The problem of distributing gas through a network of pipelines is formulated as a cost minimization subject to nonlinear flow-pressure relations, material balances, and pressure bounds. The solution method is based on piecewise linear approximations of the nonlinear flow-pressure relations. The approximated problem is solved by an extension of the Simplex method. The solution method is tested on real-world data and compared with alternative solution methods.

The gas transmission problem solved by an extension of the simplex algorithm

Clean and environment-friendly energy harvesting are of prime interest today as it is one of the key enablers in achieving the Sustainable Development Goals (SDGs) as well as accelerates social progress and enhances living standards. India, the second-most populous nation with a population of 1.353 billion, is one of the largest consumers of fossil fuels in the world which is responsible for global warming. An ever-increasing population is projected until 2050, and consequently, the energy demand in the upcoming decades will be co-accelerated by the rapid industrial growth. The Ministry of New and Renewable Energy (MNRE) with the support of National Institution for Transforming India (NITI) Aayog is working to achieve the Indian Government's target of attaining 175 GW through renewable energy resources. Many Indian states are currently increasing their renewable energy capacity in an objective to meet future energy demand. The review paper discusses in-depth about the three Indian states, namely Karnataka, Gujarat, Tamil Nadu, which pioneers the renewable energy production in India. The global energy scenario was discussed in detail with Indian contrast. Further, the barriers to the development of renewable energy generation and policies of the Indian government are discussed in detail to promote renewable energy generation throughout India as well as globally since the challenges are similar for other nations. This study analyzed various prospects of the country in renewable energy which has been done in a purpose to help the scholars, researchers, and policymakers of the nation, as it gives an insight into the present renewable energy scenario of the country.

/pdf/a-comprehensive-review-on-renewable-energy-development-1ec239x6fp.pdf

A Comprehensive Review on Renewable Energy Development, Challenges, and Policies of Leading Indian States With an International Perspective

Abstract A heterogeneous magnesium oxide catalyst is a good alternative for homogeneous catalysts for the transesterification of alkyl esters for the production of fine-chemicals as well as for the production of biodiesel. The transesterification of ethyl acetate with methanol was used as a model reaction to simulate fine-chemical production in a batch slurry reactor at industrial conditions. The transesterification of triolein with methanol to methyl oleate was chosen to simulate continuous production of biodiesel from rapeseed oil. A kinetic model based on a three-step ‘Eley–Rideal’ type of mechanism in the liquid phase was used in both process simulations. The transesterification reaction occurs between methanol adsorbed on a magnesium oxide free basic site and ethyl acetate or the glyceride from the liquid phase. Methanol adsorption is assumed to be rate-determining in both processes. Activity coefficients were required to account for the significant non-ideality of the reaction mixture in the simulations of both processes. The simulations indicate that a production of 500 tonnes methyl acetate per year can be reached at ambient temperature in a batch reactor of 10 m 3 containing 5 kg of MgO catalyst, and that a continuous production of 100,000 tonnes of biodiesel per year can be achieved at 323 K in a continuous stirred reactor of 25 m 3 containing 5700 kg of MgO catalyst. Although various assumptions and simplifications were made in these explorative simulations the assumptions concerning the reaction kinetics used, the results indicate that for both processes a heterogeneous magnesium oxide catalyst shows promising potential as a viable industrial scale alternative.

Heterogeneously MgO-catalyzed transesterification for fine-chemical and biodiesel industrial production

Review on transesterification of non-edible sources for biodiesel production with a focus on economic aspects, fuel properties and by-product applications

Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming.

Genetic algorithm has been used in various applications including reserve estimations in oil and gas industry for the last few decades It is an effective stochastic inversion technique for optimization problems The oil and gas industry is a risk based industry due to lot of uncertainties associated in each reservoir parameter used during the reserve estimation process Detailed analysis of input data is very much important, either for the pre-bid evaluation or after the discovery of hydrocarbons In this paper, stochastic approach in hydrocarbon resource estimation has been discussed The algorithm starts with development of initial population and evaluation of the same In the second step a fitness value is assigned to each individual The best fit parents are then selected and by crossover and mutation of new populations are generated The same process is continued until the optimum solution is reached The efficacy of the algorithm is tested on real data set of seismic and petrophysical data from Cambay basin The outcome is a range of resource estimates with various probability values

https://link.springer.com/content/pdf/10.1007%2Fs13202-014-0144-8.pdf

Hydrocarbon resource estimation: a stochastic approach

Resource estimation is a process by which the economically recoverable hydrocarbons (HC) in place within a reservoir are calculated. The parameter that goes into resource estimation is variable and contains some aspect of risks. Resource can be defined as the total volume of hydrocarbon in place, which is further classified as reserve when proved commercially viable. Reserve estimation can be carried out deterministically while resource estimation is done probabilistically. In this paper we have utilized a probabilistic approach using genetic algorithm (GA) for resource estimation. GA is an adaptive heuristic approach which uses an analogy to the mechanism of Darwin’s theory of natural selection. This paper discusses the basic structure of GA and its operators that is, encoding, reproduction and termination, followed by the application of GA to a synthetic model for estimating the oil initially in place (OIIP) and recoverable resource, using triangular and lognormal probability distributions.

	 

	Key words: Genetic algorithm, resource estimation, crossover, mutation.

Application of genetic algorithm to hydrocarbon resource estimation

Digital twin in hydrocarbon industry

Joint Inversion of different geophysical potentials promises to reduce chances of uncertainties which are more probable in case of independent inversions. In this paper, cross-gradient Joint Inversion was applied for gravity, magnetic, and MT data to understand the subsurface geothermal potential in Dholera and Unai, Gujarat, India. This paper narrates the understanding of individual inversion for gravity, magnetic, and MT data followed by joint inversion to delineate more clear understanding of geological and geothermal bodies in the subsurface. To formulate data space, a generalized cross-gradient inversion for data set and model set parameters are discussed. On the basis of the algorithm proposed, an integrated subsurface model for Dholera and Unai was prepared which depicts geological as well as lithological characteristics of the area. For the structural coupling, a Lagrange data multiplier was applied in an iterative manner to reach a global minima. Fluid flow models of Dholera and Unai were prepared where “J” type drilling is suggested up to a depth of 800 m and 700 m, respectively.

Integrated 2D joint inversion models of gravity, magnetic, and MT for geothermal potentials: a case study from Gujarat, India

The Tulamura anticline falls in the state Tripura, Northeast India. The anticline is extended up to neighbour country Bangladesh. The region is characterized by huge anticlines, normal faults and abnormally pressured formations which causes a wide margin of uncertainties in wildcat well planning and design. These geological complexities of Tulamura anticline make the drilling engineers more challenging. Therefore, a proper well design is essential in such a region to prevent blowout. Drilling engineer requires to maintain wellbore pressure between the pore pressure and fracture pressure to reduce the possibility of a kick and a formation damage. Pore pressure plays an important role to design a safe and economical well in such a high pressure and temperature reservoir. For wildcat drilling, only seismic data are available in the study area. There are various methods to predict pore pressure from seismic velocity data. Modified Eaton’s method is widely used for the pore pressure prediction from seismic data in terms of the velocity ratio. Modified Eaton’s equations may cause an error by manual selection of compaction trend line which is used to find normal compaction velocity. The main objectives of this study are to develop a new method to predict pore pressure and safe well design on the top of Tulamura anticline in terms of pore pressure. The new method is validated by a well-known method, modified Eaton’s method, and RFT pressure data from offset wells. An excellent match with pore pressures estimated from RFT pressure data and predicted by new model along with modified Eaton’s method is observed in this research work. The efficiency and accuracy level of the hybrid model is more as compared to other methods as it does not require compaction velocity data; thus, an error caused by manual compaction trend can be eliminated. Pore pressure predicted by new method indicates result up to the 6000 m, which is up to the basement rock. The predicted pore pressures by new method are used as an input to calculate the fracture pressure by Hubbert and Willis method, Mathews and Killy method and modified Eaton’s method. Equivalent mud weight selection is carried out using median line principle with additional 0.3 ppg, 0.3 ppg and 0.2 ppg of swab pressure, surge pressure and safety factor, respectively, for calculation of all casing pipes. Casing setting depths are selected based on pore pressure gradient, fracture pressure gradient and mud weight using graphical method. Here, four types of casing setting depths are selected: conductor, surface, intermediate and production casings at 100 ft, 6050 ft, 15500 ft and 18,500 ft, respectively, by new methods, but the casing setting depths for intermediate are at 13500 ft in the case of modified Eaton’s method. The casing policy is selected based on burst pressure, collapse pressure and tension load. For each casing, kick tolerance in bbl is determined from kick tolerance graph to prevent the blowout. Finally, comparative safe and economical wells are designed on the top of Tulamura anticline along with target depth selection, casing setting depth selection, casing policy selection and kick tolerance in consideration of collapse pressure, burst pressure and tension load which gives a clear picture of well planning on the top of anticline in pore pressure point of view.

/pdf/pre-drill-pore-pressure-prediction-and-safe-well-design-on-2i4ykqk1io.pdf

Anirbid Sircar

Papers

Hydrocarbon resource estimation: a stochastic approach

Application of genetic algorithm to hydrocarbon resource estimation

Digital twin in hydrocarbon industry

Integrated 2D joint inversion models of gravity, magnetic, and MT for geothermal potentials: a case study from Gujarat, India

Pre-drill pore pressure prediction and safe well design on the top of Tulamura anticline, Tripura, India: a comparative study