During the past ten years, unconventional hydrocarbon resources have been receiving more attention, as a source of energy. Among these unconventional resources one is the coal bed methane (CBM). To find out the feasibility of the unconventional reservoir development, reserves is estimated and the economic analysis is carried out. The methods for the estimation of reserves requires the initial gas in place calculations, however, the methods describing the IGIP estimation using conventional method cannot be applied to CBM reservoirs without modifications. This paper presents critical literature review of CBM material balance that identifies and examines challenges encountered in CBM recovery. The paper addresses the methods for the estimation of IGIP for Coal Bed Methane and included the review of the available techniques using volumetric, simulation and performance method i.e. decline curve and material balance. Also, the possible causes of error in calculation of IGIP and the limitations of each method have been identified. Additionally, the comparison between the material balance method proposed by King et al., Jenson and Smith et al., Seidel et al., Ahmed et al., Firanda et al. is conducted using available data set. The results obtained shows that Firanda et al. is more realistic as compared to other discussed methods, as it considers matrix and cleat shrinkage, moisture expansion, gas expansion, connate water expansion, water expansion, and water influx.

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Nanotechnology provides a wide variety of methods to resolve industrial issues, which could not be addressed previously using customary methods. It helps enable researchers to alter properties of bulk materials at the nanometer scale. Various nanomaterials have been successfully applied in many areas of petroleum engineering, particularly in drilling fluids, lost circulation, enhanced oil recovery (EOR), and cementing. This study examines the mechanical and microstructural properties of oil well cement with nanozeolite. During this research, API Class G cement was used with various concentrations of nanozeolite. Compressive strength development of Class G cement, with and without nanozeolite, was studied using an ultrasonic cement analyzer (UCA) for 24 hours under high-pressure and high-temperature (HP/HT) conditions. The porosity and permeability of set Class G cement admixed with nanozeolite was also analyzed in an automated permeameter/porosimeter after 24 hours of curing. Microstructural examination of cement samples was performed using scanning electron microscopy (SEM). Three important parameters during well cementing operations included time to achieve 50- and 500-psi compressive strength and time to achieve 2,000-psi compressive strength. These parameters were significantly altered by adding a small percentage of nanozeolite to the neat Class G cement. The addition of nanozeolite resulted in a decrease in transition time and accelerated achievement of 2,000-psi strength. Furthermore, the porosity and permeability of the set Class G cement specimens with nanozeolite decreased substantially, thus indicating a dense microstructure of the matrix. This was confirmed by microstructural investigations using SEM. Nanozeolite is nonhazardous, nontoxic and is compatible with API Class G cement. Nanozeolite can be an effective oil well cement additive because it enhances early strength, and the final compressive strength helps improve cement durability. The accelerated compressive strength development can help decrease wait-on-cement (WOC) time, thus lowering operation costs. Additionally, denser microstructure can help restrain the invasion of corrosive formation fluids.

Hydraulic fracturing is widely used to exploit the tight and low permeability oil and gas reservoirs. With recent technological advances, horizontal wells, along with Multi Stage Fracturing (MSF) have come up with a great effective combined solution for tight reservoirs. The combined technologies overcome formation damage, increase reservoir contact, enhances productivity from fractures; hence make tight reservoirs development economical.On the other hand, drilling a horizontal well with placing multi stage fractures along its lateral is an expensive job. The feasibility of this job should be done to analyze the productivity enhancement provided by the Multi Stage Fracturing before commencing drilling operation. This work developed a simple, easy to use empirical correlation to calculate oil productivity enhancement in terms of fold of increase (FOI) for Pre-Frac analysis. In this paper, an approach has been put forward to evaluate the FOI in Multi Stage Fractured system for horizontal wells in the form of a correlation. The approach used is based on two 3-D simulation modelling scenarios; one with hydraulic fractures and the other without. The fracture case has been run for a wide range of parameters like number of fracture stages, formation permeability, fracture spacing, fracture conductivity and fracture half length, while the other case was run with changing formation permeability only, which served as the base case. The productivity index for both cases corresponding to same formation permeability was used to compute FOI for all different scenarios.A general empirical correlation for estimation of increase in oil productivity due to multi-stage fracturing in horizontal wells is derived through the use of multivariate regression. Correlation is obtained by 70% data points with the accuracy results of 5.20%, 0.54% and 7.79 Mean absolute percentage error, Mean percentage error and standard deviation respectively. The obtained correlation is tested against 30% of unused data points and found to generate acceptable results with 5.20%, 0.50% and 7.40 Mean absolute percentage error, Mean percentage error and standard deviation respectively.The developed correlation is accurate enough to capture even the minor changes in flow inside wellbore as well as interference between adjacent fracture stages. It will serve as a handy tool for pre analysis of fracture performance as compared to the costly and time consuming analytical or simulation models.

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