Modeling Water-Injection Induced Fractures in Reservoir Simulation

2005 ◽  
Author(s):  
Bernhard Hustedt ◽  
Yuan Qiu ◽  
Dirk Zwarts ◽  
Paul Jacob van den Hoek
Keyword(s):  
Reservoir Simulation ◽  
Water Injection ◽  
Induced Fractures

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

Containment of Water-Injection-Induced Fractures: The Role of Heat Conduction and Thermal Stresses

2020 ◽  
Author(s):  
Jongsoo Hwang ◽  
Shuang Zheng ◽  
Mukul Sharma ◽  
Maria-Magdalena Chiotoroiu ◽  
Torsten Clemens
Keyword(s):  
Heat Conduction ◽  
Thermal Stresses ◽  
Water Injection ◽  
Induced Fractures

Optimizing Recovery for Waterflooding Under Dynamic Induced Fracturing Conditions

2009 ◽  
Vol 12 (05) ◽  
pp. 671-682 ◽  
Author(s):  
Paul J. van den Hoek ◽  
Rashid A. Al-Masfry ◽  
Dirk Zwarts ◽  
Jan-Dirk Jansen ◽  
Bernhard Hustedt ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Water Injection ◽  
Injection Rate ◽  
Reservoir Engineering ◽  
Spot Pattern ◽  
Engineering Parameters ◽  
Induced Fractures ◽  
Modeling Strategy ◽  
Reservoir Simulator ◽  
Fully Coupled

Summary It is well established within the industry that water injection mostly takes place under induced fracturing conditions. Particularly in low-mobility reservoirs, large fractures may be induced during the field life. This paper presents a new modeling strategy that combines fluid flow and fracture growth (fully coupled) within the framework of an existing "standard" reservoir simulator. We demonstrate the coupled simulator by applications to repeated five-spot pattern flood models, addressing various aspects that often play an important role in waterfloods: shortcut of injector and producer, fracture containment to the reservoir layer, and areal and vertical reservoir sweep. We also demonstrate how induced fracture dimensions (length, height) can be very sensitive to typical reservoir engineering parameters, such as fluid mobility, mobility ratio, 3D saturation distribution (in particular, shockfront position), 3D temperature distribution, positions of wells (producers, injectors), and geological details (e.g., layering and faulting). In particular, it is shown that lower overall (time-dependent) reservoir transmissibility will result in larger induced fractures. Finally, it is demonstrated how induced fractures can be taken into account to determine an optimum life-cycle injection rate strategy. The results presented in this paper are expected to also apply to (part of) enhanced-oil-recovery operations (e.g., polymer flooding).

Rigorous Performance Evaluation of Stochastic Optimization for Water Injection Strategies

2021 ◽  
Author(s):  
Nader BuKhamseen ◽  
Ali Saffar ◽  
Marko Maucec
Keyword(s):  
Simulation Model ◽  
Reservoir Simulation ◽  
Water Injection ◽  
Oil Production ◽  
Optimization Techniques ◽  
Evolutionary Strategies ◽  
Stochastic Methods ◽  
Water Production ◽  
Reservoir Simulation Model ◽  
Injection Strategies

Abstract This paper presents an approach to optimize field water injection strategies using stochastic methods under uncertainty. For many fields, voidage replacement was the dictating factor of setting injection strategies. Determining the optimum injection-production ratio (IPR) requires extensive experience taking into consideration all the operational facility constraints. We present the outcome of a study, in which several optimization techniques were used to find the optimum field IPR values and then elaborate on the techniques? strengths and weaknesses. The synthetic reservoir simulation model, with millions of grid blocks and significant numbers of producers and injectors, was divided into seven IPR regions based on a streamline study. Each region was assigned an IPR value with an associated uncertainty interval. An ensemble of fifty probabilistic scenarios was generated by experimental design, using Latin Hypercube sampling of IPR values within tolerance limits. Scenarios were used as the main sampling domain to evaluate a family of optimization engines: population-based methods of artificial intelligence (AI), such as Genetic algorithms and Evolutionary strategies, Bayesian inference using sequential or Markov chain Monte Carlo, and proxy-based optimization. The optimizers were evaluated based on the recommended IPR values that meet the objective of minimizing the water cut by maximizing oil production and minimizing water production. The speed of convergence of the optimization process was also a subject of evaluation. To ensure unbiased sampling of IPR values and to prevent oversampling of boundary extremes, a uniform triangular distribution was designed. The results of the study show a clear improvement of the objective function, compared to the initial sampled cases. As a direct search method, the Evolutionary strategies with covariance matrix adaptation (ES-CMA) yielded the optimum IPR value per region. While examining the effect of applying these IPR values in the reservoir simulation model, a significant reduction of water production from the initial cases without an impact on the oil production was observed. Compared to ESCMA, other optimization methods have dem

Integrated Dynamic Synthesis as Key Success to Sustain Oil Production in Mahakam: Case Water Flooding Project in Handil Field

2021 ◽  
Author(s):  
A. H. Surbakti
Keyword(s):  
Reservoir Simulation ◽  
Water Injection ◽  
Oil Production ◽  
Recovery Factor ◽  
Water Flooding ◽  
Sweet Spot ◽  
Main Zone ◽  
Spot Area ◽  
Dynamic Synthesis ◽  
Main Challenge

The Handil field is located in the Kutai Basin with an anticlinal structure consisting of a vertically stacked reservoirs deposited in a fluvial-deltaic environment. The field has been producing since 1974 under active aquifer drive followed by peripheral water injection which resulting in a high recovery factor of oil production. Cumulative oil production is more than 900 MMbbls and currently the field is still producing at 15000 bopd. The Handil Main zone is the main contributor that accounts for 60% of the Handil Field production and based on the results of new wells drilling, there is still potential of the remaining oil accumulations. Therefore, an integrated subsurface study is needed to further increase recovery in the Handil Main zone. This paper will discuss the process used to locate unswept oil in the high water cut reservoir to extend the water flood project. Waterflooding became an important part of the Handil’s development strategy to maximize oil recovery and to maintain oil reservoir pressure, as more and more fields are matured as part of their production life cycle. The main challenge is to identify area of unsweep oil that are affected by water injection activity. Understanding the reservoir behavior of the water injection sweep characteristic can significantly improve the understanding of the distribution of unswept oil in the reservoir. A robust integrated methodology was developed to identify unswept oil area by integrating Static- dynamic synthesis, 3D static model, production history, reservoir connectivity, recent well logs data and reservoir simulation. Multiple QC of oil sweet spot are done by comparing the sweet spot area of dynamic synthesis with reservoir simulation. Detailed well correlation were performed to identify the optimum water injector placement to improve the recovery factor. The results of the integrated dynamic synthesis are used to identify the sweet spot area and the optimum well injector location that will be used for the water flooding development project to be executed in 2022. The results of the study will sustain Mahakam production in the future.

Optimising Oil Field Net Present Value with Produced Water Salinities and Tracers

2021 ◽  
Author(s):  
Babalola Daramola
Keyword(s):  
Reservoir Simulation ◽  
Produced Water ◽  
Water Injection ◽  
Oil Production ◽  
Oil Field ◽  
Water Flooding ◽  
Water Salinity ◽  
History Match ◽  
Infill Drilling ◽  
Salinity Data

Abstract This paper presents case studies of how produced water salinity data was used to transform the performance of two oil producing fields in Nigeria. Produced water salinity data was used to improve Field B’s reservoir simulation history match, generate infill drilling targets, and reinstate Field C’s oil production. A reservoir simulation study was unable to history match the water cut in 3 production wells in Field B. Water salinity data enabled the asset team to estimate the arrival time of injected sea water at each production well in oil field B. This improved the reservoir simulation history match, increased model confidence, and validated the simulation model for the placement of infill drilling targets. The asset team also gained additional insight on the existing water flood performance, transformed the water flooding strategy, and added 9.6 MMSTB oil reserves. The asset team at Field C was unable to recover oil production from a well after it died suddenly. The team evaluated water salinity data, which suggested scale build up in the well, and completed a bottom-hole camera survey to prove the diagnosis. This justified a scale clean-out workover, and added 5000 barrels per day of oil production. A case study of how injection tracer data was used to characterise a water injection short circuit in Field D is also presented. Methods of using produced water salinity and injection tracer data to manage base production and add significant value to petroleum fields are presented. Produced water salinity and injection tracer data also simplify water injection connectivity evaluations, and can be used to justify test pipeline and test separator installation for data acquisition.

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