Browsing by Subject "Predictive model"
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Item Designing a laboratory model test program for developing a new offshore anchor(2015-05) Huang, Yunhan; Gilbert, Robert B. (Robert Bruce), 1965-; Rathje, Ellen MThe Flying Wing Anchor (patent pending) is a new anchor concept that combines the features of dynamically penetrating anchors, drag embedment anchors, and plate anchors. To study and optimize the behavior of the new anchor, this study developed a simplified predictive model and a new data acquisition system for performing physical model tests. The simplified predictive model couples a limit-equilibrium-based model for the anchor line and a plasticity-based model for the anchor to predict the embedment trajectory and holding capacity of the new anchor. The new data acquisition system is used to record data from sensors and control the movement of an electric motor. The system was developed by LabVIEW and demonstrated with a model test. The following major conclusions are drawn from this work about the behavior of this anchor concept in clay: (1) The pitch angle at the initiation of dive can be optimized to achieve the maximum dive depth and ultimate holding capacity. (2) The maximum depth of the dive is not strongly dependent on the undrained shear strength of the soil, while the ultimate holding capacity is proportional to the undrained shear strength of the soil at the maximum dive depth. (3) A smaller diameter of the line makes the anchor dive deeper and increases the ultimate capacity. (4) A deeper initial embedment depth after free fall makes the anchor dive deeper and increases the ultimate capacity. (5) A series of model tests to calibrate the simplified predictive model for the performance of the anchor should consist of varying the thickness of the line, the depth of initial embedment, the pitch angle at the initiation of dive, and the profile of undrained shear strength versus depth. It is recommended that model tests be conducted using the guidance presented in this thesis.Item Forecasting of isothermal enhanced oil recovery (EOR) and waterflood processes(2011-12) Mollaei, Alireza; Delshad, Mojdeh; Lake, Larry W.; Patzek, Tadeusz W.; Edgar, Thomas F.; Lasdon, Leon S.Oil production from EOR and waterflood processes supplies a considerable amount of the world's oil production. Therefore, the screening and selection of the best EOR process becomes important. Numerous steps are involved in evaluating EOR methods for field applications. Binary screening guides in which reservoirs are selected on the basis of reservoir average rock and fluid properties are consulted for initial determination of applicability. However, quick quantitative comparisons and performance predictions of EOR processes are more complicated and important than binary screening that are the objectives of EOR forecasting. Forecasting (predicting) the performance of EOR processes plays an important role in the study, design and selection of the best method for a particular reservoir or a collection of reservoirs. In EOR forecasting, we look for finding ways to get quick quantitative results of the performance of different EOR processes using analytical model/s before detailed numerical simulations of the reservoirs under study. Although numerical simulation of the reservoirs is widely used, there are significant obstacles that restrict its applicability. Lack of necessary reservoir data and time consuming computations and analyses can be barriers even for history matching and/or predicting EOR/waterflood performance of one reservoir. There are different forecasting (predictive) models for evaluation of different secondary/tertiary recovery methods. However, lack of a general purpose EOR/waterflood forecasting model is unsatisfactory because any differences in results can be caused by differences in the model rather than differences in the processes. As the main objective of this study, we address this deficiency by presenting a novel and robust analytical-base general EOR and waterflood forecasting model/tool (UTF) that does not rely on conventional numerical simulation. The UTF conceptual model is based on the fundamental law of material balance, segregated flow and fractional flux theories and is applied for both history matching and forecasting the EOR/waterflood processes. The forecasting model generates the key results of isothermal EOR and waterflooding processes including variations of average oil saturation, recovery efficiency, volumetric sweep efficiency, oil cut and oil rate with real or dimensionless time. The forecasting model was validated against field data and numerical simulation results for isothermal EOR and waterflooding processes. The forecasting model reproduced well (R2> 0.8) all of the field data and reproduced the simulated data even better. To develop the UTF for forecasting when there is no injection/production history data, we used experimental design and numerical simulation and successfully generated the in-situ correlations (response surfaces) of the forecasting model variables. The forecasting model variables were proven to be well correlated to reservoir/recovery process variables and can be reliably used for forecasting. As an extension to the abilities of the forecasting model, these correlations were used for prediction of volumetric sweep efficiency and missing/dynamic pore volume of EOR and waterflooding processes.