Climate researchers have observed that the carbon dioxide (CO 2) concentration in the atmosphere have been growing significantly over the past century. CO 2 from energy represents about global emissions. Because of impermeable cap rocks hydrocarbon reservoirs are able to sequester CO 2. In addition, due to high-demand for oil worldwide, injection of CO 2 is a useful way to enhance oil 2 storage and oil production is vital. Lack of suitable optimization techniques in the past led most multi-objective optimization problems to be tackled in the same way as a single objective optimization issue. However, there are some basic differences between the multi and single objective optimization methods. In this study, by using a nondominated sorting genetic algorithm (NSGA-II) for an oil reservoir, some appropriate scenarios are proposed based on simultaneous gas storage and enhanced oil recovery optimization. The advantages of this method allow us to amend production scenarios after implementing the optimization process, by regarding the variation of economic parameters such as oil price and CO 2 tax. This leads to reduced risks and time duration of making new decisions based on upcoming situations.

Smart Well technology has seen rapid evolution in the last decade because of technological advancements. Proper knowledge of this technology, with some risk mitigating techniques, makes several benefits to an oil company including the capability of drilling longer horizontal and extended reach wells, particularly snake wells with great reduction in cleanup, reservoir management and water and gas breakthrough concerns. Even in some cases unexpected benefits have been discovered from this technology. Sensors are considered to be the heart of smart well completion. There are different types of sensors in the oil industry including wellbore internal sensors and casing external sensors. Some of these sensors are used for wellbore measurement and some for imaging the distribution of reservoir attributes away from the well. In this paper we will investigate all types of sensors either developed sensors and emerging sensors to measure pressure, temperature, flow Rate, noise, phase composition, pH, water cut, gas fraction and capacitance. However commonly used ones are P, T and flow rate sensors. Others are still emerging technologies. It is possible to use permanent sensors named pressure, resistivity, acoustic on outside wall of the casing as well to estimate permeability, porosity, saturation and monitor near wellbore water / flood front encroachment. Although Smart Well technology has shown several benefits but it possesses some challenges regarding their reliability and cost effectiveness and implementation phases. As a result, many companies tend to apply this technology with caution knowing that the cost of a smart completion can easily be three to four times more than a conventional completion. In this paper we present a comprehensive review of state-of-the-art Smart Well technology including all benefits, different types of sensors, challenges, economic consideration and application in fractured reservoir.

Pilot-scale enhanced oil recovery in hydrocarbon field development is often implemented to reduce investment risk due to geological uncertainties. Selection of the pilot area is important, since the result will be extended to the full field. The main challenge in choosing a pilot region is the absence of a systematic and quantitative method. In this paper, we present a novel quantitative and systematic method composed of reservoir-geology and operational-economic criteria where a cluster analysis is utilized as an unsupervised machine learning method. A field of study will be subdivided into pilot candidate areas, and the optimized pilot size is calculated using the economic objective function. Subsequently, the corresponding Covariance (COV) matrix is computed for the simulated 3-D reservoir quality maps in the areas. The areas are optimally clustered to select the dominant cluster. The operational-economic criteria could be applied for decision making as well as the proximity of each area to the center of dominant cluster as a geological-reservoir criterion. Ultimately, the Shannon entropy weighting and the reference ideal method are applied to compute the pilot opportunity index in each area. The proposed method was employed for a pilot study on an oil field in south west Iran.

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