15 research outputs found
High-Precision Geosteering via Reinforcement Learning and Particle Filters in Python
No full text<div>
<div># High-Precision Geosteering via Reinforcement Learning and Particle Filters in Python</div>
<br>
<div> This GitHub repository hosts our Python code for integrating Reinforcement Learning (RL) with Particle Filter (PF) to improve decision-making in geosteering, as detailed in "High-Precision Geosteering via Reinforcement Learning and Particle Filters" [ressi2024rlpf](https://arxiv.org/abs/2402.06377).</div>
<br>
<div>## How to cite:</div>
<br>
<div>If you want to adopt the code in your research, please cite the original paper:</div>
<br>
<div>Muhammad, R. B., Srivastava, A., Alyaev, S., Bratvold, R. B., & Tartakovsky, D. M. (2024). High-Precision Geosteering via Reinforcement Learning and Particle Filters. arXiv preprint [arXiv:2402.06377](https://arxiv.org/abs/2402.06377).</div>
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<div>```</div>
<div>@article{alyaev2021direct,</div>
<div> doi = {https://doi.org/10.48550/arXiv.2402.06377},</div>
<div> url = {https://arxiv.org/abs/2402.06377},</div>
<div> author = {Muhammad, Ressi Bonti and Srivastava, Apoorv and Alyaev, Sergey and Bratvold, Reidar B. and Tartakovsky, Daniel M.},</div>
<div> title = {High-Precision Geosteering via Reinforcement Learning and Particle Filters},</div>
<div> journal = {arXiv 2402.06377},</div>
<div> year = {2024},</div>
<div>}</div>
<div>```</div>
<div>## Acknowledgements</div>
<br>
<div>This work is part of the Center for Research-based Innovation DigiWells: Digital Well Center for Value Creation, Competitiveness and Minimum Environmental Footprint (NFR SFI project no. 309589, https://DigiWells.no). The center is a cooperation of NORCE Norwegian Research Centre, the University of Stavanger, the Norwegian University of Science and Technology (NTNU), and the University of Bergen. It is funded by Aker BP, ConocoPhillips, Equinor, TotalEnergies, Vår Energi, Wintershall Dea, and the Research Council of Norway.</div>
</div>
High-Precision Geosteering via Reinforcement Learning and Particle Filters in Python
No full text<div>
<div># High-Precision Geosteering via Reinforcement Learning and Particle Filters in Python</div>
<br>
<div> This GitHub repository hosts our Python code for integrating Reinforcement Learning (RL) with Particle Filter (PF) to improve decision-making in geosteering, as detailed in "High-Precision Geosteering via Reinforcement Learning and Particle Filters" [ressi2024rlpf](https://arxiv.org/abs/2402.06377).</div>
<br>
<div>## How to cite:</div>
<br>
<div>If you want to adopt the code in your research, please cite the original paper:</div>
<br>
<div>Muhammad, R. B., Srivastava, A., Alyaev, S., Bratvold, R. B., & Tartakovsky, D. M. (2024). High-Precision Geosteering via Reinforcement Learning and Particle Filters. arXiv preprint [arXiv:2402.06377](https://arxiv.org/abs/2402.06377).</div>
<br>
<div>```</div>
<div>@article{muhammad2024RLPF,</div>
<div> doi = {https://doi.org/10.48550/arXiv.2402.06377},</div>
<div> url = {https://arxiv.org/abs/2402.06377},</div>
<div> author = {Muhammad, Ressi Bonti and Srivastava, Apoorv and Alyaev, Sergey and Bratvold, Reidar B. and Tartakovsky, Daniel M.},</div>
<div> title = {High-Precision Geosteering via Reinforcement Learning and Particle Filters},</div>
<div> journal = {arXiv 2402.06377},</div>
<div> year = {2024},</div>
<div>}</div>
<div>```</div>
<br>
<div>### To cite this code</div>
<br>
<div>Please use the link/bibtex to its deposited version:</div>
<br>
<div><a href="https://doi.org/10.5281/zenodo.10727358"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.10727358.svg" alt="DOI"></a></div>
<br>
<div>```</div>
<div>@software{muhammad2024RLPFPy,</div>
<div> author = {Muhammad, Ressi Bonti and</div>
<div> Srivastava, Apoorv and</div>
<div> Alyaev, Sergey and</div>
<div> Bratvold, Reidar Brumer and</div>
<div> Tartakovsky, Daniel M.},</div>
<div> title = {{High-Precision Geosteering via Reinforcement</div>
<div> Learning and Particle Filters in Python}},</div>
<div> month = feb,</div>
<div> year = 2024,</div>
<div> publisher = {Zenodo},</div>
<div> version = {1.1},</div>
<div> doi = {10.5281/zenodo.10727358},</div>
<div> url = {https://doi.org/10.5281/zenodo.10727358}</div>
<div>}</div>
<div>```</div>
<br>
<div>## Acknowledgements</div>
<br>
<div>This work is part of the Center for Research-based Innovation DigiWells: Digital Well Center for Value Creation, Competitiveness and Minimum Environmental Footprint (NFR SFI project no. 309589, https://DigiWells.no). The center is a cooperation of NORCE Norwegian Research Centre, the University of Stavanger, the Norwegian University of Science and Technology (NTNU), and the University of Bergen. It is funded by Aker BP, ConocoPhillips, Equinor, TotalEnergies, Vår Energi, Wintershall Dea, and the Research Council of Norway.</div>
</div>
Towards Real-Time 3D Modeling of Induction Logs Using an Integral Equation Method
No full textReal-time 3D imaging of the induction log is essential for improving the decision-making process in geosteering. To fulfill this need, we investigated various strategies for reducing the computational cost for 3D modelling of induction logs using the integral equation (IE) method, including the use of iterative Krylov solver, convolution with FFT algorithm, contraction IE formulation, computation acceleration with GPUs, and domain decomposition. We present two cases example to demonstrate the implementation of IE with these strategies. In the first case, we show that the application of domain decomposition allows one to only discretize the inhomogeneous domain and save the computation cost in the case of isolated domains. We present a logging while drilling scenario on a complex model for the second case. Our implementation of the efficient IE on GPUs enables significant acceleration and allows the computation of 3D forward modelling within less than two minutes for each local 3D simulation domain with approximately two million grid cells on a laptop. The implementation of domain decomposition formulation shows a different arrangement of solving IE by decomposing the domain.ImPhys/Medical ImagingImPhys/Van Dongen gou
The analysis of typical profile clearances formation in meshing rotors of the screw compressor
No full text3D induction log modelling with integral equation method and domain decomposition preconditioning
No full textThe deployment of electromagnetic (EM) induction tools while drilling is one
of the standard routines for assisting the geosteering decision-making process.
The conductivity distribution obtained through the inversion of the EM
induction log can provide important information about the geological structure
around the borehole. To image the 3D geological structure in the subsurface, 3D
inversion of the EM induction log is required. Because the inversion process is
mainly dependent on forward modelling, the use of fast and accurate forward
modelling is essential. In this paper, we present an improved version of the
integral equation (IE) based modelling technique for general anisotropic media
with domain decomposition preconditioning. The discretised IE after domain
decomposition equals a fixed-point equation that is solved iteratively with
either the block Gauss-Seidel or Jacobi preconditioning. Within each iteration,
the inverse of the block matrix is computed using a Krylov subspace method
instead of a direct solver. An additional reduction in computational time is
obtained by using an adaptive relative residual stopping criterion in the
iterative solver. Numerical experiments show a maximum reduction in
computational time of 35 per cent compared to solving the full-domain IE with a
conventional GMRES solver. Additionally, the reduction of memory requirement
for covering a large area of the induction tool sensitivity enables
acceleration with limited GPU memory. Hence, we conclude that the domain
decomposition method is improving the efficiency of the IE method by reducing
the computation time and memory requirement.Comment: This article is a manuscript submitted to Geophysical Journal
Internationa
Imaging Technologies in the Diagnosis and Treatment of Acute Pyelonephritis
No full textPurpose The aim of this study was to evaluate the possibilities of ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) in diagnosing acute pyelonephritis (AP) and renal abscess. Patients and Methods Two hundred and seven patients with AP were followed up from 2010 throughout 2015. All the patients were divided into three groups. Group 1 included 113 (54.6%) patients with acute nonobstructive pyelonephritis; group 2 included 33 (15.9%) patients with acute obstructive pyelonephritis; and group 3 included 61 (29.5%) pregnant female patients with AP. All 207 patients with AP underwent ultrasound examination of the kidneys. Computed tomography (CT) was performed in 87 patients (42.0%). MRI was performed in 14 patients (6.7%). Results We identified the ultrasound (US), magnetic resonance (MR), and CT-signs of acute renal inflammation at different stages of the process. The main us-signs were decreased mobility of the kidney, its enlargement, thickened parenchyma, hydrophilic parenchyma and an impairment of corticomedullary differentiation. The typical CT-signs of AP were enlargement of the kidney with its thickened parenchyma and an impairment of corticomedullary differentiation. The main MR-signs of AP were enlargement of the kidney (>12 cm lengthwise), thickened parenchyma (<2 cm in the median segment of the kidney) and an impairment of corticomedullary differentiation. Conclusions Assessment of the structural and functional state of renal parenchyma and the upper urinary tract using techniques such as ultrasonography, CT, MRI contributes to more efficacious treatment of patients at different stages of AP and timely drainage with properly adjusted pathogenetic therapy at the infiltrative stage is instrumental in preventing purulent destructive forms of AP. </jats:sec
MP62-20 STENTING IN CASES OF URETERAL ORIFICE DAMAGE DURING LASER ENUCLEATION OF THE PROSTATE FOR BPH: IS IT WORTH IT?
No full textEvaluation of Prostate HistoScanning as a Method for Targeted Biopsy in Routine Practice
No full textRetrospective Analysis of Short-Term Outcomes After Monopolar Versus Laser Endoscopic Enucleation of the Prostate: A Single Center Experience
No full textMP62-10 MONOPOLAR VERSUS LASER (THUFLEP, HOLEP) ENDOSCOPIC ENUCLEATION OF THE PROSTATE: A SINGLE-CENTER EXPERIENCE
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