The oil and gas industries remain an important drive for the world economy. On one hand, global demand for fossil fuels is still rising, and on the other hand, companies face complex investment challenges due to the harsh operational environment of exploration and production activities. Workforce regulations aim to provide a safe and secured working environment. However, exploration and production activities still cause local and global environmental risks such as groundwater contamination, or climate change in broader scale. Analyzing and reporting mechanisms are key performance indicators of sustainable development at the level of oil and gas companies. Obtaining and analyzing required data, nevertheless, seem to be a persistent challenge as to what degree these findings can affect the routine and strategic decisions of the oil and gas companies. In order to enable oil and gas companies to measure and control risks and manage incidents, artificial intelligent technologies in extended monitoring and supervising E&P operations is known to be an efficient prevention strategy. Such tools not only aid in profitability of the oil and gas companies, but also increase awareness of environment and climate change to act more responsibly. In this study, the significances of environmental policies were investigated through interviews with executives and stakeholders, revealing that the implementation of environmental protection policies is affected by the financial stability of the companies, and under severe economic situations, companies seem less enthusiastic in strictly implementing those policies. This paper provides a comprehensive review of emerging technologies in addressing existing and foreseen challenges in sustainable development in oil and gas industries, with the aim of suggesting prime solutions for strategic planning attempts.
Knowledge of pore fluid pressure is essential for safe drilling and efficient reservoir modelling. An accurate estimation of pore pressure allows for more efficient selection of casing points and a reliable mud weight design. Current commonly used methods of pore pressure prediction are based on the difference between a ‘normal trend’ in sonic wave velocity, formation resistivity factor (FRF), or d-exponent (a function of drilling parameters) and the observed value of these parameters in over-pressured zones. The majority of the techniques are based on shale behaviour, which typically exhibits a strong relationship between porosity and pore fluid pressure. However, carbonate rocks are stiffer and may contain over-pressures without any associated influence on porosity. Indeed, the application of common pore pressure prediction methods to carbonate rocks can yield large and potentially dangerous errors, even suggesting absences or decrease in abnormal pressure in zones of high magnitude over-pressure. In some cases, the hypothesises which been in the conventional methods seems to be flawed in some cases where pore pressure decreases by depth. In this research, a new method for effective stress calculation has been obtained using the compressibility attribute of reservoir rocks. In the case of over-pressure generation by undercompaction (as occurs in most clastic over-pressured sequences), pore pressure is dependent on the changes in pore space, which is a function of rock and pore compressibility. In simple terms, pore space decreases while the formation under goes compaction, and this imposes pressure on the fluid which fills the pores. Carbonate reservoirs in two fields in Iran have been investigated to establish pore fluid pressure generation mechanisms, and to attempt new methods for pore pressure prediction in carbonate rocks.
The Abadan Plain Basin is located in the Middle East region which is host to some of the world’s largest oil and gas fields around the Persian Gulf. This basin is a foredeep basin to the southwest of the Zagros Fold-Thrust-Belt, bounded along its northern and eastern margins by the Dezful Embayment. Most of the rocks in this basin have been deposited in a carbonate environment, and existing fractures have made the formations a favourable place for hydrocarbon accumulations. The basin is enriched by oil and, therefore, gas reservoirs are few, and some of the explored reservoirs exhibit significant degrees of overpressure. This paper has compiled several aspects of the Abadan Plain Basin tectonics, structural geology and petroleum systems to provide a better understanding of the opportunities and risks of development activities in this region. In addition to the existing knowledge, this paper provides a basin-wide examination of pore pressure, vertical stress, temperature gradient, and wellbore stability issues.
Knowledge of pore fluid pressure is essential for safe drilling and efficient reservoir modelling. An accurate estimation of pore pressure allows for more efficient selection of casing points and a reliable mud weight design. Current commonly used methods of pore pressure prediction are based on the difference between a ‘normal trend’ in sonic wave velocity, formation resistivity factor (FRF), or d-exponent (a function of drilling parameters) and the observed value of these parameters in over-pressured zones. The majority of the techniques are based on shale behaviour, which typically exhibits a strong relationship between porosity and pore fluid pressure. However, carbonate rocks are stiffer and may contain over-pressures without any associated influence on porosity. Indeed, the application of common pore pressure prediction methods to carbonate rocks can yield large and potentially dangerous errors, even suggesting absences or decrease in abnormal pressure in zones of high magnitude over-pressure. In some cases, the hypothesises which been in the conventional methods seems to be flawed in some cases where pore pressure decreases by depth. In this research, a new method for effective stress calculation has been obtained using the compressibility attribute of reservoir rocks. In the case of over-pressure generation by undercompaction (as occurs in most clastic over-pressured sequences), pore pressure is dependent on the changes in pore space, which is a function of rock and pore compressibility. In simple terms, pore space decreases while the formation under goes compaction, and this imposes pressure on the fluid which fills the pores. A carbonate reservoir in a field in Iran has been investigated to establish pore fluid pressure generation mechanisms, and to attempt new methods for pore pressure prediction in carbonate rocks.
Pore pressure is a key parameter in controlling the well in terms of reservoir fluid pressure. An accurate estimation of pore pressure yields to better mud weight proposition and pressure balance in the bore hole. Current well known methods of pore pressure prediction are mainly based on the differences between the recorded amount and normal trend in sonic wave velocity, formation resistivity factor (FRF), or d-exponent (a function of drilling parameters) in overpressured zone. The majority of the techniques are based on the compaction of specific formation type which need localization or calibration. They occasionally fail to proper response in carbonate reservoirs. In this research, a new method for calculating the pore pressure has been obtained using the compressibility attribute of reservoir rock. In the case of overpressure generation by undercompaction (which is the case in most of the reservoirs), pore pressure is depended on the changes in pore space which is a function of rock and pore compressibility. In a simple way, pore space decreases while the formation undergoes compaction and this imposes pressure on the fluid which fills the pores. Generally, rock compressibility has minor changes over a specific formation, but even this small amount must be considered. Thus, the statistical tools should be used to distribute the compressibility over the formation. Therefore, based on the bulk and pore compressibility achieved from the special core analysis (SCAL) or well logs in one well, the pore pressure in the other locations of a formation could be predicted.
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