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To enhance reservoir evaluation in fresh water and mixed salinity environments, we developed a dielectric processing technique for determining relative permittivity and resistivity from measurements acquired by LWD propagation resistivity tools. This enables calculating formation permittivity at 400 kHz and 2 MHz free of shoulder bed and dip effects, and thus estimating saturation in fresh water and mixed salinity environments. This current paper addresses anisotropy effect on the estimation of permittivity and resistivity, and the influence of extremely high dip angles on the performance of LWD electromagnetic (EM) data processing. The most popular formation model for LWD EM data processing is an isotropic model where the permittivity and resistivity are assumed to be uniform in all directions, i.e., the 0D model. However, when the dip is high, the resistivity anisotropy that are especially prevalent in clastic formations begins to kick in. To address this challenge, we have developed a forward model for LWD propagation resistivity tools in high angle and horizontal wells penetrating multilayer anisotropic formations; the 1D model. This model is supportive of all dip angles and allows for users to specify the anisotropy in both resistivity and permittivity in addition to bed thickness. Extensive forward modelling combined with dielectric processing allowed to properly quantify the 1D inversion results at extreme high dip angles. Very good performance is observed, given the ability to correctly depict high contrast and thin layers if resistivity allows a good dielectric signal. Resistivity 1D inversion shows a very strong and solid output, limiting artifacts to the very high relative formation angles and anisotropy. A low ratio between real and apparent components of the currents is found to be a fundamental condition for the applicability of the 1D inversion, especially for anisotropic scenario. The anisotropy ratio is observed to enhance artifact and decrease the capability of the inversion to correctly depict the dielectric constant values in the layers, though in many situation layers could be quantitatively resolved. The 1D inverted resistivity is found to be more robust and tolerant to the high dip angle and anisotropy. Spikes, polarization horns and noise start to appear only at 85° progressively increasing with anisotropy, but layers were rarely obliterated even at the border condition of 85° angle and anisotropy of 5. The use of forward model makes it possible to quantify the anisotropy effect on resistivity and dielectric logs, which can be used to better characterize uncertainty of the two properties where anisotropy effect is non-negligible. It can also assist with understanding the dielectric and resistivity logs and estimating the errors where artifacts are anticipated due to high dips and/or inaccurate dip inputs. The forward model facilitates studying the relationship between formation properties and logs quantitatively. Moreover, when combined with dielectric processing, the model enables an inversion that expands the scope of the problems the dielectric processing can solve.

In the modern oilfield, borehole images can be considered as the minimally representative element of any well-planned geological model/interpretation. In the same borehole it is common to acquire multiple images using different physics and/or resolutions. The challenge for any petro-technical expert is to extract detailed information from several images simultaneously without losing the petrophysical information of the formation. This work shows an innovative approach to combine several borehole images into one new multi-dimensional fused and high-resolution image that allows, at a glance, a petrophysical and geological qualitative interpretation while maintaining quantitative measurement properties. The new image is created by applying color mathematics and advanced image fusion techniques: At the first stage low resolution LWD nuclear images are merged into one multichannel or multiphysics image that integrates all petrophysical measurement’s information of each single input image. A specific transfer function was developed, it normalizes the input measurements into color intensity that, combined into an RGB (red-green-blue) color space, is visualized as a full-color image. The strong and bilateral connection between measurements and colors enables processing that can be used to produce ad-hoc secondary images. In a second stage the multiphysics image resolution is increased by applying a specific type of image fusion: Pansharpening. The goal is to inject details and texture present in a high-resolution image into the low resolution multiphysics image without compromising the petrophysical measurements. The pansharpening algorithm was especially developed for the borehole images application and compared with other established sharpening methods. The resulting high-resolution multiphysics image integrates all input measurements in the form of RGB colors and the texture from the high-resolution image. The image fusion workflow has been tested using LWD GR, density, photo-electric factor images and a high-resolution resistivity image. Image fusion is an innovative method that extends beyond physical constraints of single sensors: the result is a unique image dataset that contains simultaneously geological and petrophysical information at the highest resolution. This work will also give examples of applications of the new fused image.

Analysis of drill-cuttings collected on the rig has always been the most basic, yet most direct means of understanding the subsurface within its own limitations. However, automation enabled by digital transformation of this aspect of mud logging has greatly increased the importance of this data. A futuristic preview is being presented for the repositioning and value showcasing of most basic and widely available data, i.e., cuttings with digital enablement. Cost-efficient characterization with lean sample preparation, reducing the adverse environmental imprint to near real-time formation evaluation leading to enhanced well placement and completion design is reshaping the old-school mudlogging with direct detection and quantification of minerals, total organic carbon (TOC), kerogen content and elemental composition; often minimizing the requirement for time-and-cost intensive wireline logging. Labor-intensive sample collection is getting automated, and subjective and descriptive interpretation per experience of mud-logger is giving way to digital, objective interpretation, ready to be integrated with logging-while-drilling data in real-time. In addition to the X-Ray Fluorescence & Diffraction; newer technologies like Diffused Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) are being incorporated in wellsite set-up with reduced footprint on rig and minimized usage of chemicals. Unique automated process can analyze high resolution digital images to deliver plethora of information in minimum time; often augmented with the help of artificial intelligence. A futuristic view with building blocks of the automated interpretation process is presented. Examples from different steps needed to achieve automation are provided, from sample preparation to digital analysis through machine learning for a holistic futuristic vision to highlight digital enablement in delivering the well-objectives in cost-efficient and timely manner honoring the changing market dynamics. This foundational cutting analysis (Geology 101) vision would drive further adavnces in this field.