Determination of formation dip and strike from transient LWD electromagnetic measurements

An original method has been developed for estimating formation dip and strike from transient induction LWD data, based on focusing in the time domain. The focusing consists in decomposing the measured signals into a time series and diagonalizing the matrix of focused magnetic field components. We have implemented the method and comprehensively tested it in horizontally-layered media used for LWD data inversion to solve geosteering problems and evaluate the formation resistivity. Estimates of the angles contribute to reliable geosteering when choosing a direction of drilling, as well as when inverting data for a complex earth model. A significant reduction in the resource intensity of inversion and model equivalence is achieved by reducing the number of determined parameters.

Finding the Best of the Best: Hydraulic Fracturing Design Optimization Study in Oman

Development of the tight gas Khazzan Field in Sultanate of Oman has progressed through an extensive learning curve over many years. Thereby, the hydraulic fracturing design was fine-tuned and optimized to properly fit the requirements of the challenging Barik reservoir in this area. In 2018, BP Oman started developing the Barik reservoir in the Ghazeer Field, which naturally extends the reservoir boundary south of Khazzan Field. However, the Barik reservoir in the Ghazeer area is thicker and more permeable than in the Khazzan Field; therefore, the hydraulic fracturing design required adjustment to be optimized to directly reflect the reservoir needs of the Ghazeer Field. A comprehensive hydraulic fracturing design software was used for this optimization study and sensitivity analysis. This software is a plug-in to a benchmark exploration and production software platform and provides a complete fracturing optimization loop from hydraulic fracturing design sensitivity modelled with a calibrated mechanical earth model to detailed production prediction using the incorporated reservoir simulator. One of the stimulated wells from Ghazeer Field was used as the reference for this study. The reservoir sector model was created and adjusted to match actual data from this well. The data include fracturing treatment execution response, surveillance data such as radioactive tracers, bottomhole pressure gauge, and pressure transient analysis. Reservoir properties were also adjusted to match long-term production data obtained for this reference well. After the reservoir model was fully validated against actual data, multiple completion and fracturing scenarios were simulated to estimate potential production gain and thus find an optimal hydraulic fracturing design for Ghazeer Field. Many valuable outcomes can be concluded from this study. The optimal treatment design was identified. The value of fracture half-length versus conductivity was clarified for this area. The comparison between single-stage fracturing versus multistage treatment across the thick laminated Barik reservoir in a conventional vertical well was derived. The drainage of different layers with variable reservoir properties was compared for a range of different scenarios.

Fracture Geometry Calibration Using Multiple Surveillance Techniques

An accurate Mechanical Earth Model (MEM) is of vital importance in tight gas reservoirs where hydraulic fracturing is the only way to produce hydrocarbons economically. The Barik tight gas reservoir is the main target in Khazzan and Ghazeer Fields at the Sultanate of Oman (Rylance et al., 2011). This reservoir consists of multiple low-permeability sandstone layers interbedded with marine shales. A good understanding of the fracture propagation in such a reservoir has a major effect on completion and fracturing design. The MEM derived from sonic logs and calibrated with core data needs to be further validated by independent measurements of the fracturing geometry. Multiple surveillance techniques have been implemented in the Barik reservoir to validate the MEM and to match observations from hydraulic fracturing operations. These techniques include closure interpretation using a wireline deployed formation testing assembly, the use of mini-frac injection tests with deployed bottomhole pressure gauges, execution of post injection time-lapse temperature logging, the injection of radioactive tracers, associated production logging, subsequent pressure transient analysis and other techniques. A cross-disciplinary team worked with multiple sources of data to calibrate the MEM with the purpose of delivering a high-confidence prediction of the created fracture geometry, which honors all available surveillance data. In turn, this validation approach provided a solid basis for optimization of the completion and fracturing design, in order to optimally exploit this challenging reservoir and maximize the economic returns being delivered. For example, combination of stress testing with radioactive tracers provided confidence in stress barriers in this multilayered reservoir. Pressure transient analysis allowed to calibrate mechanical model to match fracturing half-length that is contributing to production. This paper provides extensive surveillance examples and workflows for data analysis. Surveillance of this degree in the same well is uncommon because of the associated time and cost. However, it provides unique value for understanding the target reservoir. This paper demonstrates the Value Of Information (VOI) that can be associated with such surveillance and provides a concrete and practical example that can be used for the justification of future surveillance programs associated with the hydraulic fracturing operations.

FrosPy: A Modular Python Toolbox for Normal Mode Seismology

We present free oscillations Python (FrosPy), a modular Python toolbox for normal mode seismology, incorporating several Python core classes that can easily be used and be included in larger Python programs. FrosPy is freely available and open source online. It provides tools to facilitate pre- and postprocessing of seismic normal mode spectra, including editing large time series and plotting spectra in the frequency domain. It also contains a comprehensive database of center frequencies and quality factor (Q) values based on 1D reference model preliminary reference Earth model for all normal modes up to 10 mHz and a collection of published measurements of center frequencies, Q values, and splitting function (or structure) coefficients. FrosPy provides the tools to visualize and convert different formats of splitting function coefficients and plot these as maps. By giving the means of using and comparing normal mode spectra and splitting function measurements, FrosPy also aims to encourage seismologists and geophysicists to learn about normal mode seismology and the study of the Earth’s free oscillation spectra and to incorporate them into their own research or use them for educational purposes.

GANSim-3D for conditional geomodelling: theory and field application

Geomodelling of subsurface reservoirs is important for water resources, hydrocarbon exploitation, and Carbon Capture and Storage (CCS). Traditional geostatistics-based approaches cannot abstract complex geological patterns and are thus not able to simulate very realistic earth models. We present a Generative Adversarial Networks (GANs)-based 3D reservoir simulation framework, GANSim-3D, which can capture geological patterns and relationships between various conditioning data and earth models and is thus able to directly simulate multiple 3D realistic and conditional earth models of arbitrary sizes from given conditioning data. In GANSim-3D, the generator, designed to only include 3D convolutional layers, takes various 3D conditioning data and 3D random latent cubes (composed of random numbers) as inputs and produces a 3D earth model. Two types of losses, the original GANs loss and condition-based loss, are designed to train the generator progressively from shallow to deep layers to learn the geological patterns and relationships from coarse to fine resolutions. Conditioning data can include 3D sparse well facies data, 3D low-resolution probability maps, and global features like facies proportion, channel width, etc. Once trained on a training dataset where each training sample is a 3D cube of a small fixed size, the generator can be used for geomodelling of 3D reservoirs of large arbitrary sizes by directly extending the sizes of all inputs and the output of the generator proportionally. To illustrate how GANSim-3D is used for field geomodelling and also to verify GANSim-3D, a field karst cave reservoir in Tahe area of China is used as an example. The 3D well facies data and 3D probability map of caves obtained from geophysical interpretation are used as conditioning data. First, we create a training dataset consisting of facies models of 64×64×64 cells with a process-mimicking simulation method to integrate field geological patterns. The training well facies data and the training probability map data are produced from the training facies models. Then, the 3D generator is successfully trained and evaluated in two synthetic cases with various metrics. Next, we apply the pretrained generator for conditional geomodelling of two field cave reservoirs of Tahe area. The first reservoir is 800m×800m×64m and is divided into 64×64×64 cells, while the second is 4200m×3200m×96m and is divided into 336×256×96 cells. We fix the input well facies data and cave probability maps and randomly change the input latent cubes to allow the generator to produce multiple diverse cave reservoir realizations, which prove to be consistent with the geological patterns of real Tahe cave reservoir as well as the input conditioning data. The noise in the input probability map is suppressed by the generator. Once trained, the geomodelling process is quite fast: each realization with 336×256×96 cells takes 0.988 seconds using 1 GPU (V100). This study shows that GANSim-3D is robust for fast 3D conditional geomodelling of field reservoirs of arbitrary sizes.

Integration of Advanced Borehole Sonic and Resistivity Image Analysis for Fracture and Stress Characterisation - Implications to Carbon Sequestration Feasibility

Borehole resistivity images and dipole sonic data analysis helps a great deal to identify fractured zones and obtain reasonable estimates of the in-situ stress conditions of geologic formations. Especially when assessing geologic formations for carbon sequestration feasibility, borehole resistivity image and borehole sonic assisted analysis provides answers on presence of fractured zones and stress-state of these fractures. While in deeper formations open fractures would favour carbon storage, in shallower formations, on the other hand, storage integrity would be potentially compromised if these fractures get reactivated, thereby causing induced seismicity due to fluid injection. This paper discusses a methodology adopted to assess the carbon dioxide sequestration feasibility of a formation in the Newark Basin in the United States, using borehole resistivity image(FMI™ Schlumberger) and borehole sonic data (SonicScaner™ Schlumberger). The borehole image was interpreted for the presence of natural and drilling-induced fractures, and also to find the direction of the horizontal stress azimuth from the identified induced fractures. Cross-dipole sonic anisotropy analysis was done to evaluate the presence of intrinsic or stress-based anisotropy in the formation and also to obtain the horizontal stress azimuth. The open or closed nature of natural fractures was deduced from both FMI fracture filling electrical character and the Stoneley reflection wave attenuation from SonicScanner monopole low frequency waveform. The magnitudes of the maximum and minimum horizontal stresses obtained from a 1-Dimensional Mechanical Earth Model were calibrated with stress magnitudes derived from the ‘Integrated Stress Analysis’ approach which takes into account the shear wave radial variation profiles in zones with visible crossover indications of dipole flexural waves. This was followed by a fracture stability analysis in order to identify critically stressed fractures. The borehole resistivity image analysis revealed the presence of abundant natural fractures and microfaults throughout the interval which was also supported by the considerable sonic slowness anisotropy present in those intervals. Stoneley reflected wave attenuation confirmed the openness of some natural fractures identified in the resistivity image. The strike of the natural fractures and microfaults showed an almost NE-SW trend, albeit with considerable variability. The azimuth of maximum horizontal stress obtained in intervals with crossover of dipole flexural waves was also found to be NE-SW in the middle part of the interval, thus coinciding with the overall trend of natural fractures. This might indicate that the stresses in those intervals are also driven by the natural fracture network. However, towards the bottom of the interval, especially from 1255ft-1380ft, where there were indications of drilling induced fractures but no stress-based sonic anisotropy, it was found that that maximum horizontal stress azimuth rotated almost about 30 degrees in orientation to an ESE-WNW trend. The stress magnitudes obtained from the 1D-Mechanical Earth Model and Integrated Stress Analysis approach point to a normal fault stress regime in that interval. The fracture stability analysis indicated some critically stressed open fractures and microfaults, mostly towards the lower intervals of the well section. These critically stressed open fractures and microfaults present at these comparatively shallower depths of the basin point to risks associated with carbon dioxide(CO2) leakage and also to induced seismicity that might result from the injection of CO2 anywhere in or immediately below this interval.

Machine Learning in Computer Vision Software for Geomechanics Modeling

AI-GEM (Artificial Intelligence of Geomechanics Earth Modelling) tool aims to detect the geomechanical features, especially the elastic parameters and stresses. Characterizing the wellbore instability issues is one of the factors increases cost of drilling and creating an AI-based tool will enhance and present a real-time solution for wellbore instability. These features are usually interpreted manually, depending on the experience and usually impacted by inconsistencies due to biased or unexperienced interpreters. Therefore, there is a need for a robust automatic or semiautomatic approach to reduce time, manual efficiency and consistency. The range of Geomechanics issues is wide and interfaces with many other upstream disciplines (e.g., Petrophysics, Geophysics, Production Geology, Drilling and Reservoir Engineering). Safe and effective field operation is built on the understanding and implementation of the subsurface in-situ stress state throughout the life of the field; the quantification of key subsurface uncertainties through well thought-out data gathering and characterization programs. The integration with appropriate Geomechanics modelling and the field surveillance /monitoring strategy. There are two major aspects that must be addressed during the design phase of any Geomechanics project. The first and most important is developing a realistic estimate of the expected mechanical behaviour of the rocks and its potential response as a result of drilling. The second is to design an economic, safe well and support method for the determined rocks behaviour. The design process begins with the feasibility study followed by preliminary design, the detail design, tender design and throughout the construction. The design is constantly updated during each phase as more information becomes available and this requires the involvement of Geologists, Engineers and Subject Matter Expert throughout the phases of a project. A central concern for all geomechanical designs is the well-rock interaction, which is not only includes the final state but also the transient effects of the well processes as well as time and stress of the dependent rock properties. The end-to-end workflow to achieve the mechanical earth model is automated, guided and orchestrated with the help of machine learning framework such as recommendation engine for offset well data, prediction of well logs, and optimization for all calibration with existing test results, enabling end users to run sensitivity and scenario analysis so on and so forth.

Accelerating Subsurface Data Processing and Interpretation with Cloud-Based Full Waveform Inversion Systems

Understanding the earth's subsurface is critical to the needs of the exploration and production (E&P) industry for minimizing risk and maximizing recovery. Until recently, the industry's service sector has not made many advances in data-driven automated earth model building from raw exploration seismic data. But thankfully, that has now changed. The industry's leading technique to gain an unprecedented increase in resolution and accuracy when establishing a view of the interior of the earth is known as the Full Waveform Inversion (FWI). Advanced formulations of FWI are capable of automating subsurface model building using only raw unprocessed data. Cloud-based FWI is helping to accelerate this journey by encompassing the most sophisticated waveform inversion techniques with the largest compute facility on the planet. This combines to give verifiable accuracy, more automation and more efficiency. In this paper, we describe the transformation of enabling cloud-based FWI to natively take advantage of the public cloud platform's main strength in terms of flexibility and on-demand scalability. We start from lift-and-shift of a legacy MPI-based application designed to be run by a traditional on-prem job scheduler. Our specific goals are to (1) utilize a heterogeneous set of compute hardware throughout the lifecycle of a production FWI run without having to provision them for the entire duration, (2) take advantage of cost-efficient spare-capacity compute instances without uptime guarantees, and (3) maintain a single codebase that can be run both on on-prem HPC systems and on the cloud. To achieve these goals meant transitioning the job-scheduling and "embarrassingly parallel" aspects of the communication code away from using MPI, and onto various cloud-based orchestration systems, as well as finding cloud-based solutions that worked and scaled well for the broadcast/reduction operation. Placing these systems behind a customized TCP-based stub for MPI library calls allows us to run the code as-is in an on-prem HPC environment, while on the cloud we can asynchronously provision and suspend worker instances (potentially with very different hardware configurations) as needed without the burden of maintaining a static MPI world communicator. With this dynamic cloud-native architecture, we 1) utilize advanced formulations of FWI capable of automating subsurface model building using only raw unprocessed data, 2) extract velocity models from the full recorded wavefield (refractions, reflections and multiples), and 3) introduce explicit sensitivity to reflection moveout, invisible to conventional FWI, for macro-model updates below the diving wave zone. This makes it viable to go back to older legacy datasets acquired in complex environments and unlock considerable value where FWI until now has been impossible to apply successfully from a poor starting model.

Computation of synthetic seismogram in real earth model by eigen function expansion

The method of eigen function expansion has been used in the present study to compute synthetic or theoretical seismogram in layered elastic half-space of real earth model. Simple dislocation source model has been considered. The transverse (SH) or radial and vertical (P-SV) components of displacement field have been computed as summed modes and compared by using both exact and numerical techniques. The methods used in the study, include exact evaluation by propagator matrix approach using Reflection-Transmission coefficients as well as numerical computations using Runge-Kutta method of order 4. The specialty of the present study is to evaluate approximate displacement field for the earth models with homogeneous and / or inhomogeneous layers. The normalization technique has been used in the study to control the overflow errors. The study has an advantage to get an idea of earth structure or source model by an inverse iterative technique.