Uncertainty in the determination of soil hydraulic parameters and its influence on the performance of two hydrological models of different complexity

Data of soil hydraulic properties forms often a limiting factor in unsaturated zone modelling, especially at the larger scales. Investigations for the hydraulic characterization of soils are time-consuming and costly, and the accuracy of the results obtained by the different methodologies is still debated. However, we may wonder how the uncertainty in soil hydraulic parameters relates to the uncertainty of the selected modelling approach. We performed an intensive monitoring study during the cropping season of a 10 ha maize field in Northern Italy. The data were used to: i) compare different methods for determining soil hydraulic parameters and ii) evaluate the effect of the uncertainty in these parameters on different variables (i.e. evapotranspiration, average water content in the root zone, flux at the bottom boundary of the root zone) simulated by two hydrological models of different complexity: SWAP, a widely used model of soil moisture dynamics in unsaturated soils based on Richards equation, and ALHyMUS, a conceptual model of the same dynamics based on a reservoir cascade scheme. We employed five direct and indirect methods to determine soil hydraulic parameters for each horizon of the experimental profile. Two methods were based on a parameter optimization of: a) laboratory measured retention and hydraulic conductivity data and b) field measured retention and hydraulic conductivity data. The remaining three methods were based on the application of widely used Pedo-Transfer Functions: c) Rawls and Brakensiek, d) HYPRES, and e) ROSETTA. Simulations were performed using meteorological, irrigation and crop data measured at the experimental site during the period June – October 2006. Results showed a wide range of soil hydraulic parameter values generated with the different methods, especially for the saturated hydraulic conductivity Ksat and the shape parameter a of the van Genuchten curve. This is reflected in a variability of the modeling results which is, as expected, different for each model and each variable analysed. The variability of the simulated water content in the root zone and of the bottom flux for different soil hydraulic parameter sets is found to be often larger than the difference between modeling results of the two models using the same soil hydraulic parameter set. Also we found that a good agreement in simulated soil moisture patterns may occur even if evapotranspiration and percolation fluxes are significantly different. Therefore multiple output variables should be considered to test the performances of methods and model

Optoelectronic Reservoir Computing

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.Comment: Contains main paper and two Supplementary Material

Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio

Predicting oil field performance using machine learning programming : a comparative case study from the UK continental shelf

Funding Information: Author contributions UO: data curation (lead), formal analysis (lead), funding acquisition (lead), methodology (equal), validation (lead), visualization (equal), writing – original draft (lead); JH: conceptualization (lead), methodology (equal), supervision (lead), visualization (equal), writing – review & editing (lead) Funding This work was funded by the Petroleum Technology Development Fund (PTDF/ED/OSS/PHD/OU/1188/17).Peer reviewedPublisher PD

Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

Permeability is a key parameter that affects fluids flow in reservoir and its accurate determination is a significant task. Permeability usually is measured using practical approaches such as either core analysis or well test which both are time and cost consuming. For these reasons applying well logging data in order to obtaining petrophysical properties of oil reservoir such as permeability and porosity is common. Most of petrophysical parameters generally have relationship with one of well logged data. But reservoir permeability does not show clear and meaningful correlation with any of logged data. Sonic log, density log, neutron log, resistivity log, photo electric factor log and gamma log, are the logs which effect on permeability. It is clear that all of above logs do not effect on permeability with same degree. Hence determination of which log or logs have more effect on permeability is essential task. In order to obtaining mathematical relationship between permeability and affected log data, fitting statistical nonlinear models on measured geophysical data logs as input data and measured vertical and horizontal permeability data as output, was studied. Results indicate that sonic log, density log, neutron log and resistivity log have most effect on permeability, so nonlinear relationships between these logs and permeability was done

Hypothesis-based machine learning for deep-water channel systems

2020 Spring.Includes bibliographical references.Machine learning algorithms are readily being incorporated into petroleum industry workflows for use in well-log correlation, prediction of rock properties, and seismic data interpretation. However, there is a clear disconnect between sedimentology and data analytics in these workflows because sedimentologic data is largely qualitative and descriptive. Sedimentology defines stratigraphic architecture and heterogeneity, which can greatly impact reservoir quality and connectivity and thus hydrocarbon recovery. Deep-water channel systems are an example where predicting reservoir architecture is critical to mitigating risk in hydrocarbon exploration. Deep-water reservoirs are characterized by spatial and temporal variations in channel body stacking patterns, which are difficult to predict with the paucity of borehole data and low quality seismic available in these remote locations. These stacking patterns have been shown to be a key variable that controls reservoir connectivity. In this study, the gap between sedimentology and data analytics is bridged using machine learning algorithms to predict stratigraphic architecture and heterogeneity in a deep-water slope channel system. The algorithms classify variables that capture channel stacking patterns (i.e., channel positions: axis, off-axis, and margin) from a database of outcrop statistics sourced from 68 stratigraphic measured sections from outcrops of the Upper Cretaceous Tres Pasos Formation at Laguna Figueroa in the Magallanes Basin, Chile. An initial hypothesis that channel position could be predicted from 1D descriptive sedimentologic data was tested with a series of machine learning algorithms and classification schemes. The results confirmed this hypothesis as complex algorithms (i.e., random forest, XGBoost, and neural networks) achieved accuracies above 80% while less complex algorithms (i.e., decision trees) achieved lower accuracies between 60%-70%. However, certain classes were difficult for the machine learning algorithms to classify, such as the transitional off-axis class. Additionally, an interpretive classification scheme performed better (by around 10%-20% in some cases) than a geometric scheme that was devised to remove interpretation bias. However, outcrop observations reveal that the interpretive classification scheme may be an over-simplified approach and that more heterogeneity likely exists in each class as revealed by the geometric scheme. A refined hypothesis was developed that a hierarchical machine learning approach could lend deeper insight into the heterogeneity within sedimentologic classes that are difficult for an interpreter to discern by observation alone. This hierarchical analysis revealed distinct sub-classes in the margin channel position that highlight variations in margin depositional style. The conceptual impact of these varying margin styles on fluid flow and connectivity is shown

Developing tools for determination of parameters involved in CO₂ based EOR methods

To mitigate the effects of climate change, CO₂ reduction strategies are suggested to lower anthropogenic emissions of greenhouse gasses owing to the use of fossil fuels. Consequently, the application of CO₂ based enhanced oil recovery methods (EORs) through petroleum reservoirs turn into the hot topic among the oil and gas researchers. This thesis includes two sections. In the first section, we developed deterministic tools for determination of three parameters which are important in CO₂ injection performance including minimum miscible pressure (MMP), equilibrium ratio (Kᵢ), and a swelling factor of oil in the presence of CO₂. For this purposes, we employed two inverse based methods including gene expression programming (GEP), and least square support vector machine (LSSVM). In the second part, we developed an easy-to-use, cheap, and robust data-driven based proxy model to determine the performance of CO₂ based EOR methods. In this section, we have to determine the input parameters and perform sensitivity analysis on them. Next step is designing the simulation runs and determining the performance of CO₂ injection in terms of technical viewpoint (recovery factor, RF). Finally, using the outputs gained from reservoir simulators and applying LSSVM method, we are going to develop the data-driven based proxy model. The proxy model can be considered as an alternative model to determine the efficiency of CO₂ based EOR methods in oil reservoir when the required experimental data are not available or accessible

Prediction of Liquid Accumulation in Gas Wells to Forecast the Critical Flowrate and the Loading Status of Individual Wells

Liquid accumulation is a major problem in gas wells. The inability of gas to lift coproduced liquids to the surface imposes back pressure on the reservoir, limits the ultimate recovery and ultimately kills the well if improperly managed. Therefore, accurate prediction of its occurrence and reliable monitoring strategy is key to effectively handling liquid accumulation in gas wells. In this study, machine learning algorithms were used to develop regression and classification models to accurately predict the critical flowrate and the loading status of individual wells. The regression models used are the feed-forward neural network and a least squares support vector machine models while the decision trees model was used as the classification model to characterize the loading status of the wells investigated. These models were validated using actual published data and it was observed that the feed-forward neural network performed better in predicting the critical rate compared to the least squares support vector machine model with an R2 value of 0.9833, and thus was adopted. The feed-forward neural network model was further compared with other critical rate models; and a consistent result with least percent error of 5.571% was also observed.  Form this study, it is obvious that the neural network model provide benefits and good prospects in investigating liquid loading phenomena in gas wells compared to empirical models that apply so many simplifying assumptions