Growing oil prices coupled with large amounts of residual oil after operating common enhanced oil recovery methods has made using methods with higher operational cost economically feasible.Nitrogen is one of the gases used in both miscible and immiscible gas injection process in oil reservoir. In heterogeneous formations gas tends to breakthrough early in production wells due to overriding, fingering and channeling. Surfactant alternating gas (SAG) injection is one of the methods commonly used to decrease this problem. Foam which is formed on the contact of nitrogen and surfactant increases viscosity of injected gas. This increases the oil–gas contact and sweep efficiency, although adsorption of surfactant on rock surface can causes difficulties and increases costs of process. Many parameters must be considered in design of SAG process. One of the most important parameters is SAG ratio that should be in optimum value to improve the flooding efficiency.In this study, initially the concentration of surfactant was optimized due to minimization of adsorption on rock surface which results in lower cost of surfactant. So, different sodium dodecyl sulfate (SDS) concentrations of 100, 500, 1000, 2000, 3000 and 4000 ppm were used to obtain the optimum concentration at 70 °C and 144.74×105 Pa. A simple, clean and relatively fast spectrophotometric method was used for determination of surfactant which is based on the formation of an ion-pair. Then the effect of surfactant to gas volume ratio on oil recovery in secondary oil recovery process during execution of immiscible surfactant alternating gas injection was examined experimentally. The experiments were performed with sand pack under certain temperature, pressure and constant rate. Experiments were performed with surfactant to gas ratio of 1:1, 1:2, 1:3, 2:1 and 3:1 and 1.2 pore volume injected. Then, comparisons were made between obtained results (SAG) with water flooding, gas flooding and water alternating gas (WAG) processes.This study shows that using the concentration of 1500 ppm of surfactant solution is practical and economical. Results also show that the SAG ratio of 1:1 with 0.2 cm3/min at temperature and pressure of 70 °C and 144.74×105 Pa, has the maximum oil removal efficiency. In this SAG ratio, stable foam was formed and viscous fingering delayed in comparison to other ratios. Finally, the results demonstrated that SAG injection has higher oil recovery in comparison to other displacement methods (water flooding, gas flooding and WAG).

In development phase of a reservoir and drilling the production wells, it is necessary to drill these wells in appropriate points in order to have no interference in their drainage areas, or have minimum value of interference. In this case, we can produce oil/gas in high flow rate; And in the presence of favorite conditions, recovery factor of the reservoir can be increased. The objective of this work is to develop a methodology for one of Iranian oil fields by combining the utilization of two important tools: traditional simulation (using of different property maps) and streamline simulation. The conventional simulation (based on finite difference) is used to do the main reservoir simulation by compositional modeling. All the economic analysis is made based on conventional simulations output. Computation of different property maps by this simulator are used for investigation of oil volume, permeability, porosity, reservoir thickness and finally oil saturation maps of our reservoir. The streamline simulation is a supporting tool employed to give more reliability and to bring up insights about the optimization process and speedup to the process, mainly in the identification of best locations of news wells and drainage volume. Also streamline simulation is used to study the fluid flow pattern in the field and determining the efficiency of infill wells. In this work we use threshold for different property maps of our reservoir in order to find best location of infill wells, then streamline simulation help us to confirm and optimize these locations based on streamline tracing and drainage volume calculations. We defined several scenarios in order to maximize oil production of our reservoir. Results show that by using streamline technique we can improve recovery of reservoir respect to conventional methods. Introduction In December 2003 the investigated field celebrated 39 years of production. The field contains undersaturated oil and is being developed under aquifer drive and has 45 exploration wells. Modern streamline-based reservoir simulators are able to account for actual field conditions such as 3D multiphase flow effects, reservoir heterogeneity, gravity, and changing well conditions. Streamlines provide new flow information (i.e., well connectivity, drainage volumes, and well allocation factors) that cannot be derived from conventional simulation methods (Pallister and Ponting 2000). One of the main advantages of streamline simulation is its ability to display paths of fluid flow. The streamline simulation results substantially have more value as a reservoir management tool when used in conjunction with traditional reservoir engineering techniques such as standard finite difference simulators (Samier and Thiele 2001). There are different ways to calculate (or estimate) drainage area and drainage radius. Well test analysis, use of dimensionless parameters, material balance and volumetric method, Pressure mapping (for streamline tracing) and use of decline curves matching (although it seems not useful in the early of development of the field). In this paper we use streamline simulation for estimating well drainage volume in a multiple well reservoir. By using of this method we can find best location for drilling of new wells and investigate effect of drainage volume of adjacent wells on each others (Hurst 1987). This technique is based on calculating reservoir pressure throughout the field and producing pressure maps over that field. From the pressure mapping, streamlines tracing the path of fluid towards the well can be plotted and drainage areas discerned (Anderson 1991).

In developing phase of a hydrocarbon reservoir and planning for drilling the production wells, it is necessary to drill these wells in an appropriate spacing to achieve maximum economic revenues, during reservoir life span. Traditionally, the optimum well spacing has been determined graphically from a plot of economic return versus spacing among the wells. This paper derives an equation to solve this problem directly without the plot and presents an illustrative example for its application in a recent developed Iranian onshore oil field. Then we optimize achieved function using a genetic algorithm (GA) approach with an initial random population with 20 chromosomes and 100 generation number as main optimization engine in order to finding optimum well spacing versus maximum project NPV. Our procedure show a well-matched results with figures that calculated by project contractor achieving from a trial and error process during defining the field developing scenarios. Introduction At the beginning of a hydrocarbon field development plan, when the field's geometry and the amount of proven, probable and possible reserves has been delineated, one of the most important variables to be decided for proper development are optimum number of wells and spacing among them. Well spacing is generally defined as maximum area of the resource reservoir that can be efficiently and economically drained by one well1. Spacing is accomplished by an order of the Colorado Oil and Gas Conservation Commission (COGCC) for a particular geographic area1. The propose of well spacing is prevent waste, avoid the drilling of unnecessary wells, and protects the rights of reserves owners1. Muskat2discussed the well spacing problem from two points of view: the physical ultimate recovery and the economic ultimate recovery. From the physical standpoint there are a minimum number of wells Wm required to achieve maximum extraction. Increasing the number of wells beyond this number would not increase the ultimate primary extraction. From the economic ultimate recovery standpoint, giving no time limit for a reservoir development project's life, it can be stated, pure theoretically that at one extreme a few wells can drain the whole reservoir, and at the other extreme, an unnecessary high number of wells could effectively drain the reservoir more rapidly, but at a high cost. In either case, the project's economic return would be negatively affected. Between these two extremes there ought to be an optimum number of wells Wo that would yield maximum economic return. This concept applies equally for vertical wells and/or horizontal wells3. Some authors developed graphical methods for estimating proper well spacing. The optimum well spacing has been determined graphically from a plot of economic return versus well spacing as proposed by Muskat2 (1949).Garaicochea and Acuna4 (1978) approach for determining optimum well spacing consists of predicting performance of the reservoir under various spacing schemes graphically. Another possibility for calculating the optimum well spacing is to prepare a cross plot of net present value vs. number of wells using as a parameter oil price as illustrated by Martinez5 (1975). A method to determine the optimum well spacing straightforward without a plot was presented by Tokunaga and Hise6 (1966). This method, however, assumes the production rate of all wells to remain constant over life (no decline). Corrie3 (2001) propose an analytical solution that the well's initial production rate to decline over life of reservoir (immediately after starting production).The method presented in this paper will cover three main production stages (build up, plateau and decline) of a real reservoir for finding proper well spacing. Well spacing principles As mentioned before well spacing has two different features. In this article we want to underline the economic aspect of problem. The problem is one of determining the optimum number of wells to drill and the accurate spacing among them, to get maximum economic profit. The following considerations highlight the subject: