As per EIA, 40% of the world reserves are sour. This has imposed significant challenges and economic burden to monetize ultra-sour gas fields with H2S levels much higher than 5 mol% in the raw gas. Currently most of the ultra-sour gas fields are monetized by using conventional treating technologies like amine solvent process followed a Claus based process to produce sulfur, Sulfur recovery unit and Tail gas treatment unit, SRU-TGTU. This process is extremely capital and operating cost prohibitive as standalone processes. Also, the overall Sulfur production requires a local demand to address overall sulfur disposal cost. With growing number of fields with higher than 5-36% H2S, ultra-sour fields, it’s difficult for operators to maintain healthy production profits while producing such large quantity of sulfur. With ultra-sour gas production, it has many Health Safety and Environmental (HSE) challenges. A new gas treating technology approach is developed to address ultra-sour fields. The approach is using hybrid process by using state of the art H2S removal membranes to do bulk separation of H2S upstream followed by small amine and Claus plant. This is ideal solution where unique membranes have ability to withstand high H2S environment without altering its performance. These membranes will separate H2S enriched stream which is ideal for reinjection or potentially used for Enhanced Oil Recovery (EOR). The membranes retain maximum hydrocarbons in the high-pressure product gas which very valuable in gas production. Low pressure H2S rich stream is water dry and can be reinjected directly. These membranes will also address CO2 capture along with H2S removal. Using combination of unique membrane technology with smaller amine and Claus plant will reduce the overall CAPEX and OPEX requirement for a given project budget. Membranes are much safer and does not have any emission issues. This will allow plants to be much more HSE safe. Having lower CAPEX and overall lower total cost of ownership (TCO) will allow operators to monetize ultra-sour gas fields and provide better return on investment compared to standalone large sulfur plants.
A huge amount of data is produced in every domain these days. Thus for applying automation on any dataset, the appropriately trained data plays an important role in achieving efficient and accurate results. According to data researchers, data scientists spare 80% of their time in preparing and organizing the data. To overcome this tedious task, IBM Research has developed a Data Quality for AI tool, which has varieties of metrics that can be applied to different datasets (in .csv format) to identify the quality of data. In this paper, we will be representing how the IBM API toolkit will be useful for different variants of datasets and showcase the results for each metrics in graphical form. This paper might be found useful for the readers to understand the working flow of the IBM data purifier tool, thus we have represented the entire flow of how to use IBM data quality for the AI toolkit in the form of architecture.
Schlumberger has developed a unique PN-1 membrane technology in collaboration with Petronas. The technology is unique in combining two distinct types of membrane fibers in one single membrane module to reduce the overall membrane requirement by 10% and offers overall CAPEX and OPEX savings. The PN-1 technology was developed in 2009 and was successfully tested onshore and offshore facilities for total 5 years. The PN-1 technology was first deployed in an onshore gas processing facility which was awarded to Schlumberger in 2013. The facility comprised of membrane pretreatment which is mainly gas dehydration, dew pointing followed by several PN-1 membranes in first stage. The membrane design was unique to handle variable inlet feed conditions from 25 to 12% CO2 inlet gas and outlet gas at 8% CO2. The feed gas design flowrate is 700 MMSCFD and at 750 psig operating pressure. Since this is an onshore gas receiving station, the processing trains should be able to handle variable inlet CO2 concentration in the inlet feed gas and particularly membranes. Schlumberger engineered the entire pretreatment system, membrane and mercaptan removal system. The entire system was delivered and commissioned by Schlumberger on time and was brought online in 2017. The PN-1 membrane system was successful in meeting the required outlet gas CO2 specification while retaining maximum hydrocarbons in the product gas.
We present a new digital solution based on a novel technique to predict acid gas membranes remaining performance based on the field data. Gas membranes are widely used onshore and offshore for acid gas removal from natural gas due to their efficiency and compactness. These systems are proven and well accepted, however their performance is highly dependent on field operations practices and conditions of the natural gas stream that feeds the system. If operating conditions are not controlled, the system performance can deteriorate. The weakened performance can lead to undesirable product gas specifications, contractual penalties, unexpected downtime, and ultimately the risk of environmental impact. On the other hand, maintenance anxiety and uncertainty can lead to overspend on membrane elements replacements; increasing overall operating expenditures. We developed the new technique during the past two years to allow the system operator to anticipate performance upsets by predictive monitoring and active machine learning using field operations data of gas membrane systems. This technique has adopted one of recursive Bayesian estimation techniques, linear Kalman filtering, and allows operators to predict and manage remaining membrane performance in the field proactively thereby optimize the membrane replacement expenditure.
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