A New Deep Azimuthal Resistivity LWD for Optimal Well Placement and Reservoir Exploitation; Successful Validation with Saudi Aramco

Author(s):  
Richard Palmer ◽  
Andre Silva ◽  
A.A. Hajari ◽  
Michael Bittar ◽  
Ramez Shokeir ◽  
...  
2010 ◽  
Author(s):  
Chris Bell ◽  
Cecile Audinet ◽  
Lisa Hammond ◽  
Ansgar Baule ◽  
Jonathon W. Skillings ◽  
...  

2021 ◽  
Author(s):  
Danil Andreevich Nemushchenko ◽  
Pavel Vladimirovich Shpakov ◽  
Petr Valerievich Bybin ◽  
Kirill Viktorovich Ronzhin ◽  
Mikhail Vladimirovich Sviridov

Abstract The article describes the application of a new stochastic inversion of the deep-azimuthal resistivity data, independent from the tool vendor. The new model was performed on the data from several wells of the PAO «Novatek», that were drilled using deep-azimuthal resistivity tools of two service companies represented in the global oilfield services market. This technology allows to respond in a timely manner when the well approaches the boundaries with contrasting resistivity properties and to avoid exit to unproductive zones. Nowadays, the azimuthal resistivity data is the method with the highest penetration depth for the geosteering in real time. Stochastic inversion is a special mathematical algorithm based on the statistical Monte Carlo method to process the readings of resistivity while drilling in real time and provide a geoelectrical model for making informed decisions when placing horizontal and deviated wells. Until recently, there was no unified approach to calculate stochastic inversion, which allows to perform calculations for various tools. Deep-azimuthal resistivity logging tool vendors have developed their own approaches. This article presents a method for calculating stochastic inversion. This approach was never applied for this kind of azimuthal resistivity data. Additionally, it does not depend on the tool vendor, therefore, allows to compare the data from various tools using a single approach.


Author(s):  
Roger Ghanem ◽  
Christian Soize ◽  
Charanraj Thimmisetty

Author(s):  
N.E. Meyer ◽  
F. Socquet-Juglard ◽  
R. Dehesdin ◽  
R. Narayanan

2012 ◽  
Author(s):  
Sameer A. Khan ◽  
Ali Nadeem Al-Shabeeb ◽  
Zankar Jani ◽  
Youcef Azoug ◽  
Nguyen Minh ◽  
...  

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Majid Siavashi ◽  
Mohammad Rasoul Tehrani ◽  
Ali Nakhaee

One of the main reservoir development plans is to find optimal locations for drilling new wells in order to optimize cumulative oil recovery. Reservoir simulation is a necessary tool to study different configurations of well locations to investigate the future of the reservoir and determine the optimal places for well drilling. Conventional well-known numerical methods require modern hardware for the simulation and optimization of large reservoirs. Simulation of such heterogeneous reservoirs with complex geological structures with the streamline-based simulation method is more efficient than the common simulation techniques. Also, this method by calculation of a new parameter called “time-of-flight” (TOF) offers a very useful tool to engineers. In the present study, TOF and distribution of streamlines are used to define a novel function which can be used as the objective function in an optimization problem to determine the optimal locations of injectors and producers in waterflooding projects. This new function which is called “well location assessment based on TOF” (WATOF) has this advantage that can be computed without full time simulation, in contrast with the cumulative oil production (COP) function. WATOF is employed for optimal well placement using the particle swarm optimization (PSO) approach, and its results are compared with those of the same problem with COP function, which leads to satisfactory outcomes. Then, WATOF function is used in a hybrid approach to initialize PSO algorithm to maximize COP in order to find optimal locations of water injectors and oil producers. This method is tested and validated in different 2D problems, and finally, the 3D heterogeneous SPE-10 reservoir model is considered to search locations of wells. By using the new objective function and employing the hybrid method with the streamline simulator, optimal well placement projects can be improved remarkably.


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