Production data are the most abundant data in the field. However, they can often be of poor quality because of undocumented operational problems, or changes in operating conditions, or even recording mistakes (Nobakht et al. 2009). If this poor quality or inconsistency is not recognized as such, it can be misinterpreted as a reservoir issue other than the data quality problem that it is. Thus quality control of production data is a crucial and necessary step that must precede any further interpretation using the production data. To restore production data, we propose to use the capacitance resistance model (CRM) to realize data reconciliation. CRM is a simple reservoir simulation model that characterizes the connectivity between injectors and producers using only production and injection rate data. Because the CRM model is based on the continuity equation, it can be used to analyze the production corresponding to the injection signal in the reservoir. The problematic production data are then put into the CRM model directly and the resulting CRM output parameters are used to evaluate what the correct production response would be under current injection scheme. We also make sensitivity analysis based on synthetic fields, which are heterogeneous ideal reservoir models with imposed geology and well features in Eclipse. The aim is to show how bad data could be misleading and the best way to restore the production data. Using the CRM model itself to control data quality is a novel method to obtain clean production data. We can then apply the new clean production data in reservoir simulators or any other processes where production data quality matters. This data quality control process can help better understand the reservoir, analyze its behavior in a more ensured way and make more reliable decisions. / text

As ferramentas visuais tem se mostrado eficazes no ensino de química. Essas ferramentas apresentam bases efetivas para a compreensão da química em seus três modos de representação (macroscópico, submicroscópico e simbólico). Apesar da importância da utilização destas ferramentas, muitos professores não estão preparados para sua utilização em sala de aula. Diante disso, surge a necessidade de formar professores com capacidades para criar metodologias que abordem as ferramentas visuais, pois estas podem facilitar o desenvolvimento da habilidade de interpretação de representações pelos estudantes. Esta pesquisa possui o objetivo de investigar se os bolsistas PIBID de química da USP, após atividades formativas, apresentaram em suas produções didáticas atividades que possuam indícios dos fatores que remetem ao desenvolvimento da habilidade representacional e se estas produções apresentam uma elevada exigência conceitual. A coleta de dados foi realizada por um acompanhamento da formação inicial a qual os bolsistas PIBID participaram. Após a formação os bolsistas acompanharam as aulas de química de uma escola da cidade de São Paulo. Durante este período os bolsistas planejaram aulas a serem aplicadas na escola. Ao final de todo o processo, os bolsistas PIBID redigiram um relatório contendo o que foi abordado, suas observações e avaliações das aulas. A análise do desenvolvimento da habilidade de interpretação de representações foi realizada através de uma metodologia qualitativa, na qual foram utilizados trechos dos relatórios que se enquadrassem ao modelo CRM, adaptado de Schonborn e Anderson (2009). Como foi observado que os bolsistas PIBID utilizaram diversos dos fatores do modelo CRM em suas produções didáticas, foi realizada uma análise para averiguar se estas produções também continham elevada exigência conceitual. O cálculo do nível de exigência conceitual foi realizado abordando uma metodologia mista, que envolve o método quantitativo e qualitativo. O cálculo mostrou que os relatórios apresentaram satisfatório nível de exigência conceitual. Com a análise foi possível concluir que esta pesquisa pode contribuir para a reflexão sobre a importância da formação de professores para o ensino de química/ciências, e ainda traz um modelo que pode servir como embasamento para que professores planejem suas aulas do modo a abordar a habilidade de interpretação de representações, que é fundamental para compreensão da química pelos estudantes. / Visual tools have been proven be very important in teaching chemistry. These tools are effective to support the understanding of chemistry in the three modes of representation (macroscopic, submicroscopic and symbolic). Despite the importance of these tools, many teachers are not prepared to use them in classrooms. This arises the need of training teachers with skills to create methodologies using visual tools, since these methodologies can facilitate the students to develop abilities to interpret representations. This research aims to investigate if after training activities, the USP chemistry students of PIBID present in their didactic productions activities evidencing the development of representational ability and a high conceptual demand. The data collection was conducted by a follow up of the initial training with scholarship students of PIBID. After the training the students participated of chemistry classes at a school in the city of São Paulo. Those classes were sometimes planned by the PIBID students. At the end of the process the PIBID students wrote a report containing what was covered, their observations and evaluations. The analysis of the development of the interpreting representations ability was realized using a qualitative methodology, in which was used parts of the report that fits the CRM model, adapted from Schonborn and Anderson (2009). Since it was observed that PIBID students used several of the CRM model factors in their didactic productions, an analysis was realized to determine whether these productions also contained evidence of high conceptual demand. The calculation of the conceptual requirement level was done by a mixed methodology, involving the quantitative and qualitative method. The calculation showed that the reports had a satisfactory conceptual requirement level. We concluded that the CRM model can be used as foundation for teachers to plan their lessons in order to develop the ability to interpretation of external representations which is fundamental to understanding the chemistry by students. We also concluded that despite the use of the CRM model factors, it is necessary that the activities elaborated by the teachers had a high conceptual demand level.

As ferramentas visuais tem se mostrado eficazes no ensino de química. Essas ferramentas apresentam bases efetivas para a compreensão da química em seus três modos de representação (macroscópico, submicroscópico e simbólico). Apesar da importância da utilização destas ferramentas, muitos professores não estão preparados para sua utilização em sala de aula. Diante disso, surge a necessidade de formar professores com capacidades para criar metodologias que abordem as ferramentas visuais, pois estas podem facilitar o desenvolvimento da habilidade de interpretação de representações pelos estudantes. Esta pesquisa possui o objetivo de investigar se os bolsistas PIBID de química da USP, após atividades formativas, apresentaram em suas produções didáticas atividades que possuam indícios dos fatores que remetem ao desenvolvimento da habilidade representacional e se estas produções apresentam uma elevada exigência conceitual. A coleta de dados foi realizada por um acompanhamento da formação inicial a qual os bolsistas PIBID participaram. Após a formação os bolsistas acompanharam as aulas de química de uma escola da cidade de São Paulo. Durante este período os bolsistas planejaram aulas a serem aplicadas na escola. Ao final de todo o processo, os bolsistas PIBID redigiram um relatório contendo o que foi abordado, suas observações e avaliações das aulas. A análise do desenvolvimento da habilidade de interpretação de representações foi realizada através de uma metodologia qualitativa, na qual foram utilizados trechos dos relatórios que se enquadrassem ao modelo CRM, adaptado de Schonborn e Anderson (2009). Como foi observado que os bolsistas PIBID utilizaram diversos dos fatores do modelo CRM em suas produções didáticas, foi realizada uma análise para averiguar se estas produções também continham elevada exigência conceitual. O cálculo do nível de exigência conceitual foi realizado abordando uma metodologia mista, que envolve o método quantitativo e qualitativo. O cálculo mostrou que os relatórios apresentaram satisfatório nível de exigência conceitual. Com a análise foi possível concluir que esta pesquisa pode contribuir para a reflexão sobre a importância da formação de professores para o ensino de química/ciências, e ainda traz um modelo que pode servir como embasamento para que professores planejem suas aulas do modo a abordar a habilidade de interpretação de representações, que é fundamental para compreensão da química pelos estudantes. / Visual tools have been proven be very important in teaching chemistry. These tools are effective to support the understanding of chemistry in the three modes of representation (macroscopic, submicroscopic and symbolic). Despite the importance of these tools, many teachers are not prepared to use them in classrooms. This arises the need of training teachers with skills to create methodologies using visual tools, since these methodologies can facilitate the students to develop abilities to interpret representations. This research aims to investigate if after training activities, the USP chemistry students of PIBID present in their didactic productions activities evidencing the development of representational ability and a high conceptual demand. The data collection was conducted by a follow up of the initial training with scholarship students of PIBID. After the training the students participated of chemistry classes at a school in the city of São Paulo. Those classes were sometimes planned by the PIBID students. At the end of the process the PIBID students wrote a report containing what was covered, their observations and evaluations. The analysis of the development of the interpreting representations ability was realized using a qualitative methodology, in which was used parts of the report that fits the CRM model, adapted from Schonborn and Anderson (2009). Since it was observed that PIBID students used several of the CRM model factors in their didactic productions, an analysis was realized to determine whether these productions also contained evidence of high conceptual demand. The calculation of the conceptual requirement level was done by a mixed methodology, involving the quantitative and qualitative method. The calculation showed that the reports had a satisfactory conceptual requirement level. We concluded that the CRM model can be used as foundation for teachers to plan their lessons in order to develop the ability to interpretation of external representations which is fundamental to understanding the chemistry by students. We also concluded that despite the use of the CRM model factors, it is necessary that the activities elaborated by the teachers had a high conceptual demand level.

Abstracts in English, Zulu and Southern Sotho / The advent of Basel II Capital Accord has revolutionised credit risk measurement (CRM) to the extent that the once “perceived riskier bank assets” are now accommodated for lending. The Small and Medium Enterprise (SME) sector has been traditionally perceived as a riskier and unprofitable asset for lending activity by Commercial Banks, in particular. But empirical studies on the implementation of the Basel II internal-ratings-based (IRB) framework have demonstrated that SME credit risk is measurable. Banks are still finding it difficult to forecast SME loan default and to provide credit to the sector that meet Basel’s capital requirements. The thesis proposes to construct an empirical credit risk measurement (CRM) model, specifically for SMEs, to ameliorate the adverse effects of SME credit inaccessibility due to high information asymmetry between financial institutions (FI) and SMEs in Zimbabwe. A well-performing and accurate CRM helps FIs to control their risk exposure through selective granting of credit based on a thorough statistical analysis of historical customer data. This thesis develops a CRM model, built on a statistically random sample, known-good-bad (KGB) sample, which is a better representation of the through-the-door (TTD) population of SME loan applicants. The KGB sample incorporates both accepted and rejected applications, through reject inference (RI). A model-based bound and collapse (BC) reject inference methodology was empirically used to correct selectivity bias inherent in CRM domain. The results have shown great improvement in the classification power and aggregate supply of credit supply to the SME portfolio of the case-studied bank, as evidenced by substantial decrease of bad rates across models developed; from the preliminary model to final model designed for the case-studied bank. The final model was validated using both bad rate, confusion matrix metrics and Area under Receiver Operating Characteristic (AUROC) curve to assess the classification power of the model within-sample and out-of-sample. The AUROC for the final model (weak model) was found to be 0.9782 whilst bad rate was found to be 14.69%. There was 28.76% improvement in the bad rate in the final model in comparison with the current CRM model being used by the case-studied bank. / Isivumelwano seBasel II Capital Accord sesishintshe indlela yokulinganisa ubungozi bokunikezana ngesikweletu credit risk measurement (CRM) kwaze kwafika ezingeni lapho izimpahla ezazithathwa njengamagugu anobungozi “riskier bank assets” sezimukelwa njengesibambiso sokuboleka imali. Umkhakha wezamaBhizinisi Amancane naSafufusayo, phecelezi, Small and Medium Enterprise (SME) kudala uqondakala njengomkhakha onobungozi obukhulu futhi njengomkhakha ongangenisi inzuzo, ikakhulu njengesibambiso sokubolekwa imali ngamabhange ahwebayo. Kodwa izifundo zocwaningo ezimayelana nokusetshenziswa nokusetshenziswa kwesakhiwo iBasel II internal-ratings-based (IRB) sezikhombisile ukuthi ubungozi bokunikeza isikweletu kumabhizinisi amancane nasafufusayo (SME) sebuyalinganiseka. Yize kunjalo, amabhange asathola ukuthi kusenzima ukubona ngaphambili inkinga yokungabhadeleki kahle kwezikweletu kanye nokunikeza isikweletu imikhakha enemigomo edingekayo yezimali kaBasel. Lolu cwaningo beluphakamisa ukwakha uhlelo imodeli ephathekayo yokulinganisa izinga lobungozi bokubolekisa ngemali (CRM) kwihlelo lokuxhasa ngezimali ama-SME, okuyihlelo elilawulwa yiziko lezimali ezweni laseZimbabwe. Imodeli ye-CRM esebenza kahle futhi eshaya khona inceda amaziko ezimali ukugwema ubungozi bokunikezana ngezikweletu ngokusebenzisa uhlelo lokunikeza isikweletu ababoleki abakhethekile, lokhu kususelwa ohlelweni oluhlaziya amanani edatha engumlando wekhasimende. Imodeli ye-CRM ephakanyisiwe yaqala yakhiwa ngohlelo lwamanani, phecelezi istatistically random sample, okuluphawu olungcono olumele uhlelo lwe through-the-door (TTD) population lokukhetha abafakizicelo zokubolekwa imali bama SME, kanti lokhu kuxuba zona zombili izicelo eziphumelele kanye nezingaphumelelanga. Indlela yokukhetha abafakizicelo, phecelezi model-based bound-and-collapse (BC) reject-inference methodology isetshenzisiwe ukulungisa indlela yokukhetha ngokukhetha ngendlela yokucwasa kwisizinda seCRM. Imiphumela iye yakhombisa intuthuko enkulu mayelana namandla okwehlukanisa kanye nokunikezwa kwezikweletu kuma SME okungamamabhange enziwe ucwaningo lotho., njengoba lokhu kufakazelwa ukuncipha okukhulu kwe-bad rate kuwo wonke amamodeli athuthukisiwe. Imodeli yokuqala kanye neyokugcina zazidizayinelwe ibhange. Imodeli yokugcina yaqinisekiswa ngokusebenzisa zombili indlela isikweletu esingagculisi kanye negrafu ye-Area under Receiver Operating Characteristic (AUROC) ukulinganisa ukwehlukaniswa kwamandla emodeli engaphakathi kwesampuli nangaphandle kwesampuli. Uhlelo lwe-AUROC lwemodeli yokugcina (weak model) lwatholakala ukuthi luyi 0.9782, kanti ibad rate yatholakala ukuthi yenza i-14.69%. Kwaba khona ukuthuthuka nge-28.76% kwi-bad rate kwimodeli yokugcina uma iqhathaniswa nemodeli yamanje iCRM model ukuba isetshenziswe yibhange elithile. / Basel II Capital Accord e fetotse tekanyo ya kotsi ya mokitlane (credit risk measurement (CRM)) hoo “thepa e kotsi ya dibanka” ka moo e neng e bonwa ka teng, e seng e fuwa sebaka dikadimong. Lekala la Dikgwebo tse Nyane le tse Mahareng (SME) le bonwa ka tlwaelo jwalo ka lekala le kotsi e hodimo le senang ditswala bakeng sa ditshebetso tsa dikadimo haholo ke dibanka tsa kgwebo. Empa dipatlisiso tse thehilweng hodima se bonweng kapa se etsahetseng tsa tshebetso ya moralo wa Basel II internal-ratings-based (IRB) di supile hore kotsi ya mokitlane ya SME e kgona ho lekanngwa. Leha ho le jwalo, dibanka di ntse di thatafallwa ke ho bonelapele palo ya ditlholeho tsa ho lefa tsa diSME le ho fana ka mokitla lekaleng leo le kgotsofatsang ditlhoko tsa Basel tsa ditjhelete. Phuputso ena e ne sisinya ho etsa tekanyo ya se bonwang ho mmotlolo wa kotsi ya mokitlane (CRM) tshebetsong ya phano ya tjhelete ya diSME e etswang ke setsi sa ditjhelete (FI) ho la Zimbabwe. Mmotlolo o sebetsang hantle hape o fanang ka dipalo tse nepahetseng o dusa diFI hore di laole pepeso ya tsona ho kotsi ka phano e kgethang ya mokitlane, e thehilweng hodima manollo ya dipalopalo ya dintlha tsa histori ya bareki. Mmotlolo o sisingwang wa CRM o hlahisitswe ho tswa ho sampole e sa hlophiswang, e leng pontsho e betere ya setjhaba se ikenelang le monyako (TTD) ya batho bao e kang bakadimi ba tjhelete ho diSME, hobane e kenyelletsa bakopi ba amohetsweng le ba hannweng. Mokgwatshebetso wa bound-and-collapse (BC) reject-inference o kentswe tshebetsong ho nepahatsa tshekamelo ya kgetho e leng teng ho lekala la CRM. Diphetho tsena di bontshitse ntlafalo e kgolo ho matla a tlhophiso le palohare ya phano ya mokitlane ho diSME tsa banka eo ho ithutilweng ka yona, jwalo ka ha ho pakilwe ke ho phokotseho ya direite tse mpe ho pharalla le dimmotlolo tse hlahisitsweng. Mmotlolo wa ho qala le wa ho qetela e ile ya ralwa bakeng sa banka. Mmotlolo wa ho qetela o ile wa netefatswa ka tshebediso ya bobedi reite e mpe le mothinya wa Area under Receiver Operating Characteristic (AUROC) ho lekanya matla a kenyo mekgahlelong a mmotlolo kahare ho sampole le kantle ho yona. AUROC bakeng sa mmotlo wa ho qetela (mmotlolo o fokotseng) e fumanwe e le 0.9782, ha reite e mpe e fumanwe e le 14.69%. Ho bile le ntlafalo ya 28.76% ho reite e mpe bakeng sa mmotlolo wa ho qetela ha ho bapiswa le mmotlolo wa CRM ha o sebediswa bankeng yona eo. / Graduate School of Business Leadership / D.B.L.