Sandstones in the Lower Cretaceous Lower Goru Formation in the Lower Indus Basin, Pakistan, are important reservoir rocks for oil, gas and gas‐condensates. For this study, nine metres of core from depths of more than 3400m from well X‐1 in the north‐central part of the basin were analysed for major variations in porosity and permeability in two Lower Goru sandstone units referred to as the Basal and Massive Sands. The Lower Goru Basal Sand was deposited in lower shoreface to inner shelf settings at the well location, while the Massive Sand was deposited in a middle to lower shoreface setting. In both units, intervals with moderate to good (> 15%) porosities alternate with intervals with very low porosity (<5%), and similar variations in core permeability were observed. In this paper, the reasons for this reservoir quality variation at well X‐1 are investigated. Specifically, the study addresses the influence of different clay types on reservoir porosity and permeability within the Lower Goru sands and the distribution and impact of hard cements such as calcite and quartz. A range of petrographical data is integrated including thin sections, whole rock and clay XRD results and SEM images, which together provide some insights into the causes of reservoir quality variation and into the paragenetic relationships between the authigenic minerals. Chlorite grain coats are present in the higher‐porosity sandstones and are interpreted to have inhibited the formation of quartz overgrowths. Dissolution of feldspar and volcanic rock fragments in both the Basal and Massive Sands has contributed to an increase in overall porosity at well X‐1. Relatively low porosity intervals in the Massive Sand are associated with the absence of chlorite grain coats and the presence of abundant quartz overgrowths. By contrast, low porosity intervals in the Basal Sand have undergone early poikilotopic calcite cementation. The formation of authigenic illite resulted in a significant decrease in permeability in both the Basal and Massive Sands. Chlorite and kaolinite also reduced the permeability. The chlorite originated mainly from the dissolution of volcanic rock fragments or from precursor depositional berthierine clay. The transformation of K‐feldspar to illite is suggested to be the main reaction responsible for the formation of both authigenic illite and quartz overgrowths in the two reservoir units; the observed pressure solution will also have contributed to development of quartz overgrowths.

Recumbent bicycles have never truly been associated with international cycling. Conventional safety (upright) bicycles have long been at the center of the cycling world, for both sport and transportation. This is despite the fact that recumbent bicycles are faster, more comfortable, and more efficient than the upright bicycles. The aim of this article is to explain the historical and social perspectives that led to the rejection of the recumbent bicycle by utilizing the theory of Social Construction of Technology (SCOT) and Bijker's two power theory, providing a contrast with the adoption of the safety bicycle.

In this paper a theoretical relationship for the effective thermal conductivity of a multiphase transversely isotropic composite system is obtained. The Generalized Self-Consistent Method and simple energy balance principle is employed to derive a more appropriate model. In the derivation, it is assumed that the orientation of fiber within the transversely isotropic composite system is unidirectional and surrounded by two different phases of porous and matrix phase. A combined effect of these three different phases on the effective thermal conductivity of the composite system in transverse direction is studied. The effect of the interfacial contact conductance between the fibers and porous medium is also considered. Results of effective thermal conductivity are plotted against volume fraction and conductance which shows extremely good agreement.

The research presents an effective way for the optimization of one of the enhanced oil recovery mechanism; surfactant-polymer flooding by the application of two stochastic evolutionary algorithms namely Covariance Matrix Adaptation-Evolutionary Strategy (CMAES) and Invasive Weed Optimization (IWO). The optimized parameters include the well placement, time duration for water and chemical flooding, and chemical injection rates in injection wells while net present value (NPV) served as the objective function. Surfactant Polymer (SP) flooding has proved to be an efficient enhanced oil recovery (EOR) mechanism in recent times. Research has been done on the efficiency of SP flooding by optimizing different properties of surfactant and polymer such that the process results in an improved oil recovery. However, these optimizations are based on the sensitivity studies which limit the researchers to search the optimum solution within a specific domain without extensively exhausted the search space. Stochastic techniques, however, showed a way to efficiently optimizes the SP flooding process even with higher number of optimization parameters. Detailed optimization results for several cases considered in this research are presented. Channeled reservoir and fully heterogeneous reservoir are the two reservoir models used for the optimization of SP flooding process. The maximization of NPV for SP flooding using well placement optimization and without well placement optimization is also compared for both reservoirs utilizing CMAES and IWO. Furthermore, all cases are compared with the base case of simple waterflooding. Statistical analysis is done for all the cases for several realizations and the realizations are ranked according to the best, median and worst, based on the NPV values for each case. The consideration of having such a higher number of optimized parameters is to fully evaluate the potential of considered stochastic optimization techniques to converge to a global maximum for NPV as opposed to conventional sensitivity analysis. Results suggested that these stochastic evolutionary algorithms have a potential to be utilized as the optimization tool for field development of reservoirs having higher number of parameters to be optimized. The successful evaluation of considered stochastic evolutionary algorithms (CMAES and IWO) proved that these algorithms can successfully be used for the optimization of field development plan under higher number of parameters to be optimized that cannot be achieved using sensitivity analysis or gradient based optimization algorithms.

Modeling fluid behavior using conventional nodal analysis software is a common practice in the oil and gas industry. However, understanding flow physics helps production engineers to understand the difference between predicted and actual flow behavior. This work presents a methodology applied to a depleting oil and gas field in northern Pakistan. The adopted approach not only helped to overcome vertical lift performance issues in the wellbore, but it also resulted in improved and sustained oil and gas production from the well. Based on these results, wells in the field with vertical lift performance issues were identified and evaluated using the analysis approach presented in this work. Basic petroleum engineering concepts are implemented using a multi-tier approach, and a proposal was outlined to understand the sluggish flow behavior from the well. The analysis approach characterizes the problem as "IPR dominated" or "VLP dominated" flow using the well's historical data and nodal analysis results, identifies the requirement for a new data set, and then operations are planned accordingly. During execution, coil tubing with memory gauges was deployed with a provision to simulate Coil Tubing Gas Lift (CTGL) with single point injection. This arrangement not only resulted in sustained production from the well, but it also provided leverage to gather bottomhole data corresponding to multiple flow parameters during sensitivity analysis. The workflow explains the physics behind oil and gas wells with sluggish liquid production and the inadequacy of conventional nodal analysis software in predicting production rates with certainty. The application of this workflow converted a "sick well" into a "sustained production well," which was previously ruled out for the implementation of ALS techniques during initial screening using conventional nodal analysis software. This novel approach highlighted the "domain of applicability" of conventional nodal analysis software and proposed a detailed workflow for artificial lift candidate selection. This workflow served as the blueprint for the overall evaluation of well productivity in depleting fields with VLP issues.