Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Assisted history matching using pattern recognition technology

Reservoir simulation and modeling is utilized throughout field development in different capacities. Sensitivity analysis, history matching, operations optimization and uncertainty assessment are the conventional analyses in full field model studies. Realistic modeling of the complexities of a reservoir requires a large number of grid blocks. As the complexity of a reservoir increases and consequently the number of grid blocks, so does the time required to accomplish the abovementioned tasks.;This study aims to examine the application of pattern recognition technologies to improve the time and efforts required for completing successful history matching projects. The pattern recognition capabilities of Artificial Intelligence and Data Mining (AI&DM;) techniques are used to develop a Surrogate Reservoir Model (SRM) and use it as the engine to drive the history matching process. SRM is a prototype of the full field reservoir simulation model that runs in fractions of a second. SRM is built using a small number of geological realizations.;To accomplish the objectives of this work, a three step process was envisioned:;• Part one, a proof of concept study: The goal of first step was to prove that SRM is able to substitute the reservoir simulation model in a history matching project. In this part, the history match was accomplished by tuning only one property (permeability) throughout the reservoir.;• Part two, a feasibility study: This step aimed to study the feasibility of SRM as an effective tool to solve a more complicated history matching problem, particularly when the degrees of uncertainty in the reservoir increase. Therefore, the number of uncertain reservoir properties increased to three properties (permeability, porosity, and thickness). The SRM was trained, calibrated, and validated using a few geological realizations of the base reservoir model. In order to complete an automated history matching workflow, the SRM was coupled with a global optimization algorithm called Differential Evolution (DE). DE optimization method is considered as a novel and robust optimization algorithm from the class of evolutionary algorithm methods.;• Part three, a real-life challenge: The final step was to apply the lessons learned in order to achieve the history match of a real-life problem. The goal of this part was to challenge the strength of SRM in a more complicated case study. Thus, a standard test reservoir model, known as PUNQ-S3 reservoir model in the petroleum engineering literature, was selected. The PUNQ-S3 reservoir model represents a small size industrial reservoir engineering model. This model has been formulated to test the ability of various methods in the history matching and uncertainty quantification. The surrogate reservoir model was developed using ten geological realizations of the model. The uncertain properties in this model are distributions of porosity, horizontal, and vertical permeability. Similar to the second part of this study, the DE optimization method was connected to the SRM to form an automated workflow in order to perform the history matching. This automated workflow is able to produce multiple realizations of the reservoir which match the past performance. The successful matches were utilized to quantify the uncertainty in the prediction of cumulative oil production.;The results of this study prove the ability of the surrogate reservoir models, as a fast and accurate tool, to address the practical issues of reservoir simulation models in the history matching workflow. Nevertheless, the achievements of this dissertation are not only aimed at the history matching procedure, but also benefit the other time-consuming operations in the reservoir management workflow (such as sensitivity analysis, production optimization, and uncertainty assessment)

Method comparison for gas lift allocation aimed at multiple gas lift wells

Continuous gas lift source is essential which allows each of gas lift wells to produce. However, the problem is the amount of total gas lift availability for a field is typically limited. Therefore engineers have to use the total available gas to allocate to all or selected gas lift wells in the field. One of the approaches is to apply the same amount of gas lift injected for each well in a field, but this method is not optimum especially for wells that have different gas lift performance. This study has been executed to compare different methods in the curve based model for gas lift allocation aimed multiple wells to maximize the total production rate in a field. In the curve based model, three methods of optimization have studied which are Binary Integer Linear Optimization, General Reduced Gradient (GRG) Optimization, and Evolutionary Optimization. General Allocation Program (GAP) software has been used to model and compute the optimum allocation and has used as a benchmark in this thesis. Result confirmed that optimize allocation can deliver more production compare to the average amount of gas lift method. Additionally, best curve fit equation in the curve based method for non-linear equation has been computed to represent the gas lift performance curve. Alarcon equation is the best curve fit equation compared to Hamedi, Haiquan, and Viera. GRG Optimization has the fastest computing time and as accurate as an Evolutionary Optimization method. Binary Integer Linear intuitively has provided better gas lift allocation comparing to the GRG and Evolutionary Method

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable

MOTOR STARTING STUDIES IN ELECTRICAL DISTRIBUTION NETWORK SYSTEM

Application of motor has been used widely in industry as it is now become very essential for an industry through out these years. Various type and size of motors are used to serve different type of purposes. An electrical power network distribution system has to be designed in such a way that it can sustain the power required by these motor, specifically HV motor. During starting process, a HV motor requires a large amount of reactive power and drags generally six times its full load current in order to magnetize the core. This tremendous demand normally will leave an effect in an electrical distribution network if it is not properly designed and reduce motor starting performance itself. The objective of this project is mainly to analyze a given electrical distribution network system capability of handling motor demand on power consumption during its starting process and come out with recommendations for the network. In this project student will be doing an analysis on the network performance by doing load flow studies and recommend a strategic planning for motor starting process to suit the network capability. PETRONAS Penapisan (Melaka) Sdn. Bhd. has been chosen as a network model to perform this study. There are three major steps involved in performing this project. In early stage, load flow is required to be performed in order to analyze the network condition prior to motor starting process. At the second stage, a time domain simulation was performed. From here, the effect of motor load on the system can be seen. At the end of the stage, few suggestions on improvement will be developed as to enhance the performance of motor starting in the network. With help from the experts and continuous hard work, hopefully this project will be able to benefit the student in acquiring knowledge and generally to the electrical power distribution network in enhancing the network performance

Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment

Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future

Patterns of Innovation in UK Industry: Exploring the CIS Data to Contrast High and Low Technology Industries.

This paper is divided into two parts. The first part is an examination of the OECD classification of industries into high, medium and low technology industries, to look at the basis for this classification and to use that as a benchmark with which to classify the Community Innovation Survey (CIS) data for the UK into similar groupings. The industries are ranked according to their research intensities and the rankings between the two datasets are compared. Some features of the UK rankings are highlighted and anomalies between the two datasets pointed out. The second part of the paper goes on to use the OECD classification into high, medium and low technology industries, applied to the CIS dataset, to contrast patterns of innovation in high technology industries with those in low technology industries. We build on the three types of innovation surveyed in the CIS, namely product, process and organisational innovation and contrast those types across high and low technology sectors. The expected relationship between high technology industries and product innovation holds - that enterprises tend to do more product innovation, the higher their research intensity. But process innovation does not conform to this pattern and there is not such a clear division between high and low technology industries. However the way they do process innovations differs with high technology industries more reliant on internal resources whereas lower technology industries tend to do it using external resources in collaboration with others. Organisational innovation is more complex, with certain types of innovation done as widely by lower technology industries as by the more research intensive industries. This supports the idea that all types of innovation should be considered, with the diffusion of ICTs making an impact across the technological spectrum of industries and showing up in various forms of organisational innovation

Advancing automation and robotics technology for the Space Station and for the US economy. Volume 1: Executive overview

In response to Public Law 98-371, dated July 18, 1984, the NASA Advanced Technology Advisory Committee has studied automation and robotics for use in the Space Station. The Executive Overview, Volume 1 presents the major findings of the study and recommends to NASA principles for advancing automation and robotics technologies for the benefit of the Space Station and of the U.S. economy in general. As a result of its study, the Advanced Technology Advisory Committee believes that a key element of technology for the Space Station is extensive use of advanced general-purpose automation and robotics. These systems could provide the United States with important new methods of generating and exploiting space knowledge in commercial enterprises and thereby help preserve U.S. leadership in space

An Independent Review of USGS Circular 1370: An Evaluation of the Science Needs to Inform Decisions on Outer Continental Shelf Energy Development in the Chukchi and Beaufort Seas, Alaska

Reviews the U.S. Geological Survey's findings and recommendations on Alaska's Arctic Ocean, including geology, ecology and subsistence, effect of climate change on, and impact of oil spills. Makes recommendations for data management and other issues