39 research outputs found
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Improved integration of information to reduce subsurface model bias
Subsurface modeling deals with data-related issues like cognitive and sampling biases, and model-related challenges including statistical assumptions, misspecification, and algorithmic biases. These challenges introduce four critical implications during subsurface modeling. Firstly, subsurface sampling is subject to sampling bias, which compromises statistical representativeness. Secondly, analog selection methodologies rely on multivariate statistics and expert judgment that overlook spatial information and data dimensionality. Thirdly, subsurface inferential workflows that utilize dimensionality reduction seldom provide repeatable frameworks that maintain model stability and are invariant to Euclidean transformations. Lastly, deep learning methods for dimensionality reduction, characterized as black-box models, lack interpretability and robust evaluation metrics, increasing susceptibility to algorithmic bias. Consequently, neglecting these challenges in subsurface modeling could lead to erroneous predictions, inconsistent inferences, diminished model reliability, and suboptimal decision-making that impacts project economics.
This dissertation integrates information within subsurface models to reduce model bias and significantly improve their accuracy, robustness, and generalizability. First, I create spatial declustering methods to debias spatial datasets with single and multiscale preferential sampling in stationary populations. Second, I introduce a novel geostatistics-based machine learning method for identifying subsurface resource analogs that integrate spatial information in subsurface datasets with high dimensionality. Next, I efficiently combine machine learning and computational geometry methods to stabilize lower dimensional spaces for uncertainty quantification and interpretation. Finally, I create a methodology to assess, evaluate, and interpret the stability of deep learning latent feature spaces.
These novel methodologies demonstrate the importance of improved techniques for information integration in subsurface modeling and show better results over naĂŻve methods. This results in objective sampling debiasing in spatial stationary populations with single or multiple data scales, improving statistical representativity. Also, the results show better generalization and accurate identification of spatial analogs in high-dimensional datasets. Moreover, the methods yield Euclidean transformation-invariant lower-dimensional spaces, ensuring unique and repeatable solutions that improve model reliability and interpretability, for rational comparisons. Finally, the results indicate that deep learning models for dimensionality reduction exhibit algorithmic biases and instabilities, including sample, structural, and inferential instability, affecting their reliability and interpretability. Together, these innovations ultimately reduce model bias and significantly improve subsurface modeling.Petroleum and Geosystems Engineerin
Rigid Transformations for Stabilized Lower Dimensional Space to Support Subsurface Uncertainty Quantification and Interpretation
Subsurface datasets inherently possess big data characteristics such as vast
volume, diverse features, and high sampling speeds, further compounded by the
curse of dimensionality from various physical, engineering, and geological
inputs. Among the existing dimensionality reduction (DR) methods, nonlinear
dimensionality reduction (NDR) methods, especially Metric-multidimensional
scaling (MDS), are preferred for subsurface datasets due to their inherent
complexity. While MDS retains intrinsic data structure and quantifies
uncertainty, its limitations include unstabilized unique solutions invariant to
Euclidean transformations and an absence of out-of-sample points (OOSP)
extension. To enhance subsurface inferential and machine learning workflows,
datasets must be transformed into stable, reduced-dimension representations
that accommodate OOSP.
Our solution employs rigid transformations for a stabilized Euclidean
invariant representation for LDS. By computing an MDS input dissimilarity
matrix, and applying rigid transformations on multiple realizations, we ensure
transformation invariance and integrate OOSP. This process leverages a convex
hull algorithm and incorporates loss function and normalized stress for
distortion quantification. We validate our approach with synthetic data,
varying distance metrics, and real-world wells from the Duvernay Formation.
Results confirm our method's efficacy in achieving consistent LDS
representations. Furthermore, our proposed "stress ratio" (SR) metric provides
insight into uncertainty, beneficial for model adjustments and inferential
analysis. Consequently, our workflow promises enhanced repeatability and
comparability in NDR for subsurface energy resource engineering and associated
big data workflows.Comment: 30 pages, 17 figures, Submitted to Computational Geosciences Journa
UNCERTAINTY ANALYSIS OF PRODUCTION FORECAST IN SHALE SYSTEMS
This research evaluates the impact of decision making and uncertainty associated with production forecast in shale oil and gas wells; over 11000 wells completed in the Barnett & Haynesville plays and more than 2000 wells from the Permian Basin. Existing studies show that unconventional reservoirs have complex reservoir characteristics making traditional methods for ultimate recovery estimation insufficient. Based on these limitations, uncertainty is increased during the estimation of reservoir properties, reserve quantification and, evaluation of economic viability. Thus, it is necessary to determine and recommend favorable conditions in which these reservoirs are developed.
In this study, cumulative production is predicted using four different decline curve analysis (DCA) - power law exponential, stretched exponential, extended exponential and Duong models. A comparison between the predicted cumulative production from the models using a subset of historical data (0-3months) and actual production data observed over the same time period determines the accuracy of DCA’s; repeating the evaluation for subsequent time intervals (0-6 months, 0-9 months,..) provides a basis to monitor the performance of each DCA with time. Moreover, the best predictive models as a combination of DCA’s predictions is determined via multivariate regression. Afterwards, uncertainty due to prediction errors excluding any bias is estimated and expected disappointment (ED) is calculated using probability density function on the results obtained.
Using these results, uncertainty is estimated from the plot of ED versus time for all wells considered. ED drops for wells having a longer production history as more data are used for estimation. Also, the surprise/disappointment an operator experiences when using various DCA methods is estimated for each scenario. However, it appears that power law exponential serves as the lower boundary of the forecast in the formations considered, whilst the upper boundary switches between stretched exponential (SE) and Duong (DNG) method. The extend exponential DCA model was found to demonstrate an erratic behavior crossing over actual trends multiple times with time.
In conclusion, profitability zones for producing oil in the Permian basin are defined implicitly based on drilling and completion practices which paves the path to determine the “sweet spot” via optimization of fracture spacing and horizontal length in the wells. Also, it can be inferred that the decline rate during production is somewhat related to pore connectivity and it could be a good qualitative indicator of wells in which EOR might be successful although it needs to be investigated further.
The outcome of this research work helps improve the industry’s take on uncertainty analysis in production forecast, especially the concept of expected disappointment/pleasant surprise. This study suggests that effects of bias and ED due to decision making can be much greater than what has often regarded; ranging from 0.41 to 0.86, which can change the performance evaluation of shales in terms of economic feasibility
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A Machine Learning Approach: Socio-economic Analysis to Support and Identify Resilient Analog Communities in Texas
Identification of analog resources or items are important during the
planning and development of new communities because available
information is usually limited or absent. Conventionally, analogs are made
by domain experts however, this is not always readily obtainable.
Coupled with this challenge, most of the available data in socioeconomic
systems have high dimensionality making interpretation, and visualization
of these datasets difficult. Hence, it is crucial to adopt a workflow that
can be used to identify analogs regardless of its existing high
dimensionality.
To this end, we present a systematic and unbiased measure, group
similarity score (GCS) and similarity scoring metric (SSM) to support the
predictive search of missing properties for target communities and
identification of analogous cities based on available socioeconomic data
and modeling. Knowing that each Texan community can be
characterized by its associated properties, the workflow combines both
spatial and multivariate statistics in a novel manner to determine the GCS
& SSM whilst visualizing the associated uncertainty space.
The workflow consists of three major steps: 1) key parameter selection via
feature engineering, 2) multivariate and spatial analysis using
multidimensional scaling (MDS) and density-based spatial clustering of
applications with noise (DBSCAN) for clustering analysis, 3) similarity
ranking using a modified Mahalanobis distance function as a clustering
basis on preprocessed data. Afterwards, to assess the quality of the
predicted feature and analog communities obtained, K-nearest neighbor
algorithm is applied, then the analog cities are found.
The workflow is demonstrated using on high dimensional socio-economic
data. We find analogs for each community cluster identified with their
GCS and SSM in relation to 4 randomly selected communities used for
testing. Thus, it is recommended to apply the integration of this workflow in
uncertainty exploration, trend-mappings, and community analog
assignment, and benchmarking to support decision making.IC2 InstitutePetroleum and Geosystems Engineerin
Knowledge of malaria amongst caregivers of young children in rural and urban communities in Southwest Nigeria
Purpose: To compare the awareness and treatment knowledge of malaria amongst caregivers of young children in urban and rural areas of Ado-Odo/Ota Local Government Area in Ogun State.
Method: Structured questionnaires were administered to caregivers of children under the age of five years in 1472 households using a multistage random sampling technique.
Results: Many respondents (65%) attributed the cause of malaria to mosquito bite. The knowledge of malaria treatment (particularly the knowledge of pediatric doses) was generally poor. Caregivers in urban areas had better understanding of the dosage regimen for both adult and pediatric doses (
ASSESSMENT OF APPLICATION OF UNIVERSAL DESIGN PRINCIPLES IN EDUCATIONAL FACILITIES FOR CHALLENGED CHILDREN IN LAGOS STATE, NIGERIA
Lagos state Nigeria is one of the metropolitan and educationally advanced cities in Nigeria. Over the years the number of children with disabilities has been on the increase. Many of these challenged children are either denied the benefit of education or are constrained to attend schools which have not been designed to take care of their disabilities. Often times, such children are frustrated and their educational abilities are also negatively impacted. This has given use to schools which are now dedicated to challenged children. The aim of this study is to evaluate the design of these educational facilities in order to determine their application of universal design principles. The study is based on case study of schools for the challenged in Lagos state. Results indicate that many of the facilities did not conform to the universal design principles and consequently have affected learning of the children. The recommendation are made to enhance learning of challenged children through proper adoption of universal principals in planning of educational facilities. This is a novel study in Nigeria and its outcome will influence policy direction in the planning of educational facilities for challenged children in Nigeria and other developing countrie
Socioeconomic and behavioral factors leading to acquired bacterial resistance to antibiotics in developing countries.
In developing countries, acquired bacterial resistance to antimicrobial agents is common in isolates from healthy persons and from persons with community-acquired infections. Complex socioeconomic and behavioral factors associated with antibiotic resistance, particularly regarding diarrheal and respiratory pathogens, in developing tropical countries, include misuse of antibiotics by health professionals, unskilled practitioners, and laypersons; poor drug quality; unhygienic conditions accounting for spread of resistant bacteria; and inadequate surveillance
Impact of Exchange Rate Fluctuation on International Trade: A Study of Selected Companies in Nigeria
This study examined the impact of exchange rate fluctuation on international trade, a study of selected companies in Nigeria. International trade is recognized as the study of the causes and repercussions of global trade in goods and services and the international mobility of factors of production. Due to exchange fluctuation, exchange appreciation or depreciation might occur. Constant fluctuations in exchange rate can also have a detrimental influence on the market, making both internal and international trade volatile and harmful. The study evaluated the impact of exchange rate fluctuations on balance of trade, import volume and export volume in selected companies in Nigeria. The study was carried out to provide answers to the following research questions: What is the impact of exchange rate fluctuations on the balance of trade of selected companies in Nigeria? What is the impact of the exchange rate fluctuations on import volume of selected companies in Nigeria? What is the impact of exchange rate fluctuations on export volume of selected companies in Nigeria? To provide answers to the research questions, the study collected primary data through the distribution of questionnaires to the employees of Unilever Nigeria Plc, John Holt Plc and PZ Cussons Nigeria Plc, which were the selected companies used in the study. The study adopted the positivism research philosophy, quantitative research method and data collected were presented in tables and chart, while the hypotheses were analyzed with the aid of Statistical Package for Social Science (SPSS). The findings of the study based on the research questions are as follows: exchange rate fluctuations have an impact on balance of trade of quoted conglomerate companies in Nigeria with a p-value of 0.000 which is less than 0.05; exchange rate fluctuations have a significant impact on export volume of quoted conglomerate companies in Nigeria, since it has a p-value of 0,003 which is less than 0.05; and exchange rate fluctuations have no significant impact on import volume of quoted conglomerate companies in Nigeria, since the p-value of 0.065 is greater than 0.05.Based on the findings of the study, the study concludes that exchange rate fluctuations have an impact on the international trade of selected companies in Nigeria
Cost Evaluation of Commonly Prescribed Antihypertensive Drugs and the Pattern of Prescription among Doctors in the Lagos University Teaching Hospital
Pharmacological anti-hypertensive prescriptions of 600 randomly selected mild to moderate hypertensive patients were reviewed for 3 years in four Clinics of the Lagos University Teaching Hospital namely Cardiology, Endocrine, Nephrology and Neurology Clinics. Calcium channel blockers were the most frequently prescribed drugs (24.8%) followed by Angiotensin converting enzyme inhibitors (12.5%). Others included combined Amiloride/Hydrochlorothiazide (Co-amilozide) (10.6%), Alpha methyl dopa (10%), Beta Blockers (8.5%), combination of Co-amilozide and Alpha methyl dopa (6.8%), Co-amilozide,Calcium channel/Beta blockers (6.0%) etc. Brand name prescription was predominantly high in all the four Clinics (Cardiology 87.4%, Endocrine - 86.8%, Nephrology 74.6% and Neurology 87.9%) as compared to low generic prescription. The overall Brand name prescription was (83.4%) compared with Generic of (16%). The monthly cost difference between Generic anti-hypertensive single drug treatment and Brand named single drug treatment was between N500.00 to N600.00. It was concluded that the prescribing of the new generation drugs i.e. Calcium channel blockers, ACE inhibitors with supposedly little or no metabolic side effects is a new trend which should be scientifically evaluated vis--vis cost effectiveness and adverse drug reaction. It was also concluded that generic prescription should be encouraged among prescribers to lessen the financial burden of patients because drugs marketed under generic names are usually cheaper than those with brand names.
Key words: Brand, Generic,Prescription, Antihypertensives,Cost.
[Nig. Jnl Health & Biomedical Sciences Vol.1(2) 2002: 68-70
The Impact of Education Intervention on the Blood Pressure Control of the Elderly Nigerian Hypertensives
Hypertension plays a major role in morbidity and mortality in Nigeria. Evidence from developed countries showed that intensive patient education has resulted in improved compliance to therapy reducing morbidity and mortality. There was a need to explore the impact of health educational intervention on the blood pressure control among elderly hypertensives. Ninety-five hypertensive patients aged sixty and above attending the Hypertension Clinic of the Lagos University Teaching Hospital were recruited. An initial level of their awareness of hypertension, its risk factors and management were assessed through a structured research questionnaire. A score of 60% was deemed adequate. Over a two year period, health talk on the need to comply with life style modification and drug therapy followed by a question and answer session was given on each clinic day. Adequate blood pressure control was define