Prediction of the functional properties of ceramic materials from composition using artificial neural networks

We describe the development of artificial neural networks (ANN) for the prediction of the properties of ceramic materials. The ceramics studied here include polycrystalline, inorganic, non-metallic materials and are investigated on the basis of their dielectric and ionic properties. Dielectric materials are of interest in telecommunication applications where they are used in tuning and filtering equipment. Ionic and mixed conductors are the subjects of a concerted effort in the search for new materials that can be incorporated into efficient, clean electrochemical devices of interest in energy production and greenhouse gas reduction applications. Multi-layer perceptron ANNs are trained using the back-propagation algorithm and utilise data obtained from the literature to learn composition-property relationships between the inputs and outputs of the system. The trained networks use compositional information to predict the relative permittivity and oxygen diffusion properties of ceramic materials. The results show that ANNs are able to produce accurate predictions of the properties of these ceramic materials which can be used to develop materials suitable for use in telecommunication and energy production applications

Investigation on Rheology of Oil Well Cement Slurries

The rheology of OWC slurries is generally more complicated than that of conventional cement paste. In order to contend with bottom hole conditions (wide range of pressure and temperature), a number of additives are usually used in the OWC slurries and the slurry shows different characteristics depending on the combination of admixture used. The objective of this research is to develop a fundamental understanding on the important mechanisms that affects the rheology of cement slurry incorporating various chemical and mineral admixtures. The thesis aimed at developing cement slurries by partial replacement of oil well cement using different mineral admixtures, offering both environmental and economic benefit. The mechanisms underlying the effect of chemical admixtures on the rheology of oil well cement slurry were investigated at different temperatures using an advanced shear-stress/shear-strain controlled rheometer. The compatibility and interactions between the binder and chemical admixtures were explored. It was found that the rheological properties of oil well cement slurries are highly dependent on temperature, water/cement ratio and the type of admixture used. Coupled effects of temperature and chemical admixtures had a substantial effect on the flow properties of the slurries. The results indicated that current technical data for chemical admixtures need to be validated for oil well cementing; admixtures proven effective in normal cementing job at moderate temperature may become ineffective for oil well cementing at high temperature. The coupled effects of temperature and supplementary cementing materials on the rheology of oil well cement slurry were also investigated. Because of differences in their chemical compositions and the mechanisms by which they act, cement slurries prepared with the addition of supplementary cementitious materials exhibit different rheological behaviour than those prepared with pure oil well cement. It was found that not all minerals/supplementary cementitious materials (SCMs) act in the same way when used as replacement of cement. For example, Fly ash, owing to its spherical particle shape, reduces the water demand when used as a partial replacement of cement. On the other hand, silica fume increase the water demand by adsorbing water because of their higher surface area. However results suggested that new generation polycarboxylate-based high-range water reducing admixture (PCH) improved the rheological properties of all slurries at all temperature tested. However, lower dosage of PCH was found to be less efficient in reducing the yield stress or plastic viscosity of OWC slurries when metakaolin (MK) or rice husk ash (RHA) was used as replacement of cement. PCH was found to enhance the shear thickening behaviour of oil well cement slurries and the intensity of this behaviour varied with the type and amount of SCM such as the phenomenon was amplified with metakaolin, reduced by SF, unchanged with FA and showed irregular behaviour with RHA. Furthermore, new equations were proposed using multiple regression analysis (MRA) and design of experiments (DOE) to predict the Bingham parameters (yield stress and plastic viscosity) of cement slurries prepared in combination with or without supplementary cementitious materials considering various parameters including the ambient temperature, chemical admixture type and dosage, and superplasticizer type and dosage. An artificial neural network (ANN) model was developed to predict the rheological properties of oil well cement slurries. The results indicated that the predicted rheological parameters for cement slurries were in good agreement with corresponding experimental results. However, the ANN-based model performed better than the MRA-based model or DOE-based model in predicting the rheological properties of OWC slurries

Application Of Artificial Neural Networks To Predict Wettability And Relative Permeability Of Sandstone Rocks

An Artificial Neural Network (ANN) model based on the back-propagation technique is trained with a number of variables from experimentally established relative permeability curves. The reservoir core input data covers an extensive range of porosities and permeabilities from different sandstone lithologies having diverse wettabilities. The trained model is then tested with only a couple of input variables such as the initial connate water saturation, S,»c and the residual oil saturation. So, . The developed model outputs, or the predictions define the relative permeability end-points and the intersection point to quantify the wettability and the shape of the relative permeability curves. A number of correlations based on empirical models and network models exist to predict the relative permeability curves and the wettability of oil bearing sandstone formations from the initial oil and water. Calculations from the ANN model were then compared with values calculated from other models currently in wide spread use

Sand production due to chemical-rock interaction: a review.

Oilfield chemicals are utilized in treating reservoir formations, wellbore completions, wellbore drilling, and to enhance reservoir productivity, which exerts pressure on the formation. Pressure from these processes cause the formation rock to weaken, and the weakened rock begins to detach, thereby producing formation sand as well as reservoir fluid (petroleum). In petroleum industry, sanding poses major challenges with significant financial consequences. The negative financial implications of sand production make it crucial to reduce sand production at the same time as optimizing reservoir fluid production and maintaining facility integrity. An effective way to manage sand production depends on several factors, so a methodical approach is needed. The paper discusses sand production from oilfield chemicals-rock interactions, models that are used to forecast sand production, personnel safety, and maintaining production facilities. In addition to determining sanding onset, some models can detect the rate or quantity of sand produced, which can help with sand management

Depositional Environment, Reservoir Properties, and EOR Potential of an Incised-valley-fill Sandstone, Pleasant Prairie Oilfield, Haskell County, Kansas

Incised-valley-fill deposits form important hydrocarbon reservoirs and can have internal heterogeneities that affect recovery of hydrocarbon resources. Better understanding of the internal heterogeneity of incised-valley-fill reservoirs will help in more accurate reservoir modeling and more efficient recovery of hydrocarbon resources. Pleasant Prairie oilfield in Haskell County, Kansas, produces oil from an incised-valley-fill reservoir in the Chesterian (Upper Mississippian) Shore Airport Formation. The reservoir is part of a larger paleovalley trend interpreted as a tide-dominated, estuarine depositional system; depositional environments within such systems vary spatially as a result of interactions of tidal and fluvial processes. Core analysis suggests that the reservoir at Pleasant Prairie oilfield is a stacked series of conglomerate-based, fining-upward siliciclastic successions deposited in the river-dominated part of a tide-influenced estuarine system. Core petrophysical data and well-log correlations suggest that reservoir heterogeneity occurs in the form of vertical and lateral compartmentalization. Reservoir modeling indicates a current field-wide recovery factor of 0.30&ndash-&ndash 0.36 of original oil in place. Comparison of modeled original oil in place to production data suggests inaccuracy of reservoir models at the scale of individual well drainage areas. Waterflooding of the reservoir has proven successful for >10 years, and remaining oil in place ranges from 7.8&ndash-&ndash 10.1 mmbo according to Petrel10 years, and remaining oil in place ranges from 7.8&ndash-&ndash 10.1 mmbo according to PetrelTM models, indicating potential for future enhanced oil recovery operations such as CO2 or chemical flooding. Other incised-valley-fill reservoirs, such as Morrowan (Lower Pennsylvanian) oilfields in Colorado and Kansas, originated in similar depositional settings and display similar reservoir properties; such reservoirs may also have potential for future enhanced oil recovery operations

Prediction of the functional properties of ceramic materials from composition using artificial neural networks

We describe the development of artificial neural networks (ANN) for the prediction of the properties of ceramic materials. The ceramics studied here include polycrystalline, inorganic, non-metallic materials and are investigated on the basis of their dielectric and ionic properties. Dielectric materials are of interest in telecommunication applications, where they are used in tuning and filtering equipment. Ionic and mixed conductors are the subjects of a concerted effort in the search for new materials that can be incorporated into efficient, clean electrochemical devices of interest in energy production and greenhouse gas reduction applications. Multi-layer perceptron ANNs are trained using the back-propagation algorithm and utilise data obtained from the literature to learn composition–property relationships between the inputs and outputs of the system. The trained networks use compositional information to predict the relative permittivity and oxygen diffusion properties of ceramic materials. The results show that ANNs are able to produce accurate predictions of the properties of these ceramic materials, which can be used to develop materials suitable for use in telecommunication and energy production applications

Intelligent data-driven decision-making to mitigate or stop lost circulation

”Lost circulation is a challenging problem in the oil and gas industry. Each year, millions of dollars are spent to mitigate or stop this problem. The aim of this work is to utilize machine learning and other intelligent solutions to help to make better decision to mitigate or stop lost circulation. A detailed literature review on the applications of decision tree analysis, expected monetary value, and artificial neural networks in the oil and gas industry was provided. Data for more than 3000 wells were gathered from many sources around the world. Detailed economics and probability analyses for lost circulation treatments’ strategies were conducted for three formations in southern Iraq which are the Dammam, Hartha, and Shuaiba formations. Multiple machine learning methods such as support vector machine, decision trees, logistic regression, artificial neural networks, and ensemble trees were used to create models that can predict lost circulation and recommend the best lost circulation treatment based on the type of loss and reason of loss. The results showed that the created models can predict lost circulation and recommend the best lost circulation strategy within a reasonable margin of error. The created models can be used globally which avoids the shortcoming in the literature. Intelligence solutions and machine learning have proven their applicability to solve complicated problems and make better future decisions. With the large data available in the oil and gas industry, these methods can help the decision-makers to make better future decisions that will save time and money”--Abstract, page iv

Caracterização e estudo comparativo de exsudações de hidrocarbonetos e plays petrolíferos em bacias terrestres das regiões central do Irã e sudeste do Brasil usando sensoriamento remoto espectral

Orientador: Carlos Roberto de Souza FilhoTese (doutorado) - Universidade Estadual de Campinas, Instituto de GeociênciasResumo: O objetivo desta pesquisa foi explorar as assinaturas de exsudações de hidrocarbonetos na superfície usando a tecnologia de detecção remota espectral. Isso foi alcançado primeiro, realizando uma revisão abrangente das capacidades e potenciais técnicas de detecção direta e indireta. Em seguida, a técnica foi aplicada para investigar dois locais de teste localizados no Irã e no Brasil, conhecidos por hospedar sistemas ativos de micro-exsudações e afloramentos betuminosos, respectivamente. A primeira área de estudo está localizada perto da cidade de Qom (Irã), e está inserida no campo petrolífero Alborz, enterrado sob sedimentos datados do Oligoceno da Formação Upper Red. O segundo local está localizado perto da cidade de Anhembi (SP), na margem oriental da bacia do Paraná, no Brasil, e inclui acumulações de betume em arenitos triássicos da Formação Pirambóia. O trabalho na área de Qom integrou evidências de (i) estudos petrográficos e geoquímicos em laboratório, (ii) investigações de afloramentos em campo, e (iii) mapeamento de anomalia em larga escala através de conjuntos de dados multi-espectrais ASTER e Sentinel-2. O resultado deste estudo se trata de novos indicadores mineralógicos e geoquímicos para a exploração de micro-exsudações e um modelo de micro-exsudações atualizado. Durante este trabalho, conseguimos desenvolver novas metodologias para análise de dados espectroscópicos. Através da utilização de dados simulados, indicamos que o instrumento de satélite WorldView-3 tem potencial para detecção direta de hidrocarbonetos. Na sequência do estudo, dados reais sobre afloramentos de arenitos e óleo na área de Anhembi foram investigados. A área foi fotografada novamente no chão e usando o sistema de imagem hiperespectral AisaFENIX. Seguiu-se estudos e amostragem no campo,incluindo espectroscopia de alcance fechado das amostras no laboratório usando instrumentos de imagem (ou seja, sisuCHEMA) e não-imagem (ou seja, FieldSpec-4). O estudo demonstrou que uma abordagem espectroscópica multi-escala poderia fornecer uma imagem completa das variações no conteúdo e composição do betume e minerais de alteração que acompanham. A assinatura de hidrocarbonetos, especialmente a centrada em 2300 nm, mostrou-se consistente e comparável entre as escalas e capaz de estimar o teor de betume de areias de petróleo em todas as escalas de imagemAbstract: The objective of this research was to explore for the signatures of seeping hydrocarbons on the surface using spectral remote sensing technology. It was achieved firstly by conducting a comprehensive review of the capacities and potentials of the technique for direct and indirect seepage detection. Next, the technique was applied to investigate two distinctive test sites located in Iran and Brazil known to retain active microseepage systems and bituminous outcrops, respectively. The first study area is located near the city of Qom in Iran, and consists of Alborz oilfield buried under Oligocene sediments of the Upper-Red Formation. The second site is located near the town of Anhembi on the eastern edge of the Paraná Basin in Brazil and includes bitumen accumulations in the Triassic sandstones of the Pirambóia Formation. Our work in Qom area integrated evidence from (i) petrographic, spectroscopic, and geochemical studies in the laboratory, (ii) outcrop investigations in the field, and (iii) broad-scale anomaly mapping via orbital remote sensing data. The outcomes of this study was novel mineralogical and geochemical indicators for microseepage characterization and a classification scheme for the microseepage-induced alterations. Our study indicated that active microseepage systems occur in large parts of the lithofacies in Qom area, implying that the extent of the petroleum reservoir is much larger than previously thought. During this work, we also developed new methodologies for spectroscopic data analysis and processing. On the other side, by using simulated data, we indicated that WorldView-3 satellite instrument has the potential for direct hydrocarbon detection. Following this demonstration, real datasets were acquired over oil-sand outcrops of the Anhembi area. The area was further imaged on the ground and from the air by using an AisaFENIX hyperspectral imaging system. This was followed by outcrop studies and sampling in the field and close-range spectroscopy in the laboratory using both imaging (i.e. sisuCHEMA) and nonimaging instruments. The study demonstrated that a multi-scale spectroscopic approach could provide a complete picture of the variations in the content and composition of bitumen and associated alteration mineralogy. The oil signature, especially the one centered at 2300 nm, was shown to be consistent and comparable among scales, and capable of estimating the bitumen content of oil-sands at all imaging scalesDoutoradoGeologia e Recursos NaturaisDoutor em Geociências2015/06663-7FAPES

Predict the flow of well fluids : a big data approach

Master's thesis in Computer scienceIn the oil and gas industry, millions of records of data are registered every day. The data is composed by a mix of structured and unstructured sources. For example downhole gauges and wellhead sensors are logging pressure and temperature in different places of the well. Emerging technologies such as fiber-optic, wireless communication, allow the sensors to be digital, more accurate and reliable. The number of sensors as well as their resolution increase, which imposes new challenges in terms of amount of data that is necessary to be processed. Moreover, engineers in oil and gas industry store well operations, interventions logs and their interpretation in different files and formats. Combining and analyzing these different types of data is a worthy challenge for each company to obtain valuable information. The production/performance engineers need the production rates to monitor the well situation and optimize the well performance. However, in a production well, the well production rates are not determined in real time. To measure the fluid rates Well Test operation is carried out periodically, normally once a month. However, these rates might change dramatically within two Well Test. For this reason, a model that can predict the fluid rates, gives great advantages to the production engineers to optimize the well performance in real time. In this thesis a real usecase of an oil well was studied and an approach was proposed to process and analysis a large amount of structured and unstructured data using Exploratory Data Analysis (EDA) and design an Artificial Neural Network (ANN) to predict the flow of the well fluids. The usefulness of this method has been proved by predicting the rates of the well fluids for the usecase with reasonable low errors. Moreover, we proposed a second approach that increased even more the network accuracy[lower errors].ConocoPhillip

Reservoir Characterization of the Brae Formation, South Brae Field, UK North Sea

The South Brae Field is located 166 miles off the coast of Aberdeen, Scotland in the UK sector of the North Sea. The Upper Jurassic Brae submarine channel-fan complex deposits are the primary hydrocarbon reservoirs in UK Blocks 16/07a and 16/07b. The field is composed of channelized silici-clastic slope-apron and fan deposits from high- to low-density debris flows, sandy and muddy turbidites, and hemipelagic settling. They were deposited during the Late Oxfordian to Middle Volgian. The Kimmeridge Clay Formation is a regionally-extensive, organic-rich, transgressive shale—deposited concurrently—that separates the Brae Formation from the overlying Cretaceous deposits and serves as the source rock and stratigraphic seal. The reservoir is trapped by the western escarpment of the South Viking Graben, which formed as a result of Permo-Triassic rifting and additional Middle to Late Jurassic rifting events. The Brae formation is composed of seven main lithofacies including conglomerate, pebbly sandstone, sandstone, sandy siltstone, silty shale, shale, and calcite-cemented sandstone. Core descriptions, thin-section petrography, x-ray diffraction, and core plug measurements were used to understand the lithological, depositional, and petrophysical variations of the formation. Log-based, and seismic stratigraphic correlations were used to identify second-, third-, and fourth-order stratigraphic sequences. Thickness maps of the subunits within the Early to Middle Volgian – J66 – third-order sequence and spectral decomposition of the seismic volume aided the identification of depositional fairways within the upper part of the Brae Formation. Electrofacies were generates using supervised multi-variate cluster analysis and artificial neural network classification models. The classifications showed overall accuracies around 90 percent. Facies proportion maps were constructed to understand their lateral distribution within the third-order sequence. Log-based and seismic stratigraphic interpretations of fourth-order sequences within the J66 sequence helped to illustrate the internal distribution of the reservoir-quality facies (sandstones, pebbly sandstones, and conglomerates) within the depositional fairways. The lower subunit of the J66 system – the Ac subunit – is composed of laterally- and vertically-connected channel-fills and fans with a high abundance of thick calcite-cemented sandstone concretions that impede the flow of fluids within the reservoir. The Aa subunit – the upper part of the J66 sequence – primarily contains isolated channel-fills with a smaller amount of calcite concretions. Both subunits are composed of sandstone, pebbly sandstone, and conglomerate channel-fans and fans that were deposited after incision into muddy turbidite units that underlie them