Flexural-dipole sonic logging has been widely used as the primary method to measure formation shear slowness because it can be applied in both fast and slow formations and can resolve azimuthal anisotropy. The flexuraldipole waveforms are dispersive borehole-guided waves that are sensitive to borehole geometry, mud and formation properties, and therefore the processing techniques need to honor the physical dispersive signatures to obtain an accurate estimation of shear slowness. Traditional processing techniques are based on either a modeldependent method, in which an isotropic model is used as a reference to compensate for the dispersion effect, or a model-independent method, which optimizes nonphysical parameters to fit a simplified model to the field dispersion data extracted in the slowness-frequency domain. Many methods often require inputs such as mud slowness, frequency bandpass filter, or an initial guess of formation shear. Consequently, these methods often fail to interpret the dispersion signature properly when those inputs are inaccurate or when the waveform quality is poor due to downhole logging noises. The users must manually tune the processing parameters and/or choose different methods as a workaround, which causes extra time and effort to obtain the result hence imposes a significant challenge for automating sonic shear interpretation. We develop a physics-driven machine learning-based method for enhancing the interpretation of borehole sonic dipole data for both wireline logging and loggingwhile- drilling. Extensive synthetic databases (i.e., lookup tables) are generated from an anisotropic root-finding mode-search routine and used to train neural network models as accurate and efficient proxies. Those neural network proxies can be used for real-time sensitivity analysis and performing inversion to the measured sonic dipole dispersion data to estimate relevant model parameters with associated uncertainties. Alternatively, various machine learning methods can also be developed based on the generated training dataset and that can be used for inferring relevant model parameters with uncertainties from the field data directly. We introduce how these trained models can be used to enhance the labeling and extraction of different dispersion modes. We developed a new method that outperforms previous modeldependent and model-independent approaches because the new method introduces a mechanism to constrain the solution with physics that also has the capability to incorporate more complicated physical dispersion signatures. This new method needs neither prior information such as mud slowness and formation shear slowness, nor any tuning parameter to be played by the user. It also paves a way to automatically identify different anisotropy mechanisms such as intrinsic, layering, stress, or fractures. This leads to significant progress toward automated sonic interpretation. The algorithm and workflow have been tested on field data for challenging borehole and geological conditions and compared with traditional flexural-dipole processing techniques with great success.

ABSTRACT Today, many machine learning techniques are regularly employed in petrophysical modelling such as cluster analysis, neural networks, fuzzy logic, self-organising maps, genetic algorithm, principal component analysis etc. While each of these methods has its strengths and weaknesses, one of the challenges to most of the existing techniques is how to best handle the variety of dynamic ranges present in petrophysical input data. Mixing input data with logarithmic variation (such as resistivity) and linear variation (such as gamma ray) while effectively balancing the weight of each variable can be particularly difficult to manage. A novel method - Domain Transfer Analysis (DTA) - has been developed which uses a non-linear partial differential equation solver for predicting log curves, enabling more effective integration of disparate data types. DTA is conceived based on extensive research conducted in the field of CFD (Computational Fluid Dynamics). This paper is focused on the application of DTA to petrophysics and its fundamental distinction from various other statistical methods adopted in the industry. Case studies are shown, predicting porosity and permeability for a variety of scenarios using the DTA method and other techniques. The results from the various methods are compared, and the robustness of DTA is illustrated. The example datasets are drawn from public databases within the Norwegian and Dutch sectors of the North Sea, and Western Australia, some of which have a rich set of input data including logs, core, and reservoir characterisation from which to build a model, while others have relatively sparse data available allowing for an analysis of the effectiveness of the method when both rich and poor training data are available. The paper concludes with recommendations on the best way to use DTA in real-time to predict porosity and permeability. The future and ongoing applications of DTA for petrophysical analysis encompasses saturation, TOC, mineral volumes, and brittleness from the data that are available at varying stages of the drilling and completions process.

Abstract An approach to identifying log-derived carbonate facies is based on recognition of the rock textures from microresistivity images with the help of the core-calibrated microresistivity images. The texture recognition was done in two ways: firstly, a detailed manual facies interpretation using the geologist's observation of the microresistivity images; and secondly, an automated process for quick facies interpretation using a neural network model. Both of the methods required direct comparison with core and images to train the geologist and the neural network model. Manual interpretation allows the geologist to integrate geological background knowledge of the field to classify facies types in a logical sequence, whereas the information for the neural network model is restricted to data from cored intervals. Carbonate facies have a close relationship with the heterogeneities of carbonate reservoirs, such as extremely heterogeneous porosity and permeability. Normally, it is very difficult to use conventional well logs to recognize the different types of carbonate facies because facies with different types of rock textures and sedimentary structures have similar well log responses. However, high-resolution microresistivity images are very sensitive to the different types of depositional textures and sedimentary structures, such as grains, the shape of beds, concretions, cleavages, and fossil content. These features typify the facies within a particular carbonate depositional environment. Comparison of the real core textures with the microresistivity images shows that many of the lithofacies types, such as rudist-dominated floatstones/rudstones, variably bioturbated orbitolinid-rich grainstones/packstones, and clay-rich wackestones/packstones, are identifiable in core and can be resolved with the high-resolution images. Through the recognition of carbonate facies on microresistivity images, six distinct depositional facies were identified within the nine wells involved in this study. Facies analysis constrains the carbonate depositional environment, which controls petrophysical properties such as porosity and permeability. Facies analysis also helps to identify the carbonate reservoirs within particular well configurations, such as horizontal wells. Introduction The carbonate facies presented in this paper are from the Upper Shuaiba Formation, a Cretaceous carbonate formation, in a field in Oman. The Upper Shuaiba Formation was divided into three geological depositional environments, characterized by different lithofacies associations: The inner ramp to proximal midramp deposits, which contain the floatstone/rudstone [FR] and skeletal grainstone/packstone [GPs] lithofacies. The midramp deposits, which are composed of the peloidal skeletal packstone [Pps] and peloidal skeletal wackestone [Wps] lithofacies. The distal midramp to outer ramp deposits, which contain the peloidal skeletal packstone [Pps], peloidal skeletal wackestone [Wps] and clay-prone peloidal skeletal wackestone [Wpsc] lithofacies. Individual lithofacies existed in several lithofacies associations, e.g., peloidal skeletal packstones were present in the inner ramp, midramp, and outer ramp deposits. However, each of the lithofacies within a specific lithofacies association had its own particular depositional textures and sedimentary structures observed in the core and the microresistivity images. The high level of heterogeneity of the Upper Shuaiba Formation carbonates is mainly due to diagenetic mechanisms that have both preserved or enhanced and degraded porosity and permeability. These mechanisms include dissolution and cementation.