SNPfiltR: an R package for interactive and reproducible SNP filtering

Here I describe the novel R package SNPfiltR and demonstrate its functionalities as the backbone of a customizable, reproducible SNP filtering pipeline implemented exclusively via the widely adopted R programming language. SNPfiltR extends existing SNP filtering functionalities by automating the visualization of key parameters such as depth, quality, and missing data, then allowing users to set filters based on optimized thresholds, all within a single, cohesive working environment. All SNPfiltR functions require a vcfR object as input, which can be easily generated by reading a SNP dataset stored as a standard vcf file into an R working environment using the function read.vcfR() from the R package vcfR. Performance benchmarking reveals that for moderately sized SNP datasets (up to 50M genotypes with associated quality information), SNPfiltR performs filtering with comparable efficiency to current state of the art command-line-based programs. These benchmarking results indicate that for most reduced-representation genomic datasets, SNPfiltR is an ideal choice for investigating, visualizing, and filtering SNPs as part of a cohesive and easily documentable bioinformatic pipeline. The SNPfiltR package can be downloaded from CRAN with the command [install.packages(“SNPfiltR”)], and a development version is available from GitHub at: (github.com/DevonDeRaad/SNPfiltR). Additionally, thorough documentation for SNPfiltR, including multiple comprehensive vignettes, is available at the website: (devonderaad.github.io/SNPfiltR/).

Energy Performance Benchmarking of Process Refrigeration Units

Process refrigeration units are one of the major energy consumers at gas processing plants. Owing to the higher energy consumption, evaluation and benchmarking of energy performance of the refrigeration units is very important for identification of energy saving opportunities. In this regard, an energy performance benchmarking study was performed by detailed assessment and evaluation of the existing process refrigeration units to identify potential of energy efficiency improvement. The study encompassed twenty-one (21) process refrigeration units installed at five (05) different sites. The methodology included collection and analysis of design & operation data and review of key variables like percent load, anti-surge valve opening, condensing temperature & pressure and chilling temperature etc. Energy Performance Indicators (EnPIs) considered for the benchmarking were compressor's specific energy, coefficient of performance (COP) and relative COP (RCOP). A thermodynamic model was developed for each unit to ascertain the refrigeration load. Instead of usual high level benchmarking techniques, the study considered unit and equipment level benchmarking which provided better insight of the systems and helped in finding opportunities for energy efficiency improvement. Further, COP which is generally considered as a benchmarking EnPI, only considers refrigeration load and energy consumption, whereas, this study introduced a new EnPI named "Relative COP" which additionally takes into account the chilling and condensing temperatures and gives true energy performance benchmarking.

Improving Re-Fracturing Efficiency and Performance Through Targeted Candidate Well Selection

This paper details how the application of a combination of video-led multi-sensor technology and advanced 3D modelling can help select the best candidate wells for re-fracturing. By shifting focus towards the higher-potential re-frac candidates, operators can maximize return on investment in a multi-billion-dollar market. Deployment of high resolution, high frame rate video and multi-sensor technology enables the selection and prioritization of high-potential candidate wells, through rigless intervention performed as part of the planning phase. Rigorous, perforation-level analysis of this acquired data enables ranking with respect to unstimulated reserves and hazards that may affect re-frac performance to identify the highest potential candidates upon which a bespoke re-frac design can be applied and executed. This engineering-led approach is only possible with up-front data and knowledge gathering, prior to commencing re-frac operations. The result to the operator is higher yields in production gains for an optimized operating expenditure, with production rates potentially reaching or exceeding initial production values. This significantly reduces the time to pay-back the required operating expenditure and increases long-term profitability for the operator. The paper will use real-world case studies to demonstrate how the application of quantified visual analysis, multi-sensor measurements and 3D modelling makes it possible to plan for the best candidate wells for re-fracturing, thus improving the probability of success and increased economic return. The following problem areas will be analyzed in detail: Production Enhancement: Understanding initial frac performance and identifying under stimulated or unexploited reservoir zones. Effective Diversion: Identifying zones of over-stimulation in order to select appropriate diversion solutions to isolate these. Well integrity: 360-degree quantified visual inspection of barrier integrity and evaluation of the limits of operation of the pressure envelope. Wellbore Access: Identifying obstructions such as proppant flow-back or milling debris to assist with wellbore clean-up or quantifying casing deformation for optimization of the plug and perforation BHA design to ensure that target depths are reached. Refrac Optimization: Evaluation and performance benchmarking of refrac stage designs for optimization of fracture initiation and refrac operational costs. The techniques described in this paper involves the application of the world's first array side-view camera combined with auxiliary services, and bespoke data analysis and visualization suite to deliver the results in an intuitive and interactive platform. Further analysis of datasets is performed using patented 3-dimensional forward modelling techniques for computational analysis of well access.