Frequently a discrepancy is found between the stimulated shale volume (SSV) estimated from production data and the SSV expected from injected water and proppant volume. One possible explanation is the presence of a fracture network, often termed fracture complexity, that may have been opened or reopened during the hydraulic fracturing operation. The main objective of this work is to investigate the role of fracture complexity in resolving the apparent SSV discrepancy and to illustrate whether the presence of reopened natural fracture network can be observed in pressure and production data of shale gas wells producing from two shale formations with different well and reservoir properties. Homogeneous, dual porosity and triple porosity models are investigated. Sensitivity runs based on typical parameters of the Barnett and the Horn River shale are performed. Then the field data from the two shales are matched. Homogeneous models for the two shale formations indicate effective infinite conductivity fractures in the Barnett well and only moderate conductivity fractures in the Horn River shale. Dual porosity models can support effectively infinite conductivity fractures in both shale formations. Dual porosity models indicate that the behavior of the Barnett and Horn River shale formations are different. Even though both shales exhibit apparent bilinear flow behavior the flow behaviors during this trend are different. Evidence of this difference comes from comparing the storativity ratio observed in each case to the storativity ratio estimated from injected fluid volumes during hydraulic fracturing. In the Barnett shale case similar storativity ratios suggest fracture complexity can account for the dual porosity behavior. In the Horn River case, the model based storativity ratio is too large to represent only fluids from hydraulic fracturing and suggests presence of existing shale formation microfractures.

Supervised machine learning (ML) projects require data for model training, validation, and testing. However, the confidential nature of field and well production data often hinders the progress of ML projects. To address this issue, we developed a well simulator that generates realistic well production data based on physical, governing differential equations. The simulation models the reservoir, wellbore, flowline, and choke coupled using transient nodal analysis to solve for transient flow rate, pressure, and temperature as a function of variable choke opening over time in addition to a wide range of static parameters for each component. The simulator’s output is then perturbed using the gauge transfer function to introduce systematic and random errors, creating a dataset for ML projects without the need for confidential production data. We then generated a simulated dataset to train a recurrent neural network (RNN) on the task of classifying well on/off times. This task typically requires a significant number of manhours to manually filter and verify data for hundreds or thousands of wells. Our RNN model achieves high accuracy in classifying the correct on/off labels, representing a promising step towards a fully-automated rate allocation process. Our simulator for well production data can be used for other ML projects, circumventing the need for confidential data, and enabling the study and development of different ML models to streamline and automate various oil and gas work processes. Overall, the success of our RNN model demonstrates the potential of ML to improve the operational efficiency of various oil and gas work processes.

No description available.

In an open economy economic agents distribute their spending between domestic and various import goods and they may reconsider their choice whenever relative international prices change. Armington elasticities quantify these reallocations in demand for goods produced in different countries. Recent analytical frameworks allow to further differentiate between a macro elasticity of substitution between domestic and import goods and a micro elasticity between different import sources. Despite the relevance of Armington elasticities for evaluating trade policy there has been no systematic study on whether micro and macro elasticities significantly differ for highly integrated economies within a free trade area and whether there is a common pattern. Using highly disaggregated data, this paper estimates Armington elasticities for a panel of 15 EMU Member States. Empirical results indicate a significant difference between micro and macro elasticities for up to one half of the consistent product groups considered, implying preferences across EMU countries are not perfectly aligned with non-discriminatory tariffs. I conclude that both the absolute and relative macro elasticities are informative and that heterogeneous preference patterns link to current trade imbalances. (author's abstract) / Series: Department of Economics Working Paper Series

The so-called "Arps" rate decline relations are by far the most widely used tool for assessing oil and gas reserves from rate performance. These relations (i.e., the exponential and hyperbolic decline relations) are empirical where the starting point for their derivation is given by the definitions of the "loss ratio" and the "derivative of the loss ratio", where the "loss ratio" is the ratio of rate data to derivative of rate data, and the "derivative of the loss ratio" is the "b-parameter" as defined by Arps [1945]. The primary goal of this work is the interpretation of the b-parameter continuously over time and thus the better understanding of its character. As is shown below we propose "monotonically decreasing functional forms" for the characterization of the b-parameter, in addition to the exponential and hyperbolic rate decline relations, where the b-parameter is assumed to be zero and constant, respectively. The proposed equations are as follow: b(t)=constant (Arps' hyperbolic rate-decline relation), []tbbtb10exp)(-bt= (exponential function), (power-law function), 10)(btbtb=)/(1)(10tbbtb+= (rational function). The corresponding rate decline relation for each case is obtained by solving the differential equation associated with the selected functional for the b-parameter. The next step of this procedure is to test and validate each of the rate decline relations by applying them to various numerical simulation cases (for gas), as well as for field data cases obtained from tight/shale gas reservoirs. Our results indicate that b-parameter is never constant but it changes continuously with time. The ultimate objective of this work is to establish each model as a potential analysis/diagnostic relation. Most of the proposed models yield more realistic estimations of gas reserves in comparison to the traditional Arps' rate decline relations (i.e., the hyperbolic decline) where the reserves estimates are inconsistent and over-estimated. As an example, the rational b-parameter model seems to be the most accurate model in terms of representing the character of rate data; and therefore, should yield more realistic reserves estimates. Illustrative examples are provided for better understanding of each b-parameter rate decline model. The proposed family of rate decline relations was based on the character of the b-parameter computed from the rate-time data and they can be applied to a wide range of data sets, as dictated by the character of rate data.

This thesis investigates the relationship between social meaning and linguistic change. An important observation regarding spoken languages is that they are constantly changing: the way we speak differs from generation to generation. A second important observation is that spoken utterances convey social as well as denotational meaning: the way we speak communicates something about who we are. How, if at all, are these two characteristics of spoken languages related? Many sociolinguistic studies have argued that the social meaning of linguistic features is central to explaining the spread of linguistic innovations. A novel form might be heard as more prestigious than the older form, or it may become associated with specific social stereotypes relevant to the community in which the change occurs. It is argued that this association between a linguistic variant and social meaning leads speakers to adopt or reject the innovation, inhibiting or facilitating the spread of the change. In contrast, a number of scholars have argued that social meaning is epiphenomenal to many linguistic changes, which are instead driven by an automatic process of convergence in face-to-face interaction. The issue that such arguments raise is that many studies proposing a role of social meaning in the spread of linguistic innovations rely on production data as their primary source of evidence. Observing the variable adoption of innovations across different groups of speakers (e.g. by gender, ethnicity, or socioeconomic status), a researcher might draw on their knowledge of the social history of the community under study to infer the role of social meaning in that change. In many cases, the observed patterns of could equally be explained by the social structure of the community under study, which constrains who speaks to whom. Are linguistic changes facilitated and inhibited by social meaning? Or is it rather the case that social meaning arises as a consequence of linguistic change, without necessarily influencing the change itself? This thesis explores these questions through a study of vocalic change in York, Northern England, focusing on the fronting and diphthongization of the tense back vowels /u/ and /o/. It presents a systematic comparison of the social meanings listeners assign to innovations (captured using perceptual methods), their social attitudes with regard to those meanings (captured through sociolinguistic interviews), and their use of those forms in production (captured through acoustic analysis). It is argued that evidence of a consistent relationship between these factors would support the proposal that social meaning plays a role in linguistic change. The results of this combined analysis of sociolinguistic perception, social attitudes and speech production provide clear evidence of diachronic /u/ and /o/ fronting in this community, and show that variation in these two vowels is associated with a range of social meanings in perception. These meanings are underpinned by the notion of 'Broad Yorkshire' speech, a socially-recognized speech register linked to notions of authentic local identity and social class. Monophthongal /o/, diphthongal /u/, and back variants of both vowels are shown to be associated with this register, implying that a speaker who adopts an innovative form will likely be heard as less 'Broad'. However, there is no clear evidence that speakers' attitudes toward regional identity or social class have any influence on their adoption of innovations, nor that that their ability to recognise the social meaning of fronting in perception is related to their production behaviour. The fronting of /u/ is spreading in a socially-uniform manner in production, unaffected by any social factor tested except for age. The fronting of /o/ is conditioned by social network structure - speakers with more diverse social networks are more likely to adopt the innovative form, while speakers with closer social ties to York are more likely to retain a back variant. These findings demonstrate that York speakers hear back forms of /u/ and /o/ as more 'local' and 'working class' than fronter realizations, and express strong attitudes toward the values and practices associated with regional identity and social class. However, these factors do not appear to influence their adoption of linguistic innovations in any straightforward manner, contrasting the predictions of an account of linguistic change where social meaning plays a central role in facilitating or inhibiting the propagation of linguistic innovations. Based on these results, the thesis argues that many linguistic changes may spread through the production patterns of a speech community without the direct influence of social meaning, and advocates for the combined analysis of sociolinguistic perception, social attitudes and speech production in future work.

<p> Soybean seed cost has dramatically increased in recent decades which has led to producer interest in lowering input cost through reductions in seeding rate. Fifty-eight seeding rate trials of soybean were conducted at field-scale in Indiana from 2010 to 2021 to update recommendations of seeding rates and plant population. The objectives were to determine the agronomic optimal seeding rate (AOSR) and plant population (AOPP) based on planting equipment, tillage practices, and planting date. Economic optimal seeding rate (EOSR) was also determined based on these field scenarios. Harvest AOPP was not influenced by planting equipment (~212,000 plants ha-1) or tillage (~239,000 plants ha-1), but AOSR varied. Soybean seeded with a row-crop planter optimized grain yield with 352,600 seeds ha-1; whereas, the grain drill required 75,200 more seeds ha-1. Soybean seeded into conventional tillage maximized grain yield at 380,400 seeds ha-1; whereas, under no-till conditions 41,400 more seeds ha-1 were required. Timely planting required 417,300 seeds ha-1 to optimize grain yield, which resulted in harvest AOPP of 216,700 plants ha-1. Conversely, late plantings required 102,800 fewer seeds ha-1 but 36,200 more plants ha-1 than timely planting. Depending on seed cost and soybean market price, seeding rates could be reduced 13,700 to 92,800 seeds ha-1 below AOSR to maximize profit.</p> <p>Secondly, digital imagery with high spatial resolution was collected with an unmanned aerial vehicle (UAV) to develop a simple and practical method to segment soybean from non-plant pixels. The best vegetation indices were selected to segment young soybean plants (VC to V6). Two field-scale trials of soybean were planted in 2020 with the agronomic trial design of two varieties x five seeding rates with three replications. The imagery was collected during this period as it coincides with the time for determining whether a soybean stand should be replanted. Five relative vegetative indices based on the red, green, and blue (RGB) imagery were evaluated: excess greenness index (ExG), excess redness index (ExR), green leaf index (GLI), normalized green-red difference index (NGRDI) and visible atmospheric resistance index (VARI). Both GLI and ExG were superior in overall accuracy compared to all other vegetative indices with very small soybean plants (VC to V1 growth stages). VARI and NGRDI had relatively poor overall accuracy at VC and V1, but had similar overall accuracy to GLI as soybean plants grew larger (V2 to V6 growth stages). Across all growth stages and locations, ExR performed the poorest. Moreover, GLI had consistent performance across the range of growth stages, suggesting its suitability for early soybean stand assessment methods.</p> <p>Six field-scale trials were established in 2020 and 2021 in Indiana with two varieties seeded from 123,000 to 618,000 seeds ha-1. Canopy cover was calculated using GLI to create binary segmentation of plant pixels and non-plant pixels. UAV-derived canopy cover measurements were correlated with plant population of soybean from VC to V4 and growing degree days (GDD) after planting. Yield potential (75, 80, 85, 90, 95, 100%) was correlated with canopy cover from VC to V4 and GDD after planting. Canopy cover of 2.1, 5.0, 8.9 and 13.8% by 150, 250, 350, and 450 GDD°C after planting, respectively, would maximize yield. Canopy cover for 75% yield potential was one-fourth as much as the 100% yield potential. Recommended threshold for replant decisions should be based on canopy cover to attain 95% yield potential. Field observations below a canopy cover of 1.8, 4.2, 7.4, and 11.5% canopy cover by 150, 250, 350, and 450 GDD°C after planting respectively, would consider replanting. </p>

Thesis (Master').

Dans ce travail, nous proposons de suivre une approche multi-échelles pour simuler des propriétés spatiales des réservoirs, permettant d'intégrer des données directes (observation de puits) ou indirectes (sismique et données de production) de résolutions différentes. Deux paramétrisations sont utilisées pour résoudre ce problème: les ondelettes et les pyramides gaussiennes. A l'aide de ces paramétrisations, nous démontrons les avantages de l'approche multi-échelles sur deux types de problèmes d'estimations des incertitudes basés sur la minimisation d'une distance. Le premier problème traite de la simulation de propriétés à partir d'un algorithme de géostatistique multipoints. Il est montré que l'approche multi-échelles basée sur les pyramides gaussiennes améliore la qualité des réalisations générées, respecte davantage les données et réduit les temps de calculs par rapport à l'approche standard. Le second problème traite de la préservation des modèles a priori lors de l'assimilation des données d'historique de production. Pour re-paramétriser le problème, nous développons une transformée en ondelette 3D applicable à des grilles stratigraphiques complexes de réservoir, possédant des cellules mortes ou de volume négligeable. Afin d'estimer les incertitudes liées à l'aspect mal posé du problème inverse, une méthode d'optimisation basée ensemble est intégrée dans l'approche multi-échelles de calage historique. A l'aide de plusieurs exemples d'applications, nous montrons que l'inversion multi-échelles permet de mieux préserver les modèles a priori et est moins assujettie au bruit que les approches standards, tout en respectant aussi bien les données de conditionnement. / In this work, we propose to follow a multi-scale approach for spatial reservoir properties characterization using direct (well observations) and indirect (seismic and production history) data at different resolutions. Two decompositions are used to parameterize the problem: the wavelets and the Gaussian pyramids. Using these parameterizations, we show the advantages of the multi-scale approach with two uncertainty quantification problems based on minimization. The first one concerns the simulation of property fields from a multiple points geostatistics algorithm. It is shown that the multi-scale approach based on Gaussian pyramids improves the quality of the output realizations, the match of the conditioning data and the computational time compared to the standard approach. The second problem concerns the preservation of the prior models during the assimilation of the production history. In order to re-parameterize the problem, we develop a new 3D grid adaptive wavelet transform, which can be used on complex reservoir grids containing dead or zero volume cells. An ensemble-based optimization method is integrated in the multi-scale history matching approach, so that an estimation of the uncertainty is obtained at the end of the optimization. This method is applied on several application examples where we observe that the final realizations better preserve the spatial distribution of the prior models and are less noisy than the realizations updated using a standard approach, while matching the production data equally well.

An automated manufacturing industry makes use of many interacting moving parts and sensors. Data from these sensors generate complex multidimensional data in the production environment. This data is difficult to interpret and also difficult to find patterns in. This project provides tools to get a deeper understanding of Swedsafe’s production data, a company involved in an automated manufacturing business. The project is based on and will show the potential of the multidimensional production data. The project mainly consists of predicting deviations from predefined threshold values in Swedsafe’s production data. Machine learning is a good method of finding relationships in complex datasets. Supervised machine learning classification is used to predict deviation from threshold values in the data. An investigation is conducted to identify the classifier that performs best on Swedsafe's production data. The technique sliding window is used for managing time series data, which is used in this project. Apart from predicting deviations, this project also includes an implementation of live graphs to easily get an overview of the production data. A steady production with stable process values is important. So being able to monitor and predict events in the production environment can provide the same benefit for other manufacturing companies and is therefore suitable not only for Swedsafe. The best performing machine learning classifier tested in this project was the Random Forest classifier. The Multilayer Perceptron did not perform well on Swedsafe’s data, but further investigation in recurrent neural networks using LSTM neurons would be recommended. During the projekt a web based application displaying the sensor data in live graphs is also developed.