Summary An atomic-force microscope (AFM), a relatively new tool for studying surface characterization, can generate image features down to atomic resolution. Not only can the AFM obtain topographic images of surfaces, but it also can simultaneously identify different materials on a surface at high resolution. Since its invention in the 1980s, AFM has been used in material science and medical research, although it has not received the attention that it probably deserves in reservoir engineering. The emergence of unconventional shale-gas reservoirs, however, has opened new research frontiers for the AFM in the field of reservoir engineering. The unique capabilities of the AFM make it ideal for studying nanopores, organic materials (kerogen), minerals, and diagenetic fractures in shales. It also can be used to measure localized bulk modulus of elasticity on a surface for further implications in geophysical exploration and designing hydraulic fracturing. We introduce different AFM techniques for all these applications, along with example results.

As many fields around the world are reaching maturity, the need to develop new tools that allow reservoir engineers to optimize reservoir performance is becoming more urgent. One of the more challenging and important problems along these lines is the well placement optimization problem. In this problem, there are many variables to consider: geological variables like reservoir architecture, permeability and porosity distributions, and fluid contacts; production variables, such as well placement, well number, well type, and production rate; and economic variables like fluid prices and drilling costs. Furthermore, availability of complex well types, such as multilateral wells (MLWs) and maximum reservoir contact (MRC) wells, aggravate this challenge. All these variables, together with reservoir geological uncertainty, make the determination of an optimum development plan for a given field difficult. The objective of this work was to employ an optimization technique that can efficiently address the aforementioned challenges. Based on the success and versatility of Genetic Algorithms (GAs) in problems of high complexity with high dimensionality and nonlinearity, it is used here as the main optimization engine. Both binary GA (bGA) and continuous GA (cGA) were tested in the optimization of well location and design in terms of well type, number of laterals, and well and lateral trajectories in a channelized synthetic model. Both GA variants showed significant improvement over initial solutions but comparisons between the two types showed that the cGA was more robust for the problem under consideration. The cGA was, thereafter, applied to a real field located in the Middle East to investigate its robustness in optimizing well location and design in more complex reservoir models. The model is an upscaled version for an offshore carbonate reservoir, which is mildly heterogeneous with low and high permeability areas scattered over the field. After choosing the optimization technique to achieve our objective, considerable work was performed to study the sensitivity of the different algorithm parameters on converged solutions. Then, multiple optimization runs were performed to obtain a sound development plan for this field. An attempt was made to quantify how solutions were affected by some of the assumptions and preconditioning steps taken during optimization. Finally, an optimization ran was performed on the fine model using optimized solutions from the coarse model. Results showed that the optimum well configuration for the reservoir model at hand can contain five or more laterals; which shows potential for drilling MRC wells. Other studies comparing results from the fine and coarse reservoir models revealed that the best solutions are different between the two models. In general, solutions from different runs had different well designs due to the stochastic nature of the algorithm but some guidance about preferred well locations could be obtained through this process

In a recent PhD dissertation (Darvish 2007), data and modeling are presented from an experiment in which CO2 is injected in a tall vertical core, surrounded by fractures. After injecting CO2 for 22 days, 65% of the oil in place is recovered (and still increasing). However, the modeling with a commercial simulator results in only 12% recovery, despite adjusting parameters. We will discuss inherent flaws in current simulators that impede realistic simulation of CO2 injection in fractured media, even at laboratory scales. These issues include: problems in accounting for phase behavior effects and Fickian diffusion in dualporosity models, and excessive numerical diffusion in all simulators that use the finite difference methods. To improve on these weaknesses, we use a higher-order algorithm based on the combined discontinuous Galerkin, mixed hybrid finite element and discrete fracture techniques. We extend that model here to include a self-consistent model for Fickian diffusion, and time splitting in an improved-implicit-pressure-explicit-composition (IIMPEC) scheme. With this new approach, we can reproduce all experimental results in Darvish (2007). The results further confirm the prospects of CO2 injection for enhanced oil recovery in fractured reservoirs. Introduction The growing awareness and concern regarding the ecological and economic threats posed by global warming have triggered a high interest in the sequestration of CO2, produced by burning of fossil fuels, in either saline aquifers or oil reservoirs. The financial burden of such plans may be greatly alleviated by the growing proof that CO2 injection may significantly improve oil recovery as a secondary or tertiary recovery mechanism in fractured reservoirs. Irrespective of the global warming aspects, recovery in many of the world's largest oil reservoirs is declining, following primary and secondary recovery. CO2 injection is attracting the most new market interest as an enhanced oil recovery (EOR) mechanism and is, for instance, being piloted by the Department of Energy in a number of reservoirs, using geological as well as industrial sources of CO2 (DOE 2008). In light of this, a proper understanding of CO2 injection in oil reservoirs and saline aquifers is essential to both governments and the industry. Governments do not, for instance, currently allow trading of carbon bonds under the Kyoto treaty for CO2 injection in oil reservoirs over concerns of CO2 breakthrough at later times, back into the atmosphere. For the industry, reliable estimates of the incremental oil recovery have to justify the cost of a CO2 supply (capture and/or transport). Due to the exceedingly large scale of field problems, most commercial simulators employ highly simplified models that may or may not capture the essential physics. In particular, there are concerns regarding the accuracy and flexibility of finite difference methods and of dual-porosity models in modeling the fracture-matrix interactions. Fickian diffusion poses further challenges that have not been resolved in dual-porosity models (a literature overview is given in Hoteit and Firoozabadi 2009). To gain further insight in the fundamental physical aspects of CO2 injection in fractured reservoirs, we consider a rich dataset provided by experiments (Darvish 2007), in which CO2 is injected at the top of a tall cylindrical chalk core saturated with live oil and surrounded by fractures. These data provide a unique test-bed for our compositional modeling, which uses higherorder methods for increased numerical accuracy in the computation of the velocity and pressure fields as well as mass transport and handles fractures as discrete 1D elements in a 2D domain algorithm (Hoteit and Firoozabadi 2005, 2006a,b, 2009). We have implemented a new model for Fickian diffusion coefficients, based on irreversible thermodynamics (Leahy-Dios and Firoozabadi 2007), to better model the diffusion in the injection experiment.