<p>Construction of advanced materials through self-assembly on the molecular level is an important route to achieve novel functionality. Self-assembly of thiols onto gold has during the last decades shown greate promise in the creation of functional nanomaterials, such as sensors or catalysts, but for many applications silicon should be a better substrate since it offers semiconducting properties and better processing abilities in addition to being cheaper. This work describes an efficient novel method to incorporate reactive disulfide bonds onto a silica surface under mild reaction conditions. The reactive thiol groups introduced onto the silica surface will be oxidized but is then converted into highly reactive thiopyridyl groups, which can easily be utilized for further organic synthesis involving thiol-containing molecules. </p><p>Cobalt tetraarylporphyrins with thioacetate-functionalized carbon chains on the aryl groups were synthesized (CoTPP-L) and were used as a model system for investigating catalytic activity in homogeneous and heterogeneous oxidation catalysis. For heterogeneous catalysis CoTPP-L was immobilized onto gold surfaces through thiol-gold self-assembly, and onto silica surfaces by the above mentioned disulfide exchange method.</p><p>The properties of the molecular layers were characterized on the molecular level by means of X-ray photoelectron spectroscopy (XPS) and scanning probe microscopy (SPM). The immobilization on gold surfaces took place through the formation of multiple thiolate bonds and it could be controlled by varying the preparation scheme. More thiolate bonds form if the thioacetyl protective groups of the thiol linkers are cleaved off prior to immobilization. The CoTPP-L molecules were in all cases found to form stable disordered monolayers on gold surfaces. On silica surfaces the CoTPP-L forms patchwise multilayers. </p><p>The catalytic performance of the heterogenized systems (CoTPP-L immobilized onto gold or silicon wafers) was evaluated and it was found that the strong inactivation observed for their homogeneous congener was avoided. As a result, the turnover number per molecule in heterogeneous catalysis was at least 100 times higher than that of the corresponding homogeneous catalyst. It is thus demonstrated that the performance of these catalysts can be dramatically improved if the catalyst arrangement can be controlled on the molecular level. Work is ongoing to extend the system to high surface area materials.</p>

Construction of advanced materials through self-assembly on the molecular level is an important route to achieve novel functionality. Self-assembly of thiols onto gold has during the last decades shown greate promise in the creation of functional nanomaterials, such as sensors or catalysts, but for many applications silicon should be a better substrate since it offers semiconducting properties and better processing abilities in addition to being cheaper. This work describes an efficient novel method to incorporate reactive disulfide bonds onto a silica surface under mild reaction conditions. The reactive thiol groups introduced onto the silica surface will be oxidized but is then converted into highly reactive thiopyridyl groups, which can easily be utilized for further organic synthesis involving thiol-containing molecules. Cobalt tetraarylporphyrins with thioacetate-functionalized carbon chains on the aryl groups were synthesized (CoTPP-L) and were used as a model system for investigating catalytic activity in homogeneous and heterogeneous oxidation catalysis. For heterogeneous catalysis CoTPP-L was immobilized onto gold surfaces through thiol-gold self-assembly, and onto silica surfaces by the above mentioned disulfide exchange method. The properties of the molecular layers were characterized on the molecular level by means of X-ray photoelectron spectroscopy (XPS) and scanning probe microscopy (SPM). The immobilization on gold surfaces took place through the formation of multiple thiolate bonds and it could be controlled by varying the preparation scheme. More thiolate bonds form if the thioacetyl protective groups of the thiol linkers are cleaved off prior to immobilization. The CoTPP-L molecules were in all cases found to form stable disordered monolayers on gold surfaces. On silica surfaces the CoTPP-L forms patchwise multilayers. The catalytic performance of the heterogenized systems (CoTPP-L immobilized onto gold or silicon wafers) was evaluated and it was found that the strong inactivation observed for their homogeneous congener was avoided. As a result, the turnover number per molecule in heterogeneous catalysis was at least 100 times higher than that of the corresponding homogeneous catalyst. It is thus demonstrated that the performance of these catalysts can be dramatically improved if the catalyst arrangement can be controlled on the molecular level. Work is ongoing to extend the system to high surface area materials.

<p>The history of oil well engineering applications has revealed that the frequent operational problems are still common in oil well practice. Well blowouts, stuck pipes, well leakages are examples of the repeated problems in the oil well engineering industry. The main reason why these unwanted problems are unavoidable can be the complexity and uncertainties of the oil well processes. Unforeseen problems happen again and again, because they are not fully predictable, which could be due to lack of sufficient data or improper modelling to simulate the real conditions in the process. Traditional mathematical models have not been able to totally eliminate unwanted oil well problems because of the many involved simplifications, uncertainties, and incomplete information. This research work proposes a new approach and breakthrough for overcoming these challenges. The main objective of this study is merging two scientific fields; artificial intelligence and petroleum engineering in order to implement a new methodology.</p><p>Case-Based Reasoning (CBR) and Model-Based Reasoning (MBR), two branches of the artificial intelligence science, are applied for solving complex oil well problems. There are many CBR and MBR modelling tools which are generally used for different applications for implementing and demonstrating CBR and MBR methodologies; however, in this study, the Creek system which combines CBR and MBR has been utilized as a framework. One specific challenging task related to oil well engineering has been selected to exemplify and examine the methodology. To select a correct candidate for this application was a challenging step by itself. After testing many different issues in the oil well engineering, a well integrity issue has been chosen for the context. Thus, 18 leaking wells, production and injection wells, from three different oil fields have been analysed in depth. Then, they have been encoded and stored as cases in an ontology model given the name Wellogy.</p><p>The challenges related to well integrity issues are a growing concern. Many oil wells have been reported with annulus gas leaks (called internal leaks) on the Norwegian Continental Shelf (NCS) area. Interventions to repair the leaking wells or closing and abandoning wells have led to: high operating cost, low overall oil recovery, and in some cases unsafe operation. The reasons why leakages occur can be different, and finding the causes is a very complex task. For gas lift and gas injection wells the integrity of the well is often compromised. As the pressure of the hydrocarbon reserves decreases, particularly in mature fields, the need for boosting increases. Gas is injected into the well either to lift the oil in the production well or to maintain pressure in the reservoir from the injection well. The challenge is that this gas can lead to breakdown of the well integrity and cause leakages. However, as there are many types of leakages that can occur and due to their complexity it can be hard to find the cause or causal relationships. For this purpose, a new methodology, the Creek tool, which combines CBR and MBR is applied to investigate the reasons for the leakages. Creek is basically a CBR system, but it also includes MBR methods.</p><p>In addition to the well integrity cases, two complex cases (knowledge-rich cases) within oil well engineering have also been studied and analysed through the research work which is part of the PhD. The goal here is to show how the knowledge stored in two cases can be extracted for the CBR application.</p><p>A model comprising general knowledge (well-known rules and theories) and specific knowledge (stored in cases) has been developed. The results of the Wellogy model show that the CBR methodology can automate reasoning in addition to human reasoning through solving complex and repeated oil well problems. Moreover, the methodology showed that the valuable knowledge gained through the solved cases can be sustained and whenever it is needed, it can be retrieved and reused. The model has been verified for unsolved cases by evaluating case-matching results. The model gives elaborated explanations of the unsolved cases through the building of causal relationships. The model also facilitates knowledge acquisition and learning curves through its growing case base.</p><p>The study showed that building a CBR model is a rather time-consuming process due to four reasons:</p><p>1. Finding appropriate cases for the CBR application is not straightforward</p><p>2. Challenges related to constructing cases when transforming reported information to symbolic entities</p><p>3. Lack of defined criteria for amount of information (number of findings) for cases</p><p>4. Incomplete data and information to fully describe problems of the cases at the knowledge level</p><p>In this study only 12 solved cases (knowledge-rich cases) have been built in the Wellogy model. More cases (typically hundreds for knowledge-lean cases and around 50 for knowledge-rich cases) would be required to have a robust and efficient CBR model. As the CBR methodology is a new approach for solving complex oil well problems (research and development phase), additional research work is necessary for both areas, i.e. developing CBR frameworks (user interfaces) and building CBR models (core of CBR). Feasibility studies should be performed for implemented CBR models in order to use them in real oil field operations. So far, the existing Wellogy model has showed some benefits in terms of; representing the knowledge of leaking well cases in the form of an ontology, retrieving solved cases, and reusing pervious cases.</p>

The history of oil well engineering applications has revealed that the frequent operational problems are still common in oil well practice. Well blowouts, stuck pipes, well leakages are examples of the repeated problems in the oil well engineering industry. The main reason why these unwanted problems are unavoidable can be the complexity and uncertainties of the oil well processes. Unforeseen problems happen again and again, because they are not fully predictable, which could be due to lack of sufficient data or improper modelling to simulate the real conditions in the process. Traditional mathematical models have not been able to totally eliminate unwanted oil well problems because of the many involved simplifications, uncertainties, and incomplete information. This research work proposes a new approach and breakthrough for overcoming these challenges. The main objective of this study is merging two scientific fields; artificial intelligence and petroleum engineering in order to implement a new methodology. Case-Based Reasoning (CBR) and Model-Based Reasoning (MBR), two branches of the artificial intelligence science, are applied for solving complex oil well problems. There are many CBR and MBR modelling tools which are generally used for different applications for implementing and demonstrating CBR and MBR methodologies; however, in this study, the Creek system which combines CBR and MBR has been utilized as a framework. One specific challenging task related to oil well engineering has been selected to exemplify and examine the methodology. To select a correct candidate for this application was a challenging step by itself. After testing many different issues in the oil well engineering, a well integrity issue has been chosen for the context. Thus, 18 leaking wells, production and injection wells, from three different oil fields have been analysed in depth. Then, they have been encoded and stored as cases in an ontology model given the name Wellogy. The challenges related to well integrity issues are a growing concern. Many oil wells have been reported with annulus gas leaks (called internal leaks) on the Norwegian Continental Shelf (NCS) area. Interventions to repair the leaking wells or closing and abandoning wells have led to: high operating cost, low overall oil recovery, and in some cases unsafe operation. The reasons why leakages occur can be different, and finding the causes is a very complex task. For gas lift and gas injection wells the integrity of the well is often compromised. As the pressure of the hydrocarbon reserves decreases, particularly in mature fields, the need for boosting increases. Gas is injected into the well either to lift the oil in the production well or to maintain pressure in the reservoir from the injection well. The challenge is that this gas can lead to breakdown of the well integrity and cause leakages. However, as there are many types of leakages that can occur and due to their complexity it can be hard to find the cause or causal relationships. For this purpose, a new methodology, the Creek tool, which combines CBR and MBR is applied to investigate the reasons for the leakages. Creek is basically a CBR system, but it also includes MBR methods. In addition to the well integrity cases, two complex cases (knowledge-rich cases) within oil well engineering have also been studied and analysed through the research work which is part of the PhD. The goal here is to show how the knowledge stored in two cases can be extracted for the CBR application. A model comprising general knowledge (well-known rules and theories) and specific knowledge (stored in cases) has been developed. The results of the Wellogy model show that the CBR methodology can automate reasoning in addition to human reasoning through solving complex and repeated oil well problems. Moreover, the methodology showed that the valuable knowledge gained through the solved cases can be sustained and whenever it is needed, it can be retrieved and reused. The model has been verified for unsolved cases by evaluating case-matching results. The model gives elaborated explanations of the unsolved cases through the building of causal relationships. The model also facilitates knowledge acquisition and learning curves through its growing case base. The study showed that building a CBR model is a rather time-consuming process due to four reasons: 1. Finding appropriate cases for the CBR application is not straightforward 2. Challenges related to constructing cases when transforming reported information to symbolic entities 3. Lack of defined criteria for amount of information (number of findings) for cases 4. Incomplete data and information to fully describe problems of the cases at the knowledge level In this study only 12 solved cases (knowledge-rich cases) have been built in the Wellogy model. More cases (typically hundreds for knowledge-lean cases and around 50 for knowledge-rich cases) would be required to have a robust and efficient CBR model. As the CBR methodology is a new approach for solving complex oil well problems (research and development phase), additional research work is necessary for both areas, i.e. developing CBR frameworks (user interfaces) and building CBR models (core of CBR). Feasibility studies should be performed for implemented CBR models in order to use them in real oil field operations. So far, the existing Wellogy model has showed some benefits in terms of; representing the knowledge of leaking well cases in the form of an ontology, retrieving solved cases, and reusing pervious cases.