A New Robust Nonlinear Inversion Algorithm for LWD Resistivity Measurements in High-Angle and Horizontal Wells

2017 ◽  
Author(s):  
F. X. Hu ◽  
Y. R. Fan
2019 ◽  
Author(s):  
Maria Cecilia Bravo ◽  
Mirza Hassan Baig ◽  
Artur Kotwicki ◽  
Nicolas Gueze ◽  
Mathias Horstmann ◽  
...  

2021 ◽  
Author(s):  
Kyubo Noh ◽  
◽  
Carlos Torres-Verdín ◽  
David Pardo ◽  
◽  
...  

We develop a Deep Learning (DL) inversion method for the interpretation of 2.5-dimensional (2.5D) borehole resistivity measurements that requires negligible online computational costs. The method is successfully verified with the inversion of triaxial LWD resistivity measurements acquired across faulted and anisotropic formations. Our DL inversion workflow employs four independent DL architectures. The first one identifies the type of geological structure among several predefined types. Subsequently, the second, third, and fourth architectures estimate the corresponding spatial resistivity distributions that are parameterized (1) without the crossings of bed boundaries or fault plane, (2) with the crossing of a bed boundary but without the crossing of a fault plane, and (3) with the crossing of the fault plane, respectively. Each DL architecture employs convolutional layers and is trained with synthetic data obtained from an accurate high-order, mesh-adaptive finite-element forward numerical simulator. Numerical results confirm the importance of using multi-component resistivity measurements -specifically cross-coupling resistivity components- for the successful reconstruction of 2.5D resistivity distributions adjacent to the well trajectory. The feasibility and effectiveness of the developed inversion workflow is assessed with two synthetic examples inspired by actual field measurements. Results confirm that the proposed DL method successfully reconstructs 2.5D resistivity distributions, location and dip angles of bed boundaries, and the location of the fault plane, and is therefore reliable for real-time well geosteering applications.


2016 ◽  
Author(s):  
Hui Xie ◽  
Chris Morriss ◽  
John Rasmus ◽  
Koji Ito ◽  
Aria Abubakar ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Michael Thiel ◽  
◽  
Haifeng Wang ◽  
Dzevat Omeragic ◽  
Jean-Michel Denichou ◽  
...  

Faulting is one type of structural trap for hydrocarbon reservoirs. With more and more fields moving toward the brownfield or mature operations stage of life, the opportunity to target bypassed or attic oil in the vicinity of bounding fault(s) is becoming more and more attractive to operators. However, without an effective logging-while-drilling (LWD) tool to locate and map a fault parallel to the well trajectory, it has been challenging and potentially high risk to optimally place a well to drain oil reserves near the fault. Operators often plan these horizontal wells at a significant distance away from the mapped fault position to avoid impacts to the well construction and production of the well. Often, the interpreted fault position, based on seismic data, can have significant lateral uncertainty, and uncertainties attached to standard well survey measurements make it challenging to place the well near the fault. This often results in the wells being placed much farther from the fault than expected, which is not optimal for maximizing recovery. In other cases, due to uncertainty in the location of the fault, the wells would accidentally penetrate the side faults and cause drilling and other issues. Conventional remote boundary detection LWD tools do not assist with locating the fault position, as they only detect formation boundaries above or below the trajectory and not to the side. In this paper, the authors propose a novel approach for mapping features like a fault parallel to the well trajectory, which was previously impossible to map accurately. This new approach utilizes a new class of deep directional resistivity measurements acquired by a reservoir mapping-while-drilling tool. The deep directional resistivity measurements are input to a newly devised inversion algorithm, resulting in high-resolution reservoir mapping on the transverse plane, which is perpendicular to the well path. These new measurements have a strong sensitivity to resistivity in contrast to the sides of the wellbore, making them suitable for side fault detection. The new inversion in the transverse plane is not limited to detecting a side fault; it can also map any feature on the transverse plane to the well path, which further broadens the application of this technology. Using the deep directional resistivity data acquired from a horizontal ultra-ERD well recently drilled in the Wandoo Field offshore Western Australia, the authors tested this approach against the well results and existing control wells. Excellent mapping of the main side fault up to 30 m to the side of the well was achieved with the new approach. Furthermore, the inversion reveals other interesting features like lateral formation thickness variations and the casing of a nearby well. In addition, the methodology of utilizing this new approach for guiding geosteering parallel to side fault in real time is elaborated, and the future applications are discussed.


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