Integration of Seismic and 3D Ultra-Deep Azimuthal Resistivity LWD

2021 ◽  
Author(s):  
M. Alexander ◽  
D. Salim ◽  
M. Etchebes ◽  
T. Akindipe
Keyword(s):  
Resistivity Lwd ◽  
Azimuthal Resistivity

THE FINAL PIECE OF THE PUZZLE: 3-D INVERSION OF ULTRA-DEEP AZIMUTHAL RESISTIVITY LWD DATA

2019 ◽  
Author(s):  
Nigel Clegg ◽  
Timothy Parker ◽  
Bronwyn Djefel ◽  
Luc Monteilhet ◽  
David Marchant
Keyword(s):  
Resistivity Lwd ◽  
Azimuthal Resistivity

An Improved Complex Image Theory for Fast Resistivity Modeling and Its Application to Geosteering

SPE Journal ◽  
2016 ◽  
Vol 21 (04) ◽  
pp. 1450-1457 ◽  
Author(s):  
Jiefu Chen ◽  
Jing Wang ◽  
Yao Yu
Keyword(s):  
Gamma Ray ◽  
Observation Point ◽  
Image Theory ◽  
Additional Information ◽  
Complex Image ◽  
Parallel Layer ◽  
Resistivity Anisotropy ◽  
Resistivity Lwd ◽  
Dip Angle ◽  
Azimuthal Resistivity

Summary azimuthal resistivity logging-while-drilling (LWD) tools with tilted antennas (Bittar 2002; Li et al. 2005) are widely used in geosteering because of their azimuthal sensitivity and the relatively large depth of investigation compared with other LWD tools such as nuclear, acoustic, or gamma ray measurements. Compared with conventional resistivity tools, azimuthal-resistivity LWD measurements can provide additional information including distance-to-boundary, relative dip angle, and resistivity anisotropy (Li et al. 2014). Because of the computing efficiency requirement, modeling and inversion of azimuthal-resistivity LWD measurements are usually based on a 1D parallel-layer model in practice (Zhong et al. 2008). Clearly, this 1D model assumption is not applicable to some realistic situations such as when the tool is navigating in unparallel-layer formation, or approaching a fault. The 3D full-wave simulations such as finite-difference or finite-element methods can handle the complex cases, but they are generally too slow for real jobs, not to mention the inversion that is based on iterative calls of forward modeling. An approximation method called complex image theory was proposed for geophysical prospecting (Wait 1969; Bannister 1986), and recently was introduced to well logging (Dong and Wang 2011; Wang and Liu 2014). This theory approximates electromagnetic-wave reflection by an interface between local and adjacent beds as signal radiated from a virtual source with a complex distance from the observation point. The complex image theory can be several orders of magnitude more efficient than 3D simulations. However, it also has several limitations: This method only works in resistive beds with conductive shoulders, and the measurement cannot be too close to bed interfaces. Those shortcomings greatly limit this method to more extensive applications. An improved complex image theory is proposed here to tackle the aforementioned difficulties. This improved theory can handle a very short distance from the tool to a bed interface as well as the scenarios in which the source is in a conductive bed instead of a resistive one. One can implement robust inversion schemes on the basis of this method. The effectiveness and efficiency of this method are verified by several numerical examples as well as laboratory tests and field jobs.

Implementation of Vendor-Independent Stochastic Inversion for Improving Quality and Efficiency of Well Placement on the Field of Novatek Company

2021 ◽  
Author(s):  
Danil Andreevich Nemushchenko ◽  
Pavel Vladimirovich Shpakov ◽  
Petr Valerievich Bybin ◽  
Kirill Viktorovich Ronzhin ◽  
Mikhail Vladimirovich Sviridov
Keyword(s):  
Real Time ◽  
Well Placement ◽  
Stochastic Inversion ◽  
Mathematical Algorithm ◽  
Service Companies ◽  
Resistivity Data ◽  
Resistivity Logging ◽  
Azimuthal Resistivity ◽  
Logging Tool ◽  
Single Approach

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.

VENDOR-NEUTRAL STOCHASTIC INVERSION OF LWD DEEP AZIMUTHAL RESISTIVITY DATA AS A STEP TOWARD EFFICIENCY STANDARDIZATION OF GEOSTEERING SERVICES

2021 ◽  
Author(s):  
Mikhail Sviridov ◽  
◽  
Anton Mosin ◽  
Sergey Lebedev ◽  
Ron Thompson ◽  
...  
Keyword(s):  
Markov Chains ◽  
Quality Indicators ◽  
Parallel Execution ◽  
Oil Field ◽  
Model Complexity ◽  
Computational Time ◽  
Model Parameters ◽  
Formation Model ◽  
Inversion Algorithm ◽  
Azimuthal Resistivity

While proactive geosteering, special inversion algorithms are used to process the readings of logging-while-drilling resistivity tools in real-time and provide oil field operators with formation models to make informed steering decisions. Currently, there is no industry standard for inversion deliverables and corresponding quality indicators because major tool vendors develop their own device-specific algorithms and use them internally. This paper presents the first implementation of vendor-neutral inversion approach applicable for any induction resistivity tool and enabling operators to standardize the efficiency of various geosteering services. The necessity of such universal inversion approach was inspired by the activity of LWD Deep Azimuthal Resistivity Services Standardization Workgroup initiated by SPWLA Resistivity Special Interest Group in 2016. Proposed inversion algorithm utilizes a 1D layer-cake formation model and is performed interval-by-interval. The following model parameters can be determined: horizontal and vertical resistivities of each layer, positions of layer boundaries, and formation dip. The inversion can support arbitrary deep azimuthal induction resistivity tool with coaxial, tilted, or orthogonal transmitting and receiving antennas. The inversion is purely data-driven; it works in automatic mode and provides fully unbiased results obtained from tool readings only. The algorithm is based on statistical reversible-jump Markov chain Monte Carlo method that does not require any predefined assumptions about the formation structure and enables searching of models explaining the data even if the number of layers in the model is unknown. To globalize search, the algorithm runs several Markov chains capable of exchanging their states between one another to move from the vicinity of local minimum to more perspective domain of model parameter space. While execution, the inversion keeps all models it is dealing with to estimate the resolution accuracy of formation parameters and generate several quality indicators. Eventually, these indicators are delivered together with recovered resistivity models to help operators with the evaluation of inversion results reliability. To ensure high performance of the inversion, a fast and accurate semi-analytical forward solver is employed to compute required responses of a tool with specific geometry and their derivatives with respect to any parameter of multi-layered model. Moreover, the reliance on the simultaneous evolution of multiple Markov chains makes the algorithm suitable for parallel execution that significantly decreases the computational time. Application of the proposed inversion is shown on a series of synthetic examples and field case studies such as navigating the well along the reservoir roof or near the oil-water-contact in oil sands. Inversion results for all scenarios confirm that the proposed algorithm can successfully evaluate formation model complexity, recover model parameters, and quantify their uncertainty within a reasonable computational time. Presented vendor-neutral stochastic approach to data processing leads to the standardization of the inversion output including the resistivity model and its quality indicators that helps operators to better understand capabilities of tools from different vendors and eventually make more confident geosteering decisions.

Mapping Complex Injectite Bodies With Multiwell Electromagnetic 3D Inversion Data

2021 ◽  
Vol 62 (1) ◽  
pp. 109-121
Author(s):  
Nigel Clegg ◽  
◽  
Endre Eriksen ◽  
Kevin Best ◽  
Ingeborg Tøllefsen ◽  
...  
Keyword(s):  
Three Dimensional ◽  
3D Inversion ◽  
Independent Verification ◽  
Major Increase ◽  
Multiple Frequencies ◽  
Geological Complexity ◽  
Azimuthal Resistivity ◽  
Subsurface Resistivity ◽  
The Vertical Distribution ◽  
Close Fit

Electromagnetic (EM) inversion processing of ultradeep resistivity data has advanced from one dimensional (1D) to three dimensional (3D). These advances have helped improve the geological complexity that can be imaged and provide additional reservoir information. The large depth of investigation (DOI) of ultradeep LWD EM tools means that distant boundaries might not be detected by any other sensor in the tool string, making it difficult to verify the results. As inversion results represent a model of the subsurface resistivity distribution and not a direct measurement, it is important to have high confidence in the results. Directly comparing the component data measured by the tool to the modeled component data from the inversion across multiple frequencies provides confidence in the resultant model where the data have a close fit. However, as measurement sensitivities decrease with distance, there is potential for non-uniqueness, generating a model that is geologically unrealistic. Increased confidence can be achieved with independent verification of the model. This paper details results from a trilateral well in an injectite reservoir wherein the sand distribution was expected to be complex. The 1D inversions showed the vertical distribution of the sand, but the results were sometimes distorted by lateral resistivity variations. The 3D inversion of the data allowed the lateral resistivity variations to be resolved. These results can be corroborated by direct comparison with azimuthal resistivity images. Additionally, the laterals all diverged from the same main bore and remained close together initially in an area containing major sand injectites. The 3D inversions from two of the wells overlap and define similarly shaped structures, providing confidence in the 3D inversion model. In complex geobodies, such as the injectites described, significant lateral variation in the reservoir distribution is expected, which is not captured by 1D inversion. Understanding the shape of these structures and their potential connectivity using 3D inversion provides a major increase in reservoir understanding that is critical to completion design.

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