Smart field technologies offer outstanding capabilities that increase the efficiency of the oil and gas fields by means of saving time and energy as far as the technologies employed and workforce concerned given that the technology applied is economic for the field of concern. Despite significant acceptance of smart field concept in the industry, there is still ambiguity not only on the incremental benefits but also the criteria and conditions of applicability technical and economic-wise. This study outlines the past, present and the dynamics of the smart oilfield concept, the techniques and methods it bears and employs, technical challenges in the application while addressing the concerns of the oil and gas industry professionals on the use of such technologies in a comprehensive way. History of smart/intelligent oilfield development, types of technologies used currently in it and those imbibed from other industries are comprehensively reviewed in this paper. In addition, this review takes into account the robustness, applicability and incremental benefits these technologie bring to different types of oilfields under current economic conditions. Real field applications are illustrated with applications in different parts of the world with challenges, advantages and drawbacks discussed and summarized that lead to conclusions on the criteria of application of smart field technologies in an individual field. Intelligent or Smart field concept has proven itself as a promising area and found vast amount of application in oil and gas fields throughout the world. The key in smart oilfield applications is the suitability of an individual case for such technology in terms of technical and economic aspects. This study outlines the key criteria in the success of smart oilfield applications in a given field that will serve for the future decisions as a comprehensive and collective review of all the aspects of the employed techniques and their usability in specific cases. Even though there are publications on certain examples of smart oilfield technologies, a comprehensive review that not only outlines all the key elements in one study but also deducts lessons from the real field applications that will shed light on the utilization of the methods in the future applications has been missing, this study will fill this gap.

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Conformance improvement is the key to success in most enhanced oil recovery (EOR) processes including CO2 flooding and steamflooding. In spite of technical and economic limitations, foam has been used as dispersions of microgas bubbles in the reservoir to enhance mobility. Steam-foam has numerous applications in the industry, including heavy oil reservoirs, which are a significant part of the future energy supply. Steam-foam applications have been used to prevent steam channeling and steam override, thus improving overall sweep efficiency, in both continuous steam and cyclic steam injection processes. The objective of this study is to investigate the key components of this complex process, where relatively high temperatures are recorded, in order to have a robust understanding of chemistry and the thermal stability of surfactants. The efficiency and therefore economics of the steam-foam process are strongly reliant on surfactant adsorption and retention. This requires a good understanding of the process for effective sizing of the foam injected. In this study, a commercial reservoir simulator is used where surfactant transport is modeled with surfactant availability and is determined by a combination of surfactant adsorption, surfactant thermal decomposition, and oil partitioning due to temperature. The degree of mobility decrease is interpolated as a result of factors that contain aqueous surfactant kind and concentration, the presence of an oil phase, and the capillary number. An empirical foam modeling method is employed with foam mobility decrease treated by means of modified gas relative permeability curves. The simulation results outline the sensitivity of these parameters and controlling agents, providing a better understanding of the influence of surfactant adsorption and thus, a number of chemicals to be used in an efficient manner. Optimum values for decision parameters that we have control on have been determined by coupling a commercial optimization software with the reservoir simulator. Uncertainty parameters such as surfactant adsorption have been analyzed in terms of significance on the recovery process. Even though steamflooding is thoroughly studied in the literature, there is no recent in-depth study that not only investigates the decision parameters but also uncertainty variables via a robust coupling of a reservoir simulator and an optimization/uncertainty software that model use of foam in steamflooding. This study aims to fill this gap by outlining the optimization workflow, the comparison of parameters with tornado charts and providing useful information for the industry.

Petroleum in general is found in sub-surface reservoir formation amongst pores existent in the formation. For several years due to lack of information regarding production and technology, free-flowing, low viscosity oil has been produced known as conventional crude oil. Fortunately, in recent times, due to advancement of technology, high viscosity with higher Sulphur content-based crude has been produced known as heavy oil. There are also exists significant difference in volatile materials as well as processing techniques used for the two types of crude. (IEA, 2005; Ancheyta et al., 2007). The oil viscosity is a huge problem in regard to heavy oil as both recovery and processing charges increase proportional to Sulphur content and viscosity of the crude. Heavy Oil can be used by definition internationally to describe oil with high viscosity (Although the Oxford dictionary might have several variations of the same, within the contents of this paper, we refer to heavy oil as high viscosity crude). Heavy oil generally contains a lower proportion of volatile constituents and larger proportion of high molecular weight constituents as compared to conventional crude oil (often referred to as light oil, we shall describe the characteristics of the types of oil further in the introduction). The heavy oil just doesn't contain a composition of paraffins and asphaltenes but also contains higher traces of wax and resins in its composition. These components have larger molecular structures leading to high melting and pour points. This makes the oil a bad candidate for flow profiles and adversely affects the mobility of the crude. (Speight, 2016). It is crucial to know the heavy oil constitution as it affects: Recovery: Low viscosity and high melting pointsProcessing: Higher Resin, Sulphur and aromatic contentTransportation: Low Viscosity These all together impact the economics related to E&P (Exploration and Production) of heavy oil resources. These resources generally have a higher of production associated with them and are one of the first candidates to be affected by reduction of crude prices as seen in 2014 and early 2015. Crude oil can generally be classified into its types by using its API values that are generally obtained through lab testing. Table B1 provides a few popular crude types and their associated API Values.

Electrical resistance heating provides key advantages over other thermal recovery methods in the recovery of heavy oil resources. These advantages include low upfront capital expenses, more control on the delivery of the heat spatially, easiness of permitting in environmentally sensitive areas as well as environmental and economic benefits due to lower carbon footprint. However, the recovery efficiency is relatively lower compared to more conventional methods such as CSS, steamflood and SAGD processes as it doesn't introduce a (pressure) drive mechanism and radius of impact is relatively small which may result in marginal economics.1 In this study, the application of electrical resistance heating on multilateral wells are studied in order to illustrate the enhanced physical and economic benefits of the method with the multilaterals.2 A comprehensive review of the technology with all the technical and economic details on the deployment of the electrical resistance heater is provided. A full-physics commercial reservoir simulator is utilized to model a benchmark model and it is coupled with a robust optimization and uncertainty tool to investigate the significance of the control and uncertainty variables in the system. Propagation of the heat, increased the radius of impact, production performance, energy input and economics are outlined in comparison to the base case where the horizontal well is modeled without the extra laterals. Production engineering and deployment aspects are all provided in detail, as well. Utilization of electrical resistance heaters on multilateral wells provides improved economics due to the increased recovery with the additional accessible reservoir volume for heating with the reduced cost of the additional laterals as opposed to the major cost of the main wellbore. The improved unit cost for the heater per foot also helps the economics, thus increased the radius of impact translates into better recovery at lower unit costs. Model inputs as well as the results including the production performances, significance of key parameters and economics, are outlined in a comparative manner. Electrical resistance heating is not a new process but has recently gained more attention due to the advances in the materials used providing better durability, however, the recovery process needs special designs that bring down the unit cost to make the projects feasible. This study provides a new approach in improving recovery in electrical resistance heating methods that may help to turn several potential marginal projects into projects with more favorable economics in a method which has a great potential in an industry becoming more environmentally sensitive.