Iranian Journal of Oil and Gas Science and Technology

Abstract The oil industry is looking for a way to develop reservoir management and optimal production of hydrocarbon reservoirs. The use of advanced technologies in the extraction of oil and gas reserves is essential in advancing the short-term and long-term goals of this industry, both in terms of product type and process. A technology road map is a plan that implements short-term and long-term goals using technology solutions to help achieve the goals. The technology road map for enhanced oil recovery (EOR)/improved oil recovery (IOR) of oil fields has been developed based on the emphasized fields and areas of the target technology. It has been expressed in 10 years according to the existing challenges and preventive measures, and all research and executive activities will be carried out with a focus on the road map. In this research, using the case study research method, by studying nine cases of research conducted in the research and technology of the National Iranian Oil Company, a map of executive achievements and technological solutions in each of the target technology areas, namely reservoir, well, and the facilities, are identified and presented based on the challenges and implementation stages. The results of this study show that in this road map, the issue of creating, developing, and equipping specialized centers for EOR; raising skills, expertise, and knowledge; and transferring technology as sustainability achievements are critical. In addition to other achievements, outputs and results of each stage and technological solutions to challenges are highly emphasized and essential.

Abstract Iran is one of the largest oil and gas producers in the world. Intelligent manufacturing approaches can lead to better performance and lower costs of the well drilling process. One of the most critical issues during the drilling operation is the wellbore stability. Instability of wellbore can occur at different stages of a well life and inflict heavy financial and time damage on companies. A controllable factor can prevent these damages by selecting a proper drilling mud weight. This research presents a drilling mud weight estimator for Iranian wells using deep-learning techniques. Our Iranian data set only contains 900 samples, but efficient deep-learning models usually need large amounts of data to obtain acceptable performance. Therefore, the samples of two data sets related to the United Kingdom and Norway fields are also used to extend our data set. Our final data set has contained more than half-million samples that have been compiled from 132 wells of three fields. Our presented mud weight estimator is an artificial neural network with 5 hidden layers and 256 nodes in each layer that can estimate the mud weight for new wells and depths with the mean absolute error (MAE) of smaller than ±0.039 pound per gallon (ppg). In this research, the presented model is challenged in real-world conditions, and the results show that our model can be reliable and efficient in the real world.