Showing papers by "Andrei Osiptsov published in 2022"

Modelling Of Tectonic Fault Activation During CO2 Sequestration in a Saline Aquifer

Abstract: Summary We perform the coupled fluid- and geo-mechanical modeling of CO2 sequestration in a saline aquifer crossed by a tectonic fault. The model is based on hydrodynamical reservoir simulator MUFITS and mechanical simulator FLAC3D linked by the algorithm of the data transfer. The modelling of multiphase filtration of CO2 and brine accounting for phase transitions and thermal phenomena is carried out using MUFITS. At certain time, the hydrodynamical simulation is paused, while current pressure and temperature fields are passed to FLAC3D to calculate the equilibrium mechanical state. Numerically found volumetric strain is used to update the porosity and permeability fields for the subsequent hydrodynamic modelling. The study is focused on the effect of CO2 injection on activation of fault crossing the target aquifer. We derive an analytical expression for the permeability alteration in the rock damage zone due to plastic deformations which is based on the dilatancy evaluated numerically. For the fault core, we apply closure relation available in open literature. Parametric study of fault behavior during CO2 sequestration is carried out by varying the governing parameters including the injection strategy. Safe injection regimes are identified which do not lead to seismic activity and CO2 leakage out of the target aquifer.

Proppant Packing Near the Fracture Tip during Tip Screenout: Asymptotic Models for Pressure Buildup Calibrated on Field Data and Verified with Two-Continua Simulations

Abstract: We analyze pressure slopes during buildup when proppant placement into a fracture results in tip screenout (TSO), as observed in real field data on pumping of fracturing jobs. The phenomenon is described by proppant packing and jamming near the fracture tip, leading to TSO. Transient evolution of the zone of packed proppant near the tip and associated additional pressure drop along the packed bank are evaluated. Under the assumption of constant fracture height, the additional pressure drop during TSO grows as t(1+α), where tα is the law of proppant concentration growth (pressure drop grows quadratically in time, if the proppant concentration on surface at the blender grows linearly in time). The model is calibrated on field data from fracturing jobs in Western Siberia, Russia. These approximate models are verified with the two-continua model of proppant transport used to describe the process of proppant packing and jamming near the fracture tip. We also show how predictions of bottomhole pressure (BHP) growth during TSO obtained using proppant transport models implemented into existing hydraulic fracturing simulators can be significantly improved by a reasonable (based on behavior of dense suspensions at the packing limit) modification to parameters, describing suspension shear viscosity.

Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements

Abstract: We propose a new method for construction of the absolute permeability map consistent with the interpreted results of well logging and well test measurements in oil reservoirs. Nadaraya-Watson kernel regression is used to approximate two-dimensional spatial distribution of the rock permeability. Parameters of the kernel regression are tuned by solving the optimization problem in which, for each well placed in an oil reservoir, we minimize the difference between the actual and predicted values of (i) absolute permeability at the well location (results of interpretation of well logging); (ii) absolute integral permeability of the domain around the well and (iii) skin factor (results of interpretation of well tests). Optimization task (inverse problem) is solved via multiple solutions to forward problems, in which we estimate the integral permeability of reser- voir surrounding a well and the skin factor by the surrogate model. The last one is developed using an artificial neural network trained on the physics-based synthetic dataset generated us- ing the procedure comprising the numerical simulation of bottomhole pressure decline curve in reservoir simulator followed by its interpretation using a semi-analytical reservoir model. The developed method for reservoir permeability map construction is applied to the available reservoir model (Egg Model) with highly heterogeneous permeability distribution due to the presence of highly-permeable channels. We showed that the constructed permeability map is hydrodynamically similar to the original one. Numerical simulations of production in the reservoir with constructed and original permeability maps are quantitatively similar in terms of the pore pressure and fluid saturations distribution at the end of the simulation period. Moreover, we obtained an good match between the obtained results of numerical simulations in terms of the flow rates and total volumes of produced oil, water and injected water.