# Streamline Simulation Theory And Practice Pdf

File Name: streamline simulation theory and practice .zip

Size: 15469Kb

Published: 22.05.2021

*This paper presents an approach to optimize the recovery factor and sweep efficiency in a waterflooding process by automating the optimum injection rate calculations for water injectors using streamline simulation.*

- Streamline simulation : theory and practice
- Waterflood optimization using an injector producer pair recovery factor, a novel approach
- Akhil Datta-Gupta
- Waterflood optimization using an injector producer pair recovery factor, a novel approach

*Al-Najem, A. Streamline and streamtube methods have been used in fluid flow computations for many years.*

## Streamline simulation : theory and practice

This paper presents an approach to optimize the recovery factor and sweep efficiency in a waterflooding process by automating the optimum injection rate calculations for water injectors using streamline simulation. A streamline simulator is an appropriate tool for modern waterflood management and can be used to determine the dynamic interaction between injector and producer pairs, which will vary over time based on sweep efficiency and operational changes.

A streamline simulator can be used to identify injectors, which are not supporting production and contributing mainly to water producing wells. Streamlines illustrate natural fluid-flow paths in the reservoir, which are based on fluid properties, rock properties, well distribution and well rates across the reservoir. Multiplying this weighting factor by the injection rates determines the new injection rate for each injector. For a well pair water cut that is lower than the average field water cut, the injection rate will be increased and vice versa.

Given a finite volume of injection water, there will be a re-allocating of water from a well pair with a low recovery factor and high water cut and redistributing the water to injectors supporting low water cut producers, thus maximizing the recovery factor and reducing the field water production.

This approach can reduce the water cut and increase the recovery factor and extend the life of the waterflooded oil fields. Streamline SL simulation has been a widely used application for waterflooded fields. The way it approaches the simulation solution makes it one of the most effective complementary tools to finite difference FD flow modeling techniques. Streamline simulations features can particularly help in solving large, geologically complex, heterogeneous systems efficiently Datta-Gupta ; Samier ; Lolomari et al.

The main advantages of SL simulation are 1 the ability to quantify and visualize the reservoir fluid flow based on the effects of rock—fluid properties 2 unique streamline flow information 3 reduced grid effects 4 computationally efficient solutions. Streamlines can provide valuable insight into the dynamics of the fluid flow in the reservoir. They can also display well drainage regions and well allocation factors WAF.

WAF gives the fluid volume interactions between injection and production wells i. Streamline simulation enables engineers to create workflows that identify 1 drainage regions of the wells 2 volume of water injected into the aquifer zone. This unique information has been used in managing waterflood operations, which is one of the most common secondary recovery methods Ghori et al.

It is a known fact that water cycling and poor sweep efficiency are the main concerns in waterflooding projects. The main objective of waterflood optimization is to reduce the water production while maximizing sweep efficiency. Thus, accurate performance prediction of both injectors and producers is crucial to the success of every waterflooded project.

Alhuthali et al. They attempted to maximize the sweep efficiency and delay the water breakthrough time. Their work was based on equalizing the arrival times of waterfronts at all producers. Thiele and Batycky presented a novel approach to optimize waterflood processes using the data derived from streamlines. They calculated IE through stream line simulation and defined IE as a ratio of offset oil production to water injection.

Once IEs are calculated, they re-allocate injection water from low-efficiency to high-efficiency water injectors. They proposed a formula to determine how to re-allocate injection water from low-efficiency well pairs to high-efficiency well pairs. The approach presented by Thiele and Batycky in was based on the injection efficiency of well pairs and it did not consider the water cut of producers. It should be highlighted that both approaches are based on the derived information from streamlines.

This paper proposes a practical and efficient approach for waterflood optimization using SL simulation. This method includes increasing the injection rates in wells with low water cut and also inject more water to zones with poor sweep efficiency. The technique presented here incorporates applied mathematical approaches and practical aspects of reservoir engineering and is suitable for brown fields with water production constraints.

This approach can increase the oil recovery factor by decreasing the water cut and extend the life of the field. In streamline simulation, the 3D fluid-flow equations saturation equations are decoupled into multiple 1D equations that are solved along streamlines Batycky This explains why the streamline simulation is faster compared to conventional FD simulators, in addition to reduced numerical dispersion and grid orientation effects.

Streamlines follow natural fluid particle paths in the reservoir, which are based on fluid properties, rock properties, well distribution and well rates across the reservoir. A key concept in streamline simulation is the time of flight TOF variable. It is the time taken for a neutral particle to move a distance along a streamline. It gives the position of the front at different times. A streamline simulator can be used to determine the dynamic interaction between injector and producer pairs.

In conventional simulators, it is difficult to quantify relationships between injectors and producers but since each streamline is associated with a flow rate, streamline simulation can provide the engineer with unique information, which can be used for:. This information is used to determine optimal water injection rates in a brown oil filed. The workflow will be described in the next section. This paper proposes a practical and efficient workflow for waterflood optimization using streamline simulation.

The average field water cut is the arithmetic average of the well pairs. The workflow, which is used in this study for calculating the optimal water injection rates, is detailed below:. If the sum of new injection rates is higher than the total field injection rate target, calculate the normalization factor and normalize the optimal injection rates,. Adjust the production rate targets of producers proportional to the corresponding new optimal injection rate.

The workflow chart for the waterflood optimization approach, which is presented in this paper, is shown in Fig. We present here a novel formulation, which is based on the derived information from streamlines. Our goal is maximizing the oil recovery factor while minimizing the water production by determining the optimal water injection rates and making the best use of available water using streamline simulation. In our formulation, we have considered both the recovery factor and water cut of the producers for determining the optimum injection rates.

The basic steps are as follows:. A synthetic model was used to illustrate how the optimum water injection rate was determined. The porosity and permeability in this simple model are constant and equal to 0.

There are four water injectors in this synthetic model, but for clarity, the optimum injection rate calculation will be described using injector I4. In Fig. The optimization process for injector I4 is as follows:. The results are given in Table 1. The cumulative oil production for each well pair is calculated. Dividing the cumulative oil production given in Table 2 by the oil volume in the bundle of streamlines connecting a well pair gives us the well pair recovery factor Eq.

The recovery factors and water cuts for each well pair are given in Table 3. The results are summarized in Table 4. The results are plotted in Figs. It is observed that the cumulative oil production is increased from The field cumulative water production for the base and optimum cases are plotted and compared in Fig. This figure indicates that the cumulative water production is decreased from 2. The under-saturated oil 31 API reservoir was discovered in , where 18 production wells and 8 water injection wells were drilled.

Production from the field commenced in with water injection to maintain reservoir pressure near psia. From the histogram for porosity shown in Fig. The mean porosity value is 0. A unimodal distribution is observed with the data range of 0. The mean permeability value is Water handling capacity of the field is operating at its limit and field abandonment has been considered by IOOC the operating company due to high water cut and water facilities constraints.

Having converted the FD simulation model to streamline one Fig. During the optimization runs, the water injectors were controlled by group rates.

The optimizer was applied on developed streamline model to maximize oil recovery while minimizing water production by determining the optimal water injection rates with a finite volume of water.

The optimizer attempts to make the best use of available water, assuming that there are no operational restrictions to re-allocate water to injectors supporting low water cut producers. There are 6 injectors in the field, but for clarity, the optimization process will be described using injector IF This information can be derived from streamlines. It should be mentioned that the last column of this table indicates water being injected into the aquifer by well IF The recovery factors and water cuts are given in Table 6.

From Eqs. The average field recovery factor and the average field water cut are 1. The optimized injection rates can be calculated by Eq. The results are given in Table 7. We repeat the above calculations for other injectors.

The results are given in Table 8. Since the sum of the optimal injection rates Finally, the production rate targets of producers are changed proportional to the corresponding new injection rates. Production rates must be changed accordingly by the same amount of change with the associated injectors. Table 9 gives the new production rate targets. The final results of water injection optimization in S field are plotted in Figs.

The field cumulative oil production and field water cut for both cases are plotted and compared in Figs. It is observed that oil recovery is increased by We have used the net present value NPV to analyze the profitability of the optimization project. NPV is the difference between the present value of cash inflows and the present value of cash outflows over a period of time. A positive NPV indicates that the projected earnings generated by a project or investment exceeds the anticipated costs.

It is assumed that an investment with a positive NPV will be profitable, and an investment with a negative NPV will result in a net loss.

## Waterflood optimization using an injector producer pair recovery factor, a novel approach

The system can't perform the operation now. Try again later. Citations per year. Duplicate citations. The following articles are merged in Scholar.

## Akhil Datta-Gupta

Search this site. Aerosol Sampling PDF. Alep dans la litterature de voyage europeenne pendant la periode ottomane PDF. An Idyl PDF.

*Google, V. Please choose whether or not you want other users to be able to see on your profile that this library is a favorite of yours.*

### Waterflood optimization using an injector producer pair recovery factor, a novel approach

Received: 17 November Accepted: 1 February Main parts of oil and gas reserves are stored in fractured reservoirs. Simulation of multiphase flow in fractured reservoirs requires a large amount of calculations due to the complexity, reservoir scale and heterogeneity of the rock properties. The accuracy and speed of the streamline method for simulating hydrocarbon reservoirs at field scale make it more applicable than conventional Eulerian simulators using finite difference and finite element techniques. Conventional simulators for fractured reservoirs consume a great deal of time and expense and require powerful CPUs like supercomputers. This makes the development of a fast, powerful and precise simulation method of great importance.

Al-Najem, A. Streamline and streamtube methods have been used in fluid flow computations for many years. Early applications for hydrocarbon reservoir simulation were first reported by Fay and Pratts in the s. Streamline-based flow simulation has made significant advances in the last 15 years. Today's simulators are fully three-dimensional and fully compressible and they account for gravity as well as complex well controls. Most recent advances also allow for compositional and thermal displacements. In this paper, we present a comprehensive review of the evolution and advancement of streamline simulation technology.

SPE Member Price USD Streamline Simulation: Theory and Practice presents a systematic exposition of current streamline simulation technology—its foundations, historical precedents, applications, field studies, and limitations. This text book emphasizes the unique features of streamline technology that in many ways complement conventional finite-difference simulation. The book should appeal to a broad audience in petroleum engineering and hydrogeology; it has been designed for use by undergraduate and graduate students, current practitioners, educators, and researchers. Included in the book is a CD-ROM with a working streamline simulator and exercises to provide the reader with hands-on experience with the technology. Raymond Award and has served on numerous industry and professional society committees. Michael J.

In waterflooded reservoirs under active scale management, produced-water samples are routinely collected and analyzed, yielding information on the evolving variations in chemical composition. In this interdisciplinary paper, the analyses of produced-water compositional data from the Miller Field are presented to investigate possible geochemical reactions taking place within the reservoir. The 1D and 2D theoretical model has been developed to test the modeling of barium sulfate precipitation implemented in the streamline simulator FrontSim. A completely 3D streamline simulation study for the Miller Field is presented to evaluate brine flow and mixing processes occurring in the reservoir by use of an available history-matched streamline reservoir-simulation model integrated with produced-water chemical data. Conservative natural tracers were added to the modeled injection water IW , and then the displacement of IW and the behaviors of the produced water in two given production wells were studied further.

In waterflooded reservoirs under active scale management, produced-water samples are routinely collected and analyzed, yielding information on the evolving variations in chemical composition. In this interdisciplinary paper, the analyses of produced-water compositional data from the Miller Field are presented to investigate possible geochemical reactions taking place within the reservoir. The 1D and 2D theoretical model has been developed to test the modeling of barium sulfate precipitation implemented in the streamline simulator FrontSim.

*Datta-Gupta is well known for his contributions to the theory and practice of Streamline Simulation in petroleum reservoir characterization, management and calibration of high resolution geologic models.*

4 comments

### Leave a comment

it’s easy to post a comment