An approximate nearest neighbors search algorithm for low-dimensional grid locations

We propose a new algorithm for the problem of approximate nearest neighbors (ANN) search in a regularly spaced low-dimensional grid for interpolation applications. It associates every sampled point to its nearest interpolation location, and then expands its influence to neighborhood locations in the grid, until the desired number of sampled points is achieved on every grid location. Our approach makes use of knowledge on the regular grid spacing to avoid measuring the distance between sampled points and grid locations. We compared our approach with four different state-of-the-art ANN algorithms in a large set of computational experiments. In general, our approach requires low computational effort, especially for cases with high density of sampled points, while the observed error is not significantly different. At the end, a case study is shown, where the ionosphere dynamics is predicted daily using samples from a mathematical model, which runs in parallel at 56 different longitude coordinates, providing sampled points not well distributed that follow Earth’s magnetic field-lines. Our approach overcomes the comparative algorithms when the ratio between the number of sampled points and grid locations is over 2849:1.     

Contribution to characterisation of biochar to estimate the labile fraction of carbon

Different analytical techniques were used to find the most reliable and economic method for determining the labile fraction of C in biochar. Biochar was produced from pine, poplar and willow (PI, PO and WI, respectively) at two temperatures (400 and 550 °C) and characterised using spectroscopic techniques [solid state ¹³C nuclear magnetic resonance spectroscopy (NMR)], molecular markers [pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS)], thermogravimetry (TG), elemental composition and wet oxidation (potassium permanganate and potassium dichromate). Short term incubation (110 h) of an A horizon from an Umbrisol amended with the biochar samples at two doses (7.5 and 15 t ha⁻¹) was also carried out to provide supplementary information on the influence of biochar-soil interaction on CO₂ evolution. Spectroscopic analysis demonstrated that the degree of biochar carbonisation was influenced by the type of feedstock and heating conditions and followed the order WI-400 < PI-400 ∼ WI-550 ∼ PO-400 < PO-550 < PI-550. The thermo-labile fraction of the biochar samples, estimated from TG, ranged between 21% and 49%. The fraction of total C oxidised with potassium permanganate (C_per/C_total) was <50 g kg⁻¹ in all cases, whereas potassium dichromate (C_dichro/C_total) oxidation efficiency ranged between 180 and 545 g kg⁻¹. For each type of feedstock, the highest values of either chemically or thermally degradable C corresponded to the biochar produced at low temperature. Results indicate that low cost methodologies, such as dichromate oxidation and TG, reflected the degree of biochar carbonisation, and could therefore be used to estimate the labile fraction of C in biochar.     

Parameter identification of minifrac numerical tests using a gradient boosting-based proxy model and genetic algorithm

In recent years, the petroleum industry has devoted considerable attention to studying fluid flow inside fracture channels due to the discovery of naturally fractured reservoirs. The behavior prediction of these reservoirs is a well-known challenging task, in which the initial stage consists of identifying reservoir hydromechanical parameters. This work proposes an artificial intelligence-based approach to identify hydromechanical parameters from borehole injection pressure curves acquired through minifrac tests. This approach combines proxy modeling with a stochastic optimization algorithm to match observed and predicted borehole pressure curves. Therefore, a gradient boosting-based proxy model is built to predict borehole pressure curves, considering a proper strategy to develop time series modeling. Moreover, a Bayesian optimization algorithm is applied to compute the gradient boosting hyperparameters. In this optimization scenario, this paper proposes an appropriate objective function established from the assumed time series prediction strategy and the k-fold cross-validation. Finally, a genetic algorithm is adopted to identify unknown hydromechanical parameters, solving an inverse problem. Based on the proposed workflow, a study of the importance of the hydromechanical parameters is developed. To assess the methodology applicability, the approach is employed to identify parameters in synthetic and field minifrac tests. The results present how this approach can adequately identify hydromechanical parameters of hydraulic fracturing problems.