Algorithm for inversion of resistivity logging-while-drilling data in 2D pixel-based model

Multi-frequency and multi-component extra-deep azimuthal resistivity measurements with depth of investigation of a few tens of meters provide advanced possibilities for mapping of complex reservoir structures. Inversion of the induction measurements set becomes an important technical problem. We present a regularized Levenberg–Marquardt algorithm for inversion of resistivity measurements in a 2D environment model with pixel-based resistivity distribution. The cornerstone of the approach is an efficient parallel algorithm for computation of resistivity tool signals and its derivatives with respect to the pixel conductivities using volume integral equation method. Numerical tests of the suggested approach demonstrate its feasibility for near real time inversion.

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

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.

Laboratory-scaled Azimuthal Resistivity Survey for Fracture Detection

We conducted azimuthal resistivity survey (ARS) at laboratory scale to study apparent resistivity patterns due to fracture existing in subsurface through physical modeling using test objects buried in a sandbox as well as in a test location outside laboratory building. This survey was divided into 2 experiments, i.e. experiment A and experiment B. In experiment A the survey is implemented on 2.50 m x 1.5 m x 0.81 m sandbox, made of 10mm thick glass plates. Sandstone was used as medium representing quasi homogenous medium. Clay roof tiles as well as steel plates as test objects were buried in the sandbox with three different deep angles: 90°, 45° and 0°. In experiment B this survey was conducted outside laboratory building on the grass field and implemented on 2.50 m x 2.5 m x 1.0 m soil body. Vertical single glass plate as well as vertical double glass plates at 30 cm distance were buried in the soil body. Azimuthal resistivity measurements at 15° angular step using Wenner and dipole-dipole configuration were carried out in both experiments located at 1 point just above anomalous object for experiment A and at 3 points at 15 cm distance from anomalous object for experiment B. As a compliment to ARS we acquired profiling data from two lines parallel and perpendicular to horizontal axis of anomalous object. Our results show that the apparent resistivity pattern can show the direction of anomalous object for both configurations and experiments with a little deviation.

Vertical to Horizontal - Ultra Deep Azimuthal Resistivity Tool UDAR Service Helping to Maximize Production

Dvalin field, discovered in 2010-2012. The location of this field is in the Norwegian Sea, as shown in (Figure 1). Dvalin field is an HPHT gas field in Middle Jurassic sandstone in the Garn and Ile Formations – the former being homogeneous with better reservoir properties, during the later heterogenous with low quality. (DVALIN, 2020) The well 6507/7-Z-2 H objective is to produce hydrocarbons from the Jurassic reservoir section of the Dvalin field safely and cost-effectively. The well was planned to be drilled near vertical in the reservoir section and TD'ed at a maximum depth corresponding to the Garn Formation base. After the productivity results from Z-3-H well came in at the low end of expectations, it was evaluated and decided to change the well profile of the Z-2-H well from vertical reservoir penetration to a horizontal profile; to have two penetrations with a minimum of 150m MD separation in the upper high permeable streak and then drop to penetrate lower high permeable streak. This decision was conducted only three days before starting the 17.5-inch section on the subject well. One Team culture was the key to achieving this significant change successfully. The decision to change the well-profile was conducted after a thorough engineering evaluation, including offset well analysis, which was very limited as the closest horizontal well was more than 40 km away. As the well was not planned as a horizontal well, departure between the surface location and Target Easting & Northing was minimal. Therefore, a high turn and deeper inclination build were required, which added some complexity to the well design. One of the additional primary risks related to this change of trajectory design is deploying a more complex BHA design in the reservoir section with a full suite of LWD technologies run in an HT environment. In the planning phase, special consideration was needed to accurately simulate the expected circulating temperature and have proper procedures in place for temperature management and control. Being the first horizontal well in the field, thus detailed planning was key for successful execution. Ultra-Deep Azimuthal Resistivity Tool (UDAR) Reservoir-Mapping capability was considered to help optimize the landing and navigate within the reservoir section. A feasibility study was conducted, and a 2-receiver Ultra Deep Azimuthal Resistivity Tool BHA configuration was selected and deployed. During the execution, the Ultra Deep Azimuthal Resistivity Tool real-time inversion mapped the reservoir geometry, revealing resistive layers within the Garn formation, thereby facilitating optimal placement of the well to achieve the set objectives. The well execution was largely considered flawless, with the real-time Ultra Deep Azimuthal Resistivity Tool data and corresponding interpretations facilitating decisions.

Maximizing Net Pay in Penta-Lateral Well With Advanced Proactive Geosteering and 2D Structural Analysis Using Azimuthal Resistivity Measurements

A unique challenge for ongoing Schrader Bluff development is to increase field production with the drilling of a high-angle, multilateral producer. To address the challenge, a project was initiated with the primary goal to safely drill five laterals with a key emphasis on maximizing net sand exposure while balancing the short- and long-term value associated with selecting the appropriate technology. This paper focuses on the technical aspects of the proactive geosteering with azimuthal resistivity measurements and multilayered user-guided inversion used in this project. The prewell study based on multiple offset wells is discussed in detail, followed by presenting the highlights of the real-time drilling/geosteering operations, and concluded with the comprehensive post-well analysis study after all five laterals were drilled. The key effort before the drilling was thorough simulation of various scenarios for several target formations. It allowed the selection of the proper geosteering service level (tools and data inversion methods), providing required resolution capabilities but not exceeding the project budget. Value of inversion on real-time data is demonstrated with several examples accompanied by uncertainty analysis of inversion results. Post-well processing included quantification of the main fault amplitude, in-depth uncertainty analysis, and 3D visualization, as well as geological interpretation using an advanced 3D modeling application. The final position and orientation of the major fault were supported with the results of 2D inversion performed in the vicinity of the fault. The integration between advanced geosteering tools and an active team effort helped achieve this complex penta-lateral well, drilling all legs with excellent directional control with numerous steering adjustments and a final net pay of 20,455 ft out of 28,886 ft drilled, which exceeded the operator’s expectations.

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

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

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.