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.

Water injection increases the percentage of recovery by means of providing pressure support and displacing the oil in the heterogeneous porous medium. In such a displacement process, mobility ratio is important for a more efficient displacement of oil by the injected fluid where mobility ratio can be improved using the fluids involving gelling agents, resulting in increased volumetric sweep. While polymers degrade and break up upon experiencing sudden extreme shear stresses and temperatures, polymer macromolecules are forced to flow into narrow channels and pores, molecular scission processes can take place, thus it is of outmost importance to have a strong understanding of use of right type and amount of viscosity reduction agent. For polymer injection, a comparison of xanthan polymer and synthetic polymer mechanisms was conducted. A commercial full-physics reservoir simulator is coupled with a robust optimization and uncertainty tool to run the model where a simplified gel kinetics is assumed to form a microgel with no redox catalyst. Water injection continues over all 6 layers for 450 days, followed by gel system injection for 150 days, in the bottom 2 layers. Water injection is continued to 4 years. The top four layers have higher horizontal permeabilities, and a high-permeability streak is at the bottom of the reservoir to reduce any helpful effects of gravity. Control and uncertainty variables are set to investigate the sensitivity under this process using the coupled optimization and uncertainty tool. Results demonstrate deep penetration of gel and blocking of the high permeability bottom layers. Sensitivity studies indicate the relative merits of biopolymer, xanthan polymer in terms of viscosity effects vs synthetic PAM in terms of resistance factor vs insitu gelation treatments and their crossflow dependence. Adsorption and retention of polymer and gel are permeability dependent. Considering the fact that there is a significant potential for application of gel solutions in the US and throughout the world, this study illustrates the relative advantages of different treatments in terms of viscosity reduction in the same model in a comparative way outlining the significance of each control and uncertainty variable for better management of reservoirs where displacement efficiency is very critical.

The objective of this work is to evaluate the efficacy of empirical models in forecasting oil production in shale reservoirs, bycomparing and analyzing their fit and effectiveness to our dataset. The following three modelswere considered: A Conventional Decline Curve Analysis (CDC), an Unconventional Rate Decline (URD) Approach, and a Logistics Growth Analysis (LGA) method. A comparative study is performed to evaluate the use of Artificial Neural Networks (ANN) for production forecasts and to reinforce the thinking that it is imperative to include physical parameters in mathematical models to predict accurateforecasts. For this project, we used non-linear regression to fit empirical models to the dataset obtained from North Dakota Industrial Commission (NDIC). We evaluated the fit of modelswith the help of coefficient of determination. Physical parameters, such as porosity, saturation, shale volume, etc., and log data from sonic logs, gamma ray logs, etc., were selected as input to the ANN model andwere aided by Analysis of Variances (ANOVA). Amongst the empirical models for shale play, URD method is the most commonly used since it is idealfor fractured reservoirs with extremely low permeability. URD model did fit the cumulative production profiles, but could not accurately fit the monthly production profile. The CRD approach was overallunsuccessful in generating accurate future production profiles. Values forecasted from the ANN show less than 10% error in estimation. The inclusion of physical parameters has proven to be extremely promising in the forecast production from fields that do not have sufficient history for statistical fitting. Through aselection of physical properties from different sources, we have built an ANN model that fits with the production data in wells that have adiverse production history. Our work has shown the importance of including physical parameters into a process that was heretofore seen as a time series regression problem. In general, our new ANN-based method generated the best results.