics. Based on this method, a fracture reservoir model of 1 injection-3 production was established. The change of oil–water action law in different injection and extraction systems under two production regimes of fixed production rate and fixed pressure is analyzed. A case study was conducted on S fault zone, where the flow of oil and gas did not follow the Darcy seepage rule and with a β value of 103–104, the single well flow pressure and oil production were perfectly matched with the real data. In addition, connected units with more prominent high-speed non-Darcy features were found to have better connectivity, which might shed light on the more accurate prediction of inter-well connectivity. Moreover, an improved injection-production well pattern and was proposed based on connectivity prediction model and reservoir engineering method to solve the problems of insufficient natural energy supply and overhigh oil production rate in Block S. Furthermore, the injection/production rate as well as the timing and cycle of water injection was predicted and optimized so as to better guide to site operations.

scholarly journals A New Methodology for the Multilayer Tight Oil Reservoir Water Injection Efficiency Evaluation and Real-Time Optimization

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Lin Cao ◽  
Jianlong Xiu ◽  
Hongjie Cheng ◽  
Hui Wang ◽  
Shujian Xie ◽  
...  
Keyword(s):  
Water Injection ◽  
Sedimentary Facies ◽  
Production Performance ◽  
Water Flooding ◽  
Tight Oil ◽  
Oil Reservoir ◽  
Reservoir Water ◽  
Injection Efficiency ◽  
Tight Oil Reservoir ◽  
Channel Bar

It is important to determine the reasonable injection and production rates in the development of multilayer tight oil reservoir with water flooding treatment. Based on the INSIM (interconnection-based numeric simulation model), a connected network model, a new method is designed to evaluate the water injection efficiency of different layers in water flooding reservoirs and to optimize the injection-production system to produce more oil. Based on the types of sedimentary facies and corresponding injection-production data, the interwell connections are divided into four major categories (middle channel, channel edge, middle channel bar, and channel bar edge) and twelve subclasses. This classification standard of interwell connections could help to significantly improve the accuracy of judging the dominant flow path without constructing a complicated geological model. The interaction of interwells such as injection-production correlation and water injection efficiency could be revealed by simulating the production performance and computing the layer dividing coefficient and well dividing coefficient. A numerical example is used to validate this method by comparing results from FrontSim and this method, and the computational efficiency of this method is several dozen times faster than that of the traditional numerical simulation. This method is applied to quickly optimize the production schedule of a tight oil reservoir with the water flooding treatment, that is, the water injection rate of multilayer reservoirs could be optimized subtly by the injection efficiency of different layers, and the target of producing more oil with lower water cut could be achieved.

Monitoring and Improving Water Injection Efficiency in a Structurally Complex Field

2011 ◽  
Author(s):  
Marco Rotondi ◽  
Andrea Binda ◽  
Mohamed Draoui ◽  
Achille Tsoumou ◽  
Loris Tealdi
Keyword(s):  
Water Injection ◽  
Complex Field ◽  
Injection Efficiency

scholarly journals Peramalan Kinerja Lapangan SNP Dengan Injeksi Air Menggunakan Metode Material Balance

2018 ◽  
Vol 1 (3) ◽  
Author(s):  
Andre Albert Sahetapy Engel ◽  
Rachmat Sudibjo ◽  
Muhammad Taufiq Fathaddin
Keyword(s):  
Oil And Gas ◽  
Water Injection ◽  
Material Balance ◽  
Oil And Gas Industry ◽  
Reservoir Pressure ◽  
Injection Wells ◽  
Gas Industry ◽  
Evaluation Result ◽  
Cumulative Production ◽  
The Common

<p>The decline in production from of a field is the common problem in the oil and gas industry. One of the causes of the decrease in production is the decline of reservoir pressure. Based on the analisis result, it was found that SNP field had a weak water drive. The most dominant drive of the field was fluid expansion. In order to reduce the problem, a reservoir pressure maintenance effort was required by injecting water. In this research, the effect of water injection to reservoir pressure and cumulative production was analyzed. From the evaluation result, it was found that the existing inejection performance using one injection well to Zones A and B was not optimum. Because, the recovery factor was predicted to 29.11% only.By activating the four injection wells, the recoverty factor could be increase to 31.43%.</p>

Subsurface Back Allocation: Calculating Production and Injection Allocation by Layer in a Multilayered Waterflood Using a Combination of Machine Learning and Reservoir Physics

2021 ◽  
Author(s):  
Javad Rafiee ◽  
Carlos Mario Calad Serrano ◽  
Pallav Sarma ◽  
Sebatian Plotno ◽  
Fernando Gutierrez
Keyword(s):  
Machine Learning ◽  
Water Injection ◽  
Material Balance ◽  
Data Entry ◽  
Phase Match ◽  
Infill Drilling ◽  
Industry Practice ◽  
Physics Model ◽  
Probabilistic Nature

Abstract Allocation of injection and production by layer is required for several production and reservoir engineering workflows including reserves estimation, water injection conformance, identification of workover and infill drilling candidates, etc. In cases of commingled production, allocation to layers is unknown; running production logging tools is expensive and not always possible. The current industry practice utilizes simplified approaches such as K*H based allocation which provides a static and inaccurate approximation of the allocation factors; this manual approach requires trial and error and can take several weeks in complex fields. This paper presents a novel technique to solve this problem using a combination of reservoir physics and machine learning. The methodology is made up of four stages: Data Entry: includes production at well level (commingled), injection at layer level and injection patterns or a connectivity map (optional) Gross Match: in order to match gross production for each well, the tool solves for time-varying layer-level injection allocation factors using a total material balance equation across all wells. Phase Match: having the allocation factors from the previous step, the tool automatically tunes various petrophysical parameters (i.e. porosity, relative permeability, etc.) in the physics model for each injector-producer pair across all the connected layers to match the oil and water production in each producer. An ensemble of several models can be run simultaneously to account for the probabilistic nature of the problem. Output: The steps 2 and 3 can be performed at pattern level for all connected patterns or for the whole field. The application of the technology in a complex field with 80+ layers in Southern Argentina is demonstrated as a case study of the benefits of the adoption of the technology.

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