The effective medium theory based on the Hertz–Mindlin contact law is the most popular theory to relate dynamic elastic moduli (or elastic velocities) and confining pressure in dry granular media. However, many experimental results proved that the effective medium theory predicts pressure trends lower than experimental ones and over‐predicts the shear modulus. To mitigate these mispredictions, several evolutions of the effective medium theory have been presented in the literature. Among these, the model named modified grain contact theory is an empirical approach in which three parametric curves are included in the effective medium theory model. Fitting the parameters of these curves permits to adjust the pressure trends of the Poisson ratio and the bulk modulus. In this paper, we present two variations of the modified grain contact theory model. First, we propose a minor modification in the fitting function for the porosity dependence of the calibration parameters that accounts for non‐linearity in the vicinity of the critical porosity. Second, we propose a major modification that reduces the three‐step modified grain contact theory model to a two‐step model, by skipping the calibration parameter–porosity fit in the model and directly modelling the calibration parameter–pressure relation. In addition to an increased simplicity (the fitting parameters are reduced from 10 to 6), avoiding the porosity fit permits us to apply the model to laboratory data that are not provided with accurate porosity measurements. For this second model, we also estimate the uncertainty of the fitting parameters and the elastic velocities. We tested this model on dry core measurements from literature and we verified that it returns elastic velocity trends as good as the original modified grain contact theory model with a reduced number of fitting parameters. Possible developments of the new model to add predictive power are also discussed.
The evolution of the energy market requires companies to increase their operating efficiency, leveraging on collaborative environment and existing assets, including Data. A new focus on data governance and integration is needed to maximize the value of data and ensure "real-time" efficient response. The decoupling of data from applications enables organization by domain and data type in one cross-functional data hub. This scheme is independent from the scope of the activity and will therefore maintain its validity when dealing with new business requiring subsurface data utilization. The integrated data platform will feed advanced digital tools capable to control the risks, optimize performance and reduce emissions associated with the operations. Eni is putting this idea into practice with a new data infrastructure which is integrated across all the subsurface disciplines (G&G, Exploration, Upstream Laboratories, Reservoir and Well Operations departments). In this paper, the example of real time data exploitation will be discussed. Real time data workflow was first established in well operations for operational supervision and later developed for real time performance optimization, through the introduction of predictive analytics. Its latest evolution in the broader subsurface domain encompasses the application of AI to operations geology processes and the extension to all operated activities. This approach will equally support new company goals, such as decarbonization, increasing performance of subsurface activities related to underground storage of CO2 in depleted reservoirs.
During the drilling of a well, a huge quantity of data is acquired in real-time. In order too mitigate risks due to geological uncertainties, to increase operational efficiency, to optimize processes and create new business models, Eni has developed its own cross-functional integrated data platform, which ensures data availability to all subsurface technical functions sharing a common data model. In this paper we describe an innovative approach, born from the collaboration between expert geologists and data scientists. The integrated team has developed a tool based on Artificial Intelligence (AI) supporting operations geologist during drilling phases. Two different tools have been created: litho-fluid interpretations, a set of AI algorithms used to identify in real-time the lithology and to interpret the formation fluids; well-to-well log correlation and look ahead, models used to find analogies between intervals of the well being drilled and the reference well, allowing to estimate the distance and time of arrival to a given geological event. The results obtained have been remarkable in terms of accuracy. The positive feedbacks from the operations geologists give the assurance of the usefulness of the tools and their expected benefits: the tools allow to better control geological uncertainties and speed up some repetitive and time-consuming tasks. The results presented in this paper are focused on two UAE applications of litho-fluid and well-to-well log correlations.
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