The Formal Modeling Of Engineering Design Information By Means Of An Axiomatic System

<div>There is mounting evidence in the current literature which suggests that our collective understanding of engineering design is insufficient to support the continued growth of the engineering endeavor. Design theory is the emergent research field that addresses this problem by seeking to improve our understanding of, and thus our ability to, design. The goal of this author's work is to demonstrate that formal techniques of logic can improve our understanding of design. Specifically, a formal system called the Hybrid Model (HM) is presented; this system is a set-theoretic description of engineering design information that is valid independent of (a) the processes that generate or manipulate the information and (b) the role of the human designer. Because of this, HM is universally applicable to the representation of design-specific information throughout all aspects of the engineering enterprise. The fundamental unit in HM is a design entity, which is defined as a unit of information relevant to a design task. The axioms of HM define the structure of design entities and the explicit means by which they may be rationally organized. HM provides (a) a basis for building taxonomies of design entities, (b) a generalized approach for making statements about design entities independent of how the entities are generated or used, and (c) a formal syntactic notation for the standardization of design entity specification. Furthermore, HM is used as the foundation of DESIGNER, an extension to the Scheme programming language, providing a prototype-based object-oriented system for the static modeling of design information. Objects in the DESIGNER language satisfy the axioms of HM while providing convenient programming mechanisms to increase usability and efficiency. Several design-specific examples demonstrate the applicability of DESIGNER, and thus of HM as well, to the accurate representation of design information. </div>

Modeling and analysis of an electro-magneto-elastic rotating cylindrical tube actuator

This paper presents the static modeling and analysis of a novel cylindrical tube actuator subjected to a rotation about longitudinal axis with an internally applied air pressure under an electromagnetic field. The current tube actuator belongs to a smart actuator category and is made of an electro-magneto-active polymer filled with a particular volume fraction of suitable fillers. A continuum mechanics-based electro-magneto-mechanical model is developed to predict the response of the actuator for a combined pressure and electromagnetic field loading. To validate the same, the model is compared with the outputs of an existing spring roll actuator. Parametric studies are subsequently performed for varying input pressure, electric field, magnetic field, fillers content, and actuator’s rotational speed. The output sensitivity in terms of strain intensity at inner and outer surfaces of the actuator is also checked at different controlling inputs. In addition, various electro-magneto-mechanical instability curves are drawn to examine the critical inflation of the tube actuator. In general, the developed model provides initial steps toward the modern actuator designs for applications where a precise control with high load-carrying capability of the actuator plays a significant role.

The Formal Modeling Of Engineering Design Information By Means Of An Axiomatic System

<div>There is mounting evidence in the current literature which suggests that our collective understanding of engineering design is insufficient to support the continued growth of the engineering endeavor. Design theory is the emergent research field that addresses this problem by seeking to improve our understanding of, and thus our ability to, design. The goal of this author's work is to demonstrate that formal techniques of logic can improve our understanding of design. Specifically, a formal system called the Hybrid Model (HM) is presented; this system is a set-theoretic description of engineering design information that is valid independent of (a) the processes that generate or manipulate the information and (b) the role of the human designer. Because of this, HM is universally applicable to the representation of design-specific information throughout all aspects of the engineering enterprise. The fundamental unit in HM is a design entity, which is defined as a unit of information relevant to a design task. The axioms of HM define the structure of design entities and the explicit means by which they may be rationally organized. HM provides (a) a basis for building taxonomies of design entities, (b) a generalized approach for making statements about design entities independent of how the entities are generated or used, and (c) a formal syntactic notation for the standardization of design entity specification. Furthermore, HM is used as the foundation of DESIGNER, an extension to the Scheme programming language, providing a prototype-based object-oriented system for the static modeling of design information. Objects in the DESIGNER language satisfy the axioms of HM while providing convenient programming mechanisms to increase usability and efficiency. Several design-specific examples demonstrate the applicability of DESIGNER, and thus of HM as well, to the accurate representation of design information. </div>

The Integration of Analytical P/Z Material Balance Method with Static Modeling and Integrated Asset Model IAM to Generate Reliable Forecasting for a Giant Onshore Gas Field in Abu Dhabi

FGIIP (Field Gas Initially in Place) is one of the most essential elements in building dependable static and Integrated Asset Model (IAM). A good estimation of FGIIP will improve history matching and generate reliable forecast. The mature gas field producing under depletion mode is an ideal example where P/Z technique can fit well to re-estimate the FGIIP. Even more, the estimation is also important to narrow down FGIIP uncertainties that initially existed in static model. Reliable FGIIP estimation is achieved by performing multiple P/Z calculations. The process involves dividing reservoir into key areas and each area is represented by individual P/Z prior to summing-up all P/Z to get the total FGIIP. Several scenarios are developed by defining key areas based on permeability variation, areal distribution and PVT behavior. The best FGIIP estimation is then fed back into the static model to generate numerous realizations considering the static uncertainties to produce the same FGIIP. Static models with realistic distribution of properties and good history match are used in the IAM model to generate forecast. The giant onshore gas field is highly heterogeneous having permeability, lateral composition variation and dynamic interaction between wells. To ensure that the heterogeneity observed in the field is honored, multiple key areas are defined by making areal sectorization and lateral PVT variation when estimating FGIIP with P/Z approach. Communication between areas was evidenced from the sectoral P/Z. The field history matching was improved after applying the new estimated FGIIP. It was observed that the sectoral history matching both for production and pressure matches from some selected realizations built in static model resulted in better matches. Succinctly the re-evaluation of static derived FGIIP with P/Z method for the mature gas field was able to reduce the uncertainty range that initially existed. Incorporating the correct estimation of FGIIP in IAM has helped to yield reliable forecast and has enabled the asset to plan proper work programs for further field development. Analytical material balance with P/Z is very applicable for mature gas reservoirs producing under depletion mode. The approach is worth doing to narrow down the uncertainty range that was previously calculated. Moreover, the integration of analytical P/Z with static and dynamic model (IAM) has achieved more reliable forecasting of the mature gas field to proceed with further development plan.

3D Integrated Structural, Facies and Petrophysical Static Modeling Approach for Complex Sandstone Reservoirs: A Case Study from the Coniacian–Santonian Matulla Formation, July Oilfield, Gulf of Suez, Egypt

The Coniacian–Santonian Matulla Formation is one of the important reservoirs in the July oilfield, Gulf of Suez Basin. However, this formation is characterized by uncertainty due to the complexity of reservoir architecture, various lithologies, lateral facies variations and heterogeneous reservoir quality. These reservoir challenges, in turn, affect the effectiveness of further exploitation of this reservoir along the Gulf of Suez Basin. In this work, we conduct an integrated study using multidisciplinary datasets and techniques to determine the precise structural, petrophysical, and facies characteristics of the Matulla Formation and predict their complex geometry in 3D space. To complete this study, 30 2D seismic sections, five digital well logs, and core samples of 75 ft (ft = 0.3048 m) length were used to build 3D models for the Matulla reservoir. The 3D structural model shows strong lateral variation in thickness of the Matulla Formation with NW–SE, NE–SW and N–S fault directions. According to the 3D facies model, shale beds dominate the Matulla Formation, followed by sandstone, carbonate, and siltstone beds. The petrophysical model demonstrates the Matulla reservoir's ability to store and produce oil; its upper and lower zones have good quality reservoir, whereas its middle zone is a poor quality reservoir. The most promising areas for hydrocarbon accumulation and production via the Matulla reservoir are located in the central, southeast, and southwest sectors of the oilfield. In this approach, we combined multiple datasets and used the most likely parameters calibrated by core measurements to improve the reservoir modeling of the complex Matulla reservoir. In addition, we reduced many of the common uncertainties associated with the static modeling process, which can be applied elsewhere to gain better understanding of a complex reservoir.

Dynamic to Static Modeling

Dynamic-to-static modulus conversion has long been recognized as a complicated and challenging task in reservoir characterization and seismic geomechanics, and many single- and two-variable regression equations have been proposed. In practice however, the form and constants of the regression equation are variable from case to case. I introduce a methodology for estimating the static moduli called dynamic-to-static modeling (DTS). The methodology was validated by laboratory tests (ultrasonic and triaxial compression tests) to obtain dynamic and quasi-static bulk and Young’s (elasticity) moduli. Next, rock deformation phenomena were simulated considering different parameters affecting the process. The dynamic behavior was further modeled using rock physics methods. Unlike the conventional dynamic-to-static conversion procedures, the method considers a wide range of factors affecting the relationship between the dynamic and static moduli, including strain amplitude, dispersion, rock failure mechanism, pore shape, crack parameters, poromechanics, and upscaling. A comparison between the data from laboratory and in-situ tests and the estimation results indicated promising findings. The accuracy of the results was assessed by the analysis of variance (ANOVA). In addition to modeling the static moduli, DTS can be used to verify the static and dynamic moduli values with appropriate accuracy when core data is not available.

Dynamic modeling of hospitalized COVID-19 patients reveals disease state dependent risk factors

The global pandemic of the SARS-CoV-2 coronavirus has significantly strained hospital resources worldwide. Improved understanding of the COVID-19 disease trajectory for patients requiring hospitalization would allow for the development of more targeted preventative, diagnostic and therapeutic strategies. A covariate-dependent, continuous-time hidden Markov model with four states (moderate-illness, severe-illness, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1,362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2. Demographic characteristics, co-morbidities, vital signs and laboratory test results were retrospectively evaluated to predict clinical progression and outcomes. Several patient-level covariates were associated with differential impacts on the risk of progression. Specifically, while being male, being black or having a medical co-morbidity were all associated with an increased risk of progressing from the moderate to severe disease state, these factors resulted in a decreased risk of transitioning from the severe to the deceased disease state. Body mass index (BMI) alone was not found to be associated with an increased risk of disease progression, while higher age was associated with an increased risk in progressing from moderate to severe and from severe to deceased states. Regardless of the differential risk profiles, all covariates considered other than BMI and asthma were associated with an overall increased risk of transitioning to the deceased state. Recent studies have not included analyses of the temporal progression of COVID-19, making the current study a unique modeling-based approach to understand the dynamics of COVID-19 in hospitalized patients. Such dynamic risk stratification models have the potential not only to improve clinical outcomes in COVID-19, but also a myriad of other acute and chronic diseases that, to date, have largely been assessed only by static modeling techniques.