Yearly Archives: 2022

Digital twins seek to replicate a physical structure in a digital domain. For a digital twin to have close correspondence to its physical twin, data are required. However, it is not always possible, or cost-effective, to collect a complete set of data for a structure in all configurations of interest. It is nonetheless useful to repurpose data to help validate predictions for different configurations and scenarios. This statement is true in drilling applications, where, for example, the length of the drill string is altered throughout operation. This paper demonstrates how transfer learning, in the form of three domain-adaptation methods, — transfer component analysis (TCA), maximum independence domain adaptation (MIDA) and geodesic flow kernel (GFK) — can be used to construct a digital twin for localising torsional friction in deviated wells under structural changes (e.g., when the drill column gets longer). The method uses a physics-based torsional model to train a machine-learning classifier that can localise torsional friction for a given drill string length and diameter, where friction localisation labels are known (source). As the length or diameter of the drill string are altered in the field, transfer learning is utilised to map the classifier from the labelled (source) scenario onto these unlabelled (target) scenarios. As a result, transfer learning improves the performance of the classifier when applied to the target data, and increases the domain of validity for the classifier. The performance of the classifier, and therefore its suitability to new drill-string configurations, is estimated by utilising two different distance metrics between the source and a proposed target dataset.

Black oil delumping, also known as a stream conversion method, converts a black oil wellstream into a compositional wellstream. This procedure ensures consistent flowrate allocations and monitoring of well’s performance. This method requires volumetric oil and gas flowrates given in well-test reports, an equation of state model, and additional black oil information reported in the Well Test, PVT Analysis, and Gas Chromatographic Analysis. This work proposes an improvement on the method to convert black oil data into compositional wellstream. The method’s performance was tested using data of three wells of a platform from an offshore oil field. This improvement significantly increased the accuracy of the method by decreasing the maximum percentage relative error from 16.50% to 4.44% when comparing the calculated and measured oil and gas properties for Well 1, for example. The method also preserves the gas and oil ratio reported in the well tests.

An appropriate techno-economic assessment of biorefineries is essential for consolidating a bio-based economy. Herein, the effects of scale and seasonality on the design of a sugarcane to ethanol biorefinery were assessed using an extensive process simulation and economic modeling methodology. Four sub-processes were simulated for eight different scales: ethanol from sugarcane, ethanol dehydration to ethylene, ethylene to mono ethylene glycol (MEG), and electricity and steam generation from bagasse. Models for capital expenditure and minimum selling price versus product capacity were developed for ethanol, bio-ethylene, and MEG. The integrated biorefinery was analyzed for 200 kt/yr and 440 kt/yr bio-ethylene capacities, considering three regions of Brazil: Northeast, South-central, and São Paulo/Paraná. Seasonality effects were mitigated by establishing a storage park alongside the biorefinery. The results indicated that the selling price of bio-MEG must be at least 20–30% higher than the fossil-based MEG to ensure competition. The 440 kt/yr biorefinery located in the South-central region had the lowest bio-MEG selling price, however, it required an outsized distillery. Moreover, scale and seasonality had a significant impact on the competitiveness of the biorefinery. The bio-MEG selling price might vary by up to 22%, depending on the specific combination of these factors.