Showing papers by "Abu Dhabi Company for Onshore Oil Operations published in 2013"

Thermodynamic and transport property models for carbon capture and sequestration (CCS) processes with emphasis on CO2 transport

Abstract: Carbon capture and sequestration (CCS) is one of the most promising technologies for the reduction of carbon dioxide (CO 2 ) concentration in the atmosphere, so that global warming can be controlled and eventually eliminated. A crucial part in the CCS process design is the model that is used to calculate the physical properties (thermodynamic, transport etc.) of pure CO 2 and CO 2 mixtures with other components. In this work, an overview of various thermodynamic models together with calculations from cubic and higher order equations of state (EoS) are provided. Calculations are compared to experimental data and a discussion on the accuracy of the models is given. The CO 2 mixture properties studied include phase equilibria, density, isothermal compressibility, speed of sound, and Joule–Thomson inversion curve. The Peng–Robinson, Soave–Redlich–Kwong, and the Perturbed Chain-Statistical Associating Fluid Theory (PC-SAFT) are the EoS used for the calculations. In addition, various models for transport properties are discussed and calculations for viscosity and diffusion coefficient are presented.

Reservoir parameters determination using artificial neural networks: Ras Fanar field, Gulf of Suez, Egypt

Abstract: Ras Fanar field is one of the largest oil-bearing carbonate reservoirs in the Gulf of Suez. The field produces from the Middle Miocene Nullipore carbonate reservoir, which consists mainly of algal-rich dolomite and dolomitic limestone rocks, and range in thickness between 400 and 980 ft. All porosity types within the Nullipore rocks have been modified by diagenetic processes such as dolomitization, leaching, and cementation; hence, the difficulty arise in the accurate determination of certain petrophysical parameters, such as porosity and permeability, using logging data only. In this study, artificial neural networks (ANN) are used to estimate and predict the most important petrophysical parameters of Nullipore reservoir based on well logging data and available core plug analyses. The different petrophysical parameters are first calculated from conventional logging and measured core analyses. It is found that pore spaces are uniform all over the reservoirs (17–23%), while hydrocarbon content constitutes more than 55% and represented mainly by oil with little saturations of secondary gasses. A regular regression analysis is carried out over the calculated and measured parameters, especially porosity and permeability. Fair to good correlation (R 80%) indicating that good matching and correlation is achieved between the measured and predicted parameters. This well-learned artificial neural network can be further applied as a predictive module in other wells in Ras Fanar field where core data are unavailable.