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Sen, Mrinal K.

Publications and source records attributed to Sen, Mrinal K..

Inversion of downhole electrical measurements for proppant mapping using very fast simulated annealing

Proppant mapping is critical for optimizing fracture treatment design and improving wells’ productivity. An electrode-based resistivity tool concept was developed earlier for proppant mapping in cased-hole wells. An array of insulating gaps is installed and cemented in place as a permanent part of the casing string. The electrical measurements are performed by imposing a voltage across each insulating gap, one at a time, before and after hydraulic fracture operations. The voltages across other insulating gaps near the transmitter gap are recorded. The method relies on direct excitation of the casing, which is expected to overcome the severe limitations of induction tools in cased-hole wells. A forward model based on a finite volume method has been developed to simulate the tool’s response to one or multiple fractures. To enable the implementation of such a practical system in multistage fractured horizontal wells, a fast and robust inversion approach is required. To that end, we have developed a divide-and-conquer approach based on a global optimization algorithm very fast simulated annealing (VFSA). Specifically, the original inverse problem is divided into subproblems and each subproblem can be solved separately using VFSA. The results indicate that our approach can invert the data and output widths and radii of multiple fractures without requiring a large number of forward simulations. The robustness of the inverse solver is also tested by adding Gaussian noise to the synthetic data. We tested example cases that demonstrate that when up to 5% noise is introduced, VFSA still provides very accurate inversion results with moderate uncertainties. Inversion results with some more realistic conditions, e.g., tilted fractures, complex fractures, and so on, are also presented.

Geochemistry & Geophysics↗

Crustal and uppermost mantle structure in the Middle East: assessing constraints provided by jointly modelling Ps and Sp receiver functions and Rayleigh wave group velocity dispersion curves

Seismic velocity models are found, along with uncertainty estimates, for 11 sites in the Middle East by jointly modelling Ps and Sp receiver functions and surface (Rayleigh) wave group velocity dispersion. The approach performs a search for models that satisfy goodness-of-fit criteria guided by a variant of simulated annealing and uses statistical tools to assess these products of searches. These tools, a parameter correlation matrix and marginal posterior probability density (PPD) function, allow us to evaluate quantitatively the constraints that each data type imposes on model parameters and to identify portions of each model that are well-constrained relative to other portions. This joint modelling technique, which we call ‘multi-objective optimization for seismology’, does not require a good starting solution, although such a model can be incorporated easily, if available, and can reduce the computation time significantly. Applying the process described above to broadband seismic data reveals that crustal thickness varies from 15 km beneath Djibouti (station ATD) to 45 km beneath Saudi Arabia (station RAYN). A pronounced low velocity zone for both Vp and Vs is present at a depth of ~12 km beneath station KIV located in northern part of greater Caucasus, which may be due to the presence of a relatively young volcano. Similarly, we also noticed a 6-km-thick low velocity zone for Vp beginning at 20 km depth beneath seismic station AGIN, on the Anatolian plateau, while positive velocity gradients prevail elsewhere in eastern Turkey. Beneath station CSS, located in Cyprus, an anomalously slow layer is found in the uppermost mantle, which may indicate the presence of altered lithospheric material. Crustal P- and S-wave velocities beneath station D2, located in the northeastern portion of central Zagros, range between 5.2–6.2 and 3.2–3.8 km s -1 , respectively. Finally, in Oman, we find a Moho depth of 34.0 ± 1.0 km and 25.0 ± 1.0 to 30.0 ± 1.0 km beneath stations S02 and S04, respectively.

58 GEOSCIENCES↗