As major oil and gas companies have been investing in greener energy resources, even though has been part of the oil and gas industry for long time, it has gained its popularity back. Oil and gas industry has adapted to the wind of change and has started investing in and utilizing the technologies significantly. In this perspective, this study investigates and outlines the latest advances, technologies in artificial lift methods as it is applied to unconventionals in a comprehensive manner to serve as an industry-reference for petroleum industry. In order to optimize production of oil and gas from unconventional wells, artificial lift (AL) is found to be the most effective method of production. However, as the well passes through its production lifespan, the reservoir pressure declines and artificial lifting of hydrocarbon fluids remains a serious problem. Identifying the best approach, the kind of AL, ideal time and settings to mount the AL as the well passes through its production lifespan is a major challenge. Several investigations have been carried out in the past to identify and establish ground-breaking techniques to improve the application of AL in tight reservoirs. The aim of this study is to conduct a comprehensive review of recent developments in the field of AL and its applications. A review of the application of machine learning (ML) is also presented in this study. A comprehensive literature review focusing on the recent developments and findings in artificial lift along with the availability and locations are outlined and discussed under the current dynamics of the oil and gas developments. Literature review includes a broad spectrum that spans from technical petroleum literature with very comprehensive research using onepetro and other databases to other renowned resources including journals and other publications. The information and data are summarized and outlined. This study also provides the techniques on optimziation of artificial lift methods in addition to their comprehensive details with a comparative approach. Aritifical lift is a critical component of any oil and gas well especially in unconventionals. The key artificial lift selection and optimziation is through a robust design and established reservoir and well management practices and reflecting the production physics and well dynamics in all aspects. This study outlines the key criteria in the success of aritifical lift applications in individual examples using different methods 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. Among the few existing studies that explain the methods of aritficial lift are up-to-date and the existing studies within SPE domain focus on certain methods. This study closes the gap and aims to serve as a comprehensive single-source reference for artificial lift and its optimization in unconventionals including all traditional and relatively-new methods.

Objectives/Scope Coalbed methane (CBM) has become an important source of clean energy in the recent decades worldwide including the US, China, Australia, India and Russia with more than 60 countries having different degrees of promising coal reserves. CBM reservoirs are distinguished from conventional reservoirs due to the major difference in the mechanism of gas storage and production of water. In CBM reservoirs, pores act as the major storage mechanism as gas is trapped and stored there and produced by means of dewatering and thus lowering the reservoir pressure. Free gas forms as the pressure is lowered leading to increased gas permeability of coal and thus increasing recovery. Microbial activity and thermal maturation of organic compounds are the main mechanisms of methane generation in lower-and-higher rank coals, respectively. Even though methane is an abundant and clean energy source, there are certain operational, technical and economic challenges involved in its production due its unique nature outlined above. Thus, a strong understanding of the parameters and uncertainties that influence the recovery is crucial. Methods, Procedures, Process Due to the fact that the organic materials that make up coals generally have a stronger affinity for CO2 than for methane, CO2 is used as an enhanced recovery method to displace methane as an enhanced coalbed methane recovery (ECBM) method. While there is no current comprehensive optimization study on the effects of such factors, ECBM has a very significant role in the future of energy as it means more energy out of natural gas while eliminating the adverse effects of greenhouse gases. Results, Observations, Conclusions In this study, a standard SPE reservoir simulation model is used to study the factors influencing the recovery in coal bed methane reservoirs by investigating the significance of parameters including but not limited to porosity, adsorption capacity, fracture permeability along with coal density and irreducible water saturation. Novel/Additive Information The optimization results obtained by means of coupling a full-physics commercial numerical reservoir simulator with an optimization/uncertainty tool are presented outlining the different degrees of significance of these factors on production and ultimate recovery for better understanding of the phenomenon that will lead to more robust reservoir management decisions.

Hamaca project is a large resource base producing Extra Heavy Crude Oil from the Orinoco belt of Venezuela. The project is operated by Petrolera Ameriven, an operating agent owned by ConocoPhillips, ChevronTexaco, and Petróleos de Venezuela, S.A. (PDVSA). Primary production started in September 2001 from a few horizontal wells; development drilling will continue over the next 34 years to maintain a production plateau of 190,000 bopd of cold production. The subsurface challenges involve building a representative reservoir model on a very fine scale to honor a very heterogeneous system over a large area, wide variations in temperature across the field and their effect on oil viscosities, vertical and lateral variations in oil properties and fluid pressure, upscaling the model sufficiently to be capable of running the dynamic model on available computer hardware and software. The model is calibrated using the short Hamaca production history (less than 2 years), and is then used to predict the performance of development scenarios containing hundreds of horizontal wells to be drilled in the future. The paper discusses the challenges of selecting suitable modeling methodologies, of matching the limited historical production data, of modeling the influence of production in adjoining fields, of populating the model with hundreds of wellpaths for future horizontal wells, and of conducting sensitivity analyses to evaluate the effect on production forecasts of uncertainties in some key reservoir parameters. Introduction Hamaca project produces Extra Heavy Crude Oil (EHCO) from the Orinoco Heavy Oil Belt of Venezuela (Figure 1). The project is operated by Petrolera Ameriven, an operating agent on behalf of ConocoPhillips (40%), ChevronTexaco (30%) and PDVSA, Venezuelan National Oil Company (30%). Production began in September 2001, and development drilling will continue throughout the life of the project. Full-scale production of 190,000 bopd (cold production) will commence upon completion of the heavy oil upgrader facility. Heavy oil from the field is currently blended with a lighter crude to facilitate pipeline transport. When full-scale production commences, the heavy oil will be blended with a diluent (naptha) and transported about 200 km through pipelines to an upgrader complex on the coast of Venezuela. The upgrader will reclaim the naptha, which will be piped back to dilute future oil production. The reservoir engineering for this project is challenging. It is necessary to build a very large earth model that includes small areas with considerable data and large areas with very little data, upscale it for flow simulation, fine tune the model with limited production history, and then forecast extended future production. The model must be frequently updated to accommodate an aggressive drilling campaign and the influx of initial production data. The challenges are heightened by the need to quantify risks in long-range forecasts, based on limited subsurface and initial production data, which will affect billion-dollar decisions. Since flow simulation is only as good as the data that goes into it, efforts are made to ensure that the model captures and realistically represents the uniqueness of the reservoir and the uncertainties present in the reservoir characterization. Yet the model must remain small enough to be executed on the available computer hardware and software. Starting from the static earth model, components such as model gridding, data preparation, generation of future well traces, PVT, and SCAL were brought into the flow-simulation model in a practical way, mimicking the known reservoir behavior and incorporating the advice of owner company experts and others with operating experience in the Orinoco heavy oil belt. At each step of the way we paused to evaluate the progress made and to ensure that we were on the right track. Reservoir Description Hamaca is only a small segment of the giant Orinoco Heavy Oil belt that extends several hundred miles east to west in the eastern section of Venezuela (Figure 1), yet the Hamaca operating area in itself is quite large. It covers an area of about 160,000 acres (657 sq. km) and contains over 40 billion barrels of oil. Over 100 vertical control wells and over 170 horizontal wells were completed by November 2003. Some of these have established world records for length of slotted liner.

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Intelligent digital oilfield (iDOF) operations include the transfer, monitoring, visualization, analysis, and interpretation of real-time data. Enabling this process requires a significant investment to upgrade surface, subsurface, and well instrumentation and also the installation of a sophisticated infrastructure for data transmission and visualization. Once upgraded, the system then has the capability to transfer massive quantities of data, converting it into real information at the right time. The transformation of raw data into information is achieved through intelligent, automated work processes, which are referred to here as "smart flows," which assist engineers in their daily well surveillance activities, helping make them more productive and improve decision making. A major oil and gas operator in the Middle East has invested in such an infrastructure and is developing a set of smart flows for key activities and workflows for its production operations, with the ultimate goal of improved asset performance. The Sabriyah Mauddud limestone of north Kuwait is a giant depletion-drive oil reservoir currently under the waterflooding process and is being monitored as part of this iDOF operation. A "smart flow" has been developed for visualization and analysis of the waterflooding performance that uses real-time production data to derive and update daily water saturation and reservoir pressure from numerical simulation. The ultimate goal of the smart flow is to identify and define actions to improve water injection and production over the long term. This paper explains how this smart flow works-how it automatically runs a reservoir simulator to estimate water saturation and reservoir pressure and iteratively calculates the waterflooding indicators to provide advice to change the water injection on a monthly basis.