Shale has ultra-low matrix permeability, and consequently requiring the creation of hydraulic fracturing to maximize the contact area with the reservoir. The key to successful fracturing treatment in shale formation is the identification of the sweet spots. Productive shale consists of quartz, feldspar or carbonate and clays, in addition to organic matter (Kerogen). Thus it is challenging process to map the best zones to fracture and locate horizontal wells. A new Fracturability Index based on mineralogy has been developed to locate the best areas along horizontal wells to fracture. A good Mineralogical Index would prolong production plateau for shale plays.Current technology follow two schools, first one through equally spaced fracturing, and the more fractures the better production, second; guide through brittleness index, which distinguish brittle versus ductile zones along the well path, supporting the second school, we have a better correlation of Fracturability index, the new correlation indicates the most brittle parts of the reservoir (MI>0.6) versus the most ductile parts (MI <0.6), it became easier to map the producing shale with sweet or unattractive spots leading to the effective fracture locations. It is a new sweet spot identifiers, which guide the fracture design and fracture allocation along horizontal wellbore path, it may optimize well placement and hydraulic fracturing positioning in unconventional resources.A new Minerological Index is developed ranging from 0 to 1 which helps optimize the fracturing and shale development, in addition to guaranteeing fracture treatments are in the right place. This will facilitate connectivity with natural fracture network, priority of guiding fracture design start with High FI which corresponds to higher mineralogy index. MI value of 0.6 is a good starting point to map sweet spots. This may enhance far field Fracturing Complexity and help getting a branched fracture. Based on a sub category of quartz, type of treatment may be recommended.

The Wells Placement Problem (WPP) consists in choosing well locations within an oil reservoir grid to maximize the reservoir total oil production, subject to distance threshold between wells and number of wells cap constraints. A popular approach to WPP is Genetic Algorithms (GA). Alternatively, WPP has been approached in the literature through Mathematical Optimization. Here, we conduct a computational study of both methods and compare their solutions and performance. Our results indicate that, while GA can provide near-optimal solutions to instances of WPP, typically Mathematical Optimization provides better solutions within less computational time.

Well optimization is an important factor in field development strategies targeted to maximizing the hydrocarbon recovery, and economic feasibility of new field development projects. Particularly, in view of shortage in new oil field discoveries, maximizing oil production and net present value (NPV) have become critical factors (hereinafter called the "critical factors") in reservoir engineering. As a result, well optimization research has become a separate field in its own merit. Recent attempts by academic and industrial researchers converged on the goal to create efficient well optimization models that can predict strategies for managing the existing oil and gas fields and developing new fields with potential for maximizing the critical factors. Important elements in field development optimization include well type, well placement and scheduling. In the last decade, significant amount of work has been done in the area of well optimization for which both gradient based and gradient-free optimization methods were used. In gradient-based well optimization methods, the derivative of the objective function with respect to the decision variables is sought. In gradient-free optimization, a family of algorithms classified as global or "stochastic" algorithms - such as the genetic algorithm, simulated annealing, and particle swarm optimization - can be employed. Other algorithms such as local or "deterministic" algorithms (e.g. Generalized Pattern Search, and Hook Jeeves Direct Search,) are also useful in these studies. These optimization strategies can be applied individually or as an ensemble of optimization methods to maximize the critical factors in reservoir simulation. In this paper, we review several of the current optimization techniques, and their application to maximize the critical factors. In the process, we address the significance of different methods and highlight their limitations. We discuss as well the challenges associated in extending these methods, and their potential in the future.

The application of Complex Wells (CW) as a component of an optimized field development strategy at single well, sector model and or small scale multi-well level is generally well understood. In contrast, the application, modeling and optimization of a full field strategy inclusive of CW remains a daunting industry challenge. This paper describes a highly successful four-step workflow to manage, assess and model the qualification of a CW optimization strategy at a full field model scale. The paper utilizes a field case to illuminate a CW field wide optimization - extended production plateau, increased production rates and CW completion strategies - approach as compared to single, sector and or multi-well level CW model derived field development decisions. In this paper, complex wells include; Multi-laterals (ML) and Maximum Reservoir Contact wells (MRC) inclusive of down-hole Internal Control Devices (ICD), Equalizers (ICD or equalizers) and or Internal Control Valves (ICV). The workflow starts with reservoir understanding and identifying the need for CW. A sector model is then extracted from the full field model to conduct detailed well level analysis. This is followed by defining well sensitivity cases, that may include a range of field development decision scenarios and a resultant optimization strategy that recommends a combined conventional and CW field optimization approach. From sensitivity cases, the best fit well(s) case scenario is identified. Thereafter, the optimized well(s) strategy is applied to the full field model and evaluated under uncertainty. Detailed analysis is conducted on both the sector and full field models including well location, orientation, placement, length, and target zones for various CW configurations. The resultant development strategy is an optimized full field CW development decision approach including for example; improved sweep efficiency, delayed water breakthrough, improved oil recovery, reduced water handling expense, reduced produced water, extended oil plateau duration and reduced environmental impact. Introduction A field development plan that supports optimized cost, maximize production plateau duration and recovery (maximizing net present value) is a primary target of all studies. Although simply stated, the scale and complexity of most studies presents many challenges to a successful study outcome. For example, the requirement to investigate all possible study decisions and or strategies (e.g., depletion, water injection, etc.), the potential of various well types (i.e., vertical, horizontal, maximum reservoir contact, etc.), the range of potential well completions (e.g., open hole, ICVs, ICDs, etc), alternative optimization tactics (e.g., water injection voidage replacement, well spacing, etc.), and finally the need to consider full study uncertainty analysis and a specific study objective function (e.g., recovery, NPV, etc.). Most attempts to handle the scale and complexity of most projects opt to reduce study complexity by reducing the number of study variables either on the uncertainty or decision side1,2. Such an approach, in our experience, omits critical project decision factors, leading to misleading project decisions and results.

The understanding and accuracy of modeling fluid flow behavior in naturally fractured carbonate reservoir is critical in predicting reservoir sweep efficiency, remaining drilling targets and evaluating field development alternatives. The use of appropriate complex wells design such as Horizontals, (H) Multi-laterals (ML) and completion technology such as equalizers (ICD) or Inflow Control Valves (ICV) are of equal importance. The approach presented in this paper is based on detailed integrated analysis of all available well data including logs, production, pressure transient analysis (PTA), fracture distributions, well flow profiles (PLT) etc, to provide a first-line insight of the fractured reservoir fluid flow mechanism. These first-line insights provide the basis to develop mechanistic or concept reservoir simulation models to fine-tune fluid movement understanding in fractures, reservoir matrix, well types (H, ML) and completion placement to field development strategies. Sector modeling provides further insight to well design in field areas of different rock quality and fracture density. Well type and completion strategy alternatives for each identified field area including intelligent smart well completions are developed and tested in each sector model. The combined developed understanding of fluid flow mechanisms, well type and completion strategy are rolled up and implemented into a full field simulation model, fine tuned through history match and prediction processes. This paper describes the methodology used to study a number of naturally fractured carbonate reservoirs through the integrated "Event Solution"1 study approach. The methodology presented in this paper was applied on a number of large Middle East carbonate fields. The fields studied have naturally fractured reservoirs with two distinct fracture systems. Namely, fracture corridors or clusters and diffuse or layer-bounded fractures. Diffuse fractures are typically horizontal (layer-bounded fractures) inter-connect with the fracture corridors which are normally vertical to sub-vertical. This fracture system combination forms a highly conductive fluid flow and pressure medium which is responsible for observed water movement as well as pressure propagation from the aquifer/injectors into the reservoirs. Background Literature presented some methodologies to numerically simulate fracture corridors and diffuse fractures systems in naturally fractured carbonate reservoirs. Halilu et al.2 presented a method for large fields dominated by clusters of sub-vertical fractures called fracture corridors. In this study, the effective Warren-Root and fracture parameters were adjusted to mimic explicit fracture modeling to represent fractures corridors. The study results showed that fluid flow in these fields is largely influenced by large scale fracture corridors. These large scale fracture corridors were lately named fracture fairways.