Insights on Fractured Domains in Reservoirs Resulting from Modeling Complex Geology/Structures - Case Study of the Ratana Field in the Potwar Basin, Pakistan

Exploration success in fold-and-thrust belts, like the Potwar petroleum province, is impacted by seismic imaging challenges and structural complexity. Success partly relies on the ability to validate subsurface models and model a range of properties, such as reservoir permeability. This is particularly important in the case of tight carbonate reservoirs such as the Eocene Sakesar Formation, where the recovery of economic quantities of hydrocarbons is conditioned by the presence of fracture-enhanced permeability. This requires the application of geological and geophysical modeling techniques to address these challenges, to minimize uncertainty and drive exploration success. The interpretation and structural validation of the Ratana structure presented here allows the proposal of a consistent and robust structural model even in areas of higher uncertainty in the data, such as along faults. The dynamically updatable, watertight, complex 3D structural framework created for the top Sakesar reservoir was used in combination with an assisted fault interpretation algorithm to characterize the fault and fracture pattern. The results showed a higher density of high amplitude fractures on the flanks of the structure rather than along the hinge. These results are supported by the incremental strain modeling based on the kinematic evolution of the structure. Together, this helped to characterize potential fracture corridors in areas of the seismic volume that previously proved challenging for human driven interpretation. Our results allow us to reduce the uncertainty related to the geometrical characteristics of the reservoir and provide insights into potential exploration well targets to maximize chances of success, suggesting that permeability and hydrocarbon flow may be higher at the margins of the Ratana structure, and not at the crest, which was the focus of previous exploration and production efforts.

A novel high performing multiplex immunoassay Multi-HTLV for serological confirmation and typing of HTLV infections

Background Human T-Cell Lymphotropic Viruses (HTLV) type 1 and type 2 account for an estimated 5 to 10 million infections worldwide and are transmitted through breast feeding, sexual contacts and contaminated cellular blood components. HTLV-associated syndromes are considered as neglected diseases for which there are no vaccines or therapies available, making it particularly important to ensure the best possible diagnosis to enable proper counselling of infected persons and avoid secondary transmission. Although high quality antibody screening assays are available, currently available confirmatory tests are costly and have variable performance, with high rates of indeterminate and non-typable results reported in many regions of the world. The objective of this project was to develop and validate a new high-performance multiplex immunoassay for confirmation and discrimination of HTLV-1 and HTLV-2 strains. Methodology/Principal findings The multiplex platform was used first as a tool to identify suitable antigens and in a second step for assay development. With data generated on over 400 HTLV-positive blood donors sourced from USA and French blood banks, we developed and validated a high-precision interpretation algorithm. The Multi-HTLV assay demonstrated very high performance for confirmation and strain discrimination with 100% sensitivity, 98.1% specificity and 100% of typing accuracy in validation samples. The assay can be interpreted either visually or automatically with a colorimetric image reader and custom algorithm, providing highly reliable results. Conclusions/Significance The newly developed Multi-HTLV is very competitive with currently used confirmatory assays and reduces considerably the number of indeterminate results. The multiparametric nature of the assay opens new avenues to study specific serological signatures of each patient, follow the evolution of infection, and explore utility for HTLV disease prognosis. Improving HTLV diagnostic testing will be critical to reduce transmission and to improve monitoring of seropositive patients.

Determining reservoir parameters with nonisothermal real gas flow

Creation and developing methods of determining gas reservoir properties are one of the most important gas hydrodynamics tasks as production project efficiency and reservoir exploitation depend upon layer properties knowledge. Nonstationary gas hydrodynamics investigations are one of the base well and layer researching methods. Results of these investigations are interpreted based on solving of linear isothermal gas flow equation. The current investigation describes the nonstationary gas hydrodynamic survey results interpretation algorithm, which is based on nonlinear equations system solving. The system consists of nonlinear nonisothermal real gas flow and energy equations accounting well influence, Joule-Thompson and adiabatic expansion effects. Integro-interpolation and iteration finite methods were used for creating their own numerical algorithm. Numerical programs allow solving as direct as inverse gas flow tasks in the cylindrical layer. For verification of inverse task solution, the survey interpretation results from the real gas field were paralleled with currently methods results and showed sufficient accuracy. The described method allows to interpret survey gas hydrodynamic results accounting real gas and porous matrix properties, and well influence to enhance integrity and precision reservoir properties estimation.

Integrating Horizontal Wellbores When Building a Geological Model of an Offshore Field

With the advent of the equipment for full well logging suite in the horizontal wells, it became possible to evaluate the reservoir's quantitative parameters. However, the original curves are mainly used for this purpose, which leads to significant errors, in particular due to the significant influence of nearby reservoirs on the tools readings in the penetrated deposits. There is a need to discuss the current issues of interpretation in directional, horizontal and multi-lateral wells with the experts. 3DP module in the downhole software platform* allows to evaluate the overall influence of geometric effects, as well as to adjust logging curves for the influence of several reservoirs on the logging tools responses, which are not still taken into account by conventional methods when processing. The modeled density image is especially useful for confirming the model geometry, updating the local dip angle, and identifying areas, where additional features, such as thin layers, are to be added. The accounting for density and neutron porosity for layers in the petrophysical analysis increases the efficiency of calculating clay volume and porosity, which affects the saturation. The authors also proposed a methodology for assessing share of sand component based on RHOB image. Further accounting of NTG, for the correct assessment of the reservoir properties in a heterogeneous reservoir, followed by the data accounting in the geological model. The results obtained in the course of the work allowed to apply the spatial interpretation of horizontal well in geological modeling, as well as to improve the interpretation algorithm.

New Downhole Sand Entry Detection Technology Leads Directly to Successful Remedial Work and Additional Oil Production

The consequences of sand production are often disadvantageous to the short and long-term productivity of the well. Although some wells routinely experience controllable sand production, these are the exception rather than the rule. Sand production and its management over the life of the well is not an attractive situation but is often essential to extract the resource. Knowing the root cause behind sand inflow in a well and the possible results can inform an appropriate strategy to safely extract as much of the resource as possible. The sands in such reservoir units often have high permeability and are mechanically weak and prone to sand production. The producing wells are often completed with gravel-packed completions for efficient sand control. Most of the wells have multi-zone completions for better productivity but this further complicates reservoir characterization. This paper describes the first use of downhole sand impact detection technology in such fields. The sand detection technology integrates the fully digitized high-resolution acquisition with signal processing and interpretation algorithm to enhance the sand particle detections as small as 0.1 mm in diameter and up to 1,500 impacts per second. The tool is designed to immune the sensors from any background noise and gas/liquid jetting effect. A combination of production logging tools (PLT) and the sand impact detection tool, was used to understand four phase zonal contributions (gas, oil, water and sand) and pinpoint sand entry in these cases. Results exceeded expectations and the ability for the sand detection tool to accurately detect the point of sand entry enabled immediate intervention to eliminate sand production in these case studies. One of them also resulted in increased production of 7.4kb/d oil without any sand flow and with greatly reduced gas-oil ratio as compared to pre-intervention production. The work clearly demonstrates the practical and effective use of downhole sand impact detection with new sand detection technology to identify and isolate sand production in wells. The innovative tool design makes it feasible to detect even small sand particles in adverse wellbore conditions and varied production rates, thus adding a detection of the fourth phase in an otherwise three phase production log.

Fabrication of Weak C-Axis Preferred AlN Thin Film for Temperature Measurement

A weak C-axis preferred AlN thin film with a lot of defects was fabricated for temperature measurement. It was found that the (002) diffraction peak of the thin film increased monotonously with the increase in annealing temperature and annealing time. This phenomenon is ascribed to the evolution of defects in the lattice of the AlN film. Therefore, the relationship between defects and annealing can be expressed by the offset of (002) diffraction peak, which can be used for temperature measurement. Furthermore, a temperature interpretation algorithm Equation based on the lattice parameter (2θ), annealing temperature and annealing time was established, and a temperature interpretation software was built with MATLAB. Visual temperature interpretation is realized by the software, and the relative error is less than 7%. This study is of great significance for promoting the accurate temperature measurement on the surface of high temperature component.

Human–Machine Perception of Complex Signal Data

Electrocardiograms (ECGs), which capture the electrical activity of the human heart, are widely used in clinical practice, and notoriously difficult to interpret. Whilst there have been attempts to automate their interpretation for several decades, human reading of the data presented visually remains the ‘gold standard’. We demonstrate how a visualisation technique that significantly improves human interpretation of ECG data can be used as a basis for an automated interpretation algorithm that is more accurate than current signal processing techniques, and has the benefit of the human and machine sharing the same representation of the data. We discuss the potential of the approach, in terms of its accuracy and acceptability in clinical practice.

Transmitted HIV drug resistance and subtype patterns among blood donors in Poland

Surveillance on the HIV molecular variability, risk of drug resistance transmission and evolution of novel viral variants among blood donors remains an understudied aspect of hemovigilance. This nationwide study analyses patterns of HIV diversity and transmitted resistance mutations. Study included 185 samples from the first time and repeat blood donors with HIV infection identified by molecular assay. HIV protease, reverse transcriptase and integrase were sequenced using population methods. Drug resistance mutation (DRM) patterns were analyzed based on the Stanford Interpretation Algorithm and standardized lists of transmitted mutations. Phylogeny was used to investigate subtyping, clustering and recombination patterns. HIV-1 subtype B (89.2%) followed by subtype A6 (7.6%) were predominant, while in three (1.6%) cases, novel recombinant B/A6 variants were identified. Non-B variants were more common among repeat donors (14.5%) compared to the first time ones (1.8%), p = 0.011, with higher frequency (9.9%) of A6 variant in the repeat donor group, p = 0.04. Major NRTI DRMs were observed in 3.8%, NNRTI and PI in 0.6% and INSTI 1.1% of cases. Additionally, E157Q polymorphism was observed in 9.8% and L74I in 11.5% of integrase sequences. Transmission of drug resistance among blood donors remains infrequent. Subtype patters increase in complexity with emergence of novel intersubtype A6B recombinants.

Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data

Technological advances in next-generation sequencing (NGS) have made it possible to uncover extensive and dynamic alterations in diverse molecular components and biological pathways across healthy and diseased conditions. Large amounts of multi-omics data originating from emerging NGS experiments require feature engineering, which is a crucial step in the process of predictive modeling. The underlying relationship among multi-omics features in terms of insulin resistance is not well understood. In this study, using the multi-omics data of type II diabetes from the Integrative Human Microbiome Project, from 10,783 features, we conducted a data analytic approach to elucidate the relationship between insulin resistance and multi-omics features, including microbiome data. To better explain the impact of microbiome features on insulin classification, we used a developed deep neural network interpretation algorithm for each microbiome feature’s contribution to the discriminative model output in the samples.