Petroleum exploration and development are capital intensive and smart economic decisions that need to be made to profitably extract oil and gas from the reservoirs. Accurate quantification of uncertainty in production forecasts will help in assessing risk and making good economic decisions. This study investigates the effect of combining dynamic data with the uncertainty in static data to see the effect on estimates of uncertainty in production forecasting. Fifty permeability realizations were generated for a reservoir in west Texas from available petrophysical data. We quantified the uncertainty in the production forecasts using a likelihood weighting method and an automatic history matching technique combined with linear uncertainty analysis. The results were compared with the uncertainty predicted using only static data. We also investigated approaches for best selecting a smaller number of models from a larger set of realizations to be history matched for quantification of uncertainty. We found that incorporating dynamic data in a reservoir model will result in lower estimates of uncertainty than considering only static data. However, incorporation of dynamic data does not guarantee that the forecasted ranges will encompass the true value. Reliability of the forecasted ranges depends on the method employed. When sampling multiple realizations of static data for history matching to quantify uncertainty, a sampling over the entire range of realization likelihoods shows larger confidence intervals and is more likely to encompass the true value for predicted fluid recoveries, as compared to selecting the best models.

This dissertation describes research designed to enhance hydrometeorological forecasts. The objective of the research is to deliver an optimal methodology to produce reliable, skillful and economically valuable probabilistic temperature and precipitation forecasts. Weather plays a dominant role for energy companies relying on forecasts of watershed precipitation and temperature to drive reservoir models, and forecasts of temperatures to meet energy demand requirements. Extraordinary precipitation events and temperature extremes involve consequential water- and power-management decisions. This research compared weighted-average, recursive, and model output statistics bias-correction methods and determined optimal window-length to calibrate temperature and precipitation forecasts. The research evaluated seven different methods for daily maximum and minimum temperature forecasts, and three different methods for daily quantitative precipitation forecasts, within a region of complex terrain in southwestern British Columbia, Canada. This research then examined ensemble prediction system design by assessing a three-model suite of multi-resolution limited area mesoscale models. The research employed two different economic models to investigate the ensemble design that produced the highest-quality, most valuable forecasts. The best post-processing methods for temperature forecasts included moving-weighted average methods and a Kalman filter method. The optimal window-length proved to be 14 days. The best post-processing methods for achieving mass balance in quantitative precipitation forecasts were a moving-average method and the best easy systematic estimator method. The optimal window-length for moving-average quantitative precipitation forecasts was 40 days. The best ensemble configuration incorporated all resolution members from all three models. A cost/loss model adapted specifically for the hydro-electric energy sector indicated that operators managing rainfall-dominated, high-head reservoirs should lower their reservoir with relatively low probabilities of forecast precipitation. A reservoir-operation model based on decision theory and variable energy pricing showed that applying an ensemble-average or full-ensemble precipitation forecast provided a much greater profit than using only a single deterministic high-resolution forecast. Finally, a bias-corrected super-ensemble prediction system was designed to produce probabilistic temperature forecasts for ten cities in western North America. The system exhibited skill and value nine days into the future when using the ensemble average, and 12 days into the future when employing the full ensemble forecast.

A rigorous three-phase asphaltene precipitation model was implemented into a compositional reservoir simulator to represent and estimate the reduction of porosity and permeability responsible for productivity impairment. Previous modeling techniques were computationally inefficient, showed thermodynamic inconsistencies, or required special laboratory experiments to characterize the fluid. The approach developed in this study uses a cubic equation of state to solve for vapor/liquid/liquid equilibrium (VLLE), where asphaltene is the denser liquid phase. Precipitation from the liquid mixture occurs as its solubility is reduced either by changes in pressure (natural depletion), or composition (i.e. mixing resulting from gas injection). The dynamic relationship between phase composition, pressure, and porosity/permeability is modeled with a finite differences reservoir simulator and solved using an implicit-pressure, explicit-saturations and explicit-compositions (IMPESC) direct sequential method. The robustness of this model is validated by the ability to reproduce experimental asphaltene precipitation data while predicting the expected phase behavior envelope and response to key thermodynamic variables (i.e. type of components and composition, pressure and, temperature). The three-phase VLLE flash provides superior thermodynamic predictions compared to existing commercial techniques. Computer performance analysis showed that the model has a comparable cost to existing asphaltene precipitation models, taking only 1.1 more time to calculate but requiring fewer tunable parameters. The VLLE flash was in average 4.47 times slower compared to a conventional two-phase vapor/liquid flash. This model has the speed of a flash calculation while maintaining thermodynamic consistency, enabling efficient optimization of reservoir development strategies to mitigate the detrimental effects of asphaltene precipitation on productivity.

Over the last four decades there has been considerable research in the improvement of imaging exo-atmospheric objects through air turbulence from ground-based instruments. Whilst such research was initially motivated for military purposes, the benefits to the astronomical community have been significant. A key topic in this research is isoplanatism. The isoplanatic angle is an angular limit that separates two point-source objects, where if independent measurements of wavefront perturbations were obtained from each source, the wavefront distortion would be considered equivalent. In classical adaptive optics, perturbations from a point-source reference, such as a bright, natural guide star, are used to partially negate perturbations distorting an image of a fainter, nearby science object. Various techniques, such as atmospheric tomography, maximum a posteriori (MAP), and parameterised modelling, have been used to estimate wavefront perturbations when the distortion function is spatially variant, i.e., angular separations exceed the isoplanatic angle, θ₀, where θ₀ ≈ 10 μrad for mild distortion at visual wavelengths. However, the effectiveness of such techniques is also dependent on knowledge a priori of turbulence profiles and configuration data. This dissertation describes a new method used to estimate the eigenvalues that comprise wavefront perturbations over a wide, spatial field. To help reduce dependency on prior knowledge for specific configurations, machine learning is used with a recurrent neural network trained using a posteriori wavefront ensembles from multiple point-source objects. Using a spatiotemporal framework for prediction, the eigenvalues, in terms of Zernike polynomials, are used to reconstruct the spatially-variant, point spread function (SVPSF) for image restoration. The overall requirement is to counter the adverse effects of atmospheric turbulence on the images of extended astronomical objects. The method outlined in this thesis combines optical wavefront sensing using multiple natural guide stars, with a reservoir-based, artificial neural network. The network is used to predict aberrations caused by atmospheric turbulence that degrade the images of faint science objects. A modified geometric wavefront sensor was used to simultaneously measure phase perturbations from multiple, point-source reference objects in the pupil. A specialised recurrent neural network (RNN) was used to learn the spatiotemporal effects of phase perturbations measured from several source references. Modal expansions, in terms of Zernike coefficients, were used to build time-series ensembles that defined wavefront maps of point-source reference objects. The ensembles were used to firstly train an RNN by applying a spatiotemporal training algorithm, and secondly, new data ensembles presented to the trained RNN were used to estimate the wavefront map of science objects over a wide field. Both simulations and experiments were used to evaluate this method. The results of this study showed that by employing three or more source references over an angular separation of 24 μrad from a target, and given mild turbulence with Fried coherence length of 20 cm, the normalised mean squared error of low-order Zernike modes could be estimated to within 0.086. A key benefit in estimating phase perturbations using a time-series of short exposure point-spread functions (PSFs) is that it is then possible to determine the long exposure PSF. Based on the summation of successive, corrected, short-exposure frames, high resolution images of the science object can be obtained. The method was shown to predict a contiguous series of short exposure aberrations, as a phase screen was moved over a simulated aperture. By qualifying temporal decorrelation of atmospheric turbulence, in terms of Taylor's hypothesis, long exposure estimates of the PSF were obtained.

No description available.

No description available.

In this study, the optimum conditions for wormhole propagation in horizontal well carbonate acidizing was investigated numerically using a horizontal well acidizing simulator. The factors that affect the optimum conditions are rock mineralogy, acid concentration, temperature and acid flux in the formation. The work concentrated on the investigation of the acid flux. Analytical equations for injection rate schedule for different wormhole models. In carbonate acidizing, the existence of the optimum injection rate for wormhole propagation has been confirmed by many researchers for highly reactive acid/rock systems in linear core-flood experiments. There is, however, no reliable technique to translate the laboratory results to the field applications. It has also been observed that for radial flow regime in field acidizing treatments, there is no single value of acid injection rate for the optimum wormhole propagation. In addition, the optimum conditions are more difficult to achieve in matrix acidizing long horizontal wells. Therefore, the most efficient acid stimulation is only achieved with continuously increasing acid injection rates to always maintain the wormhole generation at the tip of the wormhole at its optimum conditions. Examples of acid treatments with the increasing rate schedules were compared to those of the single optimum injection rate and the maximum allowable rate. The comparison study showed that the increasing rate treatments had the longest wormhole penetration and, therefore, the least negative skin factor for the same amount of acid injected into the formations. A parametric study was conducted for the parameters that have the most significant effects on the wormhole propagation conditions such as injected acid volume, horizontal well length, acid concentration, and reservoir heterogeneity. The results showed that the optimum injection rate per unit length increases with increasing injected acid volume. And it was constant for scenarios with different lateral lengths for a given system of rock/ acid and injected volume. The study also indicated that for higher acid concentration the optimum injection rate was lower. It does exist for heterogeneous permeability formations. Field treatment data for horizontal wells in Middle East carbonate reservoirs were also analyzed for the validation of the numerical acidizing simulator.

In a natural gas field, determining the life of the field and deciding the best production technique, meeting the economical considerations is the most important criterion. In this study, a field in Thrace Basin was chosen. Available reservoir data was compiled to figure out the characteristics of the field. The data, then, formatted to be used in the commercial simulator, IMEX, a subprogram of CMG (Computer Modeling Group). The data derived from the reservoir data, used to perform a history match between the field production data and the results of the simulator for a 3 year period between May 2002 and January 2005. After obtaining satisfactory history matching, it was used as a base for future scenarios. Four new scenarios were designed and run to predict future production of the field. Two new wells were defined for the scenarios after determining the best region in history matching. Scenario 1 continues production with existing wells, Scenario 2 includes a new well called W6, Scenario 3 includes another new well, W7 and Scenario 4 includes both new defined wells, W6 and W7. All the scenarios were allowed to continue until 2010 unless the wellhead pressure drops to 500 psi. None of the existing wells reached 2010 but newly defined wells achieved to be on production in 2010. After comparing all scenarios, Scenario 4, production with two new defined wells, W6 and W7, was found to give best performance until 2010. During the scenario 4, between January 2005 and January 2010, 7,632 MMscf gas was produced. The total gas production is 372 MMscf more than Scenario 2, the second best scenario which has a total production of 7,311MMscf. Scenario 3 had 7,260 MMscf and Scenario 1 had 6,821 MMscf respectively. A nodal system analysis is performed in order to see whether the initial flow rates of the wells are close to the optimum flow rates of the wells, Well 1 is found to have 6.9 MMscf/d optimum production rate. W2 has 3.2 MMscf/d, W3 has 8.3 MMscf/d, W4 has 4.8 MMscf/d and W5 has 0.95 MMscf/d optimum production rates respectively.

Characterization of connectivity in compartmentalized deepwater Gulf of Mexico (GoM) reservoirs is an outstanding challenge of the industry that can significantly impact the development planning and recovery from these assets. In these deep formations, temperature gradient can be quite significant and temperature data can provide valuable information about field connectivity, vertical fluid displacement, and permeability distribution in the vertical direction. In this paper, we examine the importance of temperature data by integrating production and temperature data jointly and individually and conclude that including the temperature data in history matching of deep GoM reservoirs can increase the resolution of reservoir permeability distribution map in the vertical direction. To illustrate the importance of temperature measurements, we use a coupled heat and fluid flow transport model to predict the heat and fluid transport in the reservoir. Using this model we ran a series of data integration studies including: 1) integration of production data alone, 2) integration of temperature data alone, and 3) joint integration of production and temperature data. For data integration, we applied four algorithms: Maximum A-Posteriori (MAP), Randomized Maximum Likelihood (RML), Sparsity Regularized Reconstruction and Sparsity Regularized RML methods. The RML and Sparsity Regularized RML approaches were used because they allow for uncertainty quantification and estimation of reservoir heterogeneity at a higher resolution. We also investigated the sensitivity of temperature and production data to the distribution of permeability, which showed that while production data primarily resolved the distribution of permeability in the horizontal direction, the temperature data did not display much sensitivity to permeability in the horizontal extent of the reservoir. The results of these experiments were compelling in that they clearly illuminated the role of temperature data in enhancing the resolution of reservoir permeability maps with depth. We present several experiments that clearly illustrate and support the conclusions of this study.

AbstractInside of the Norne E-segment remains a considerable amount of residual oil even after applying the primary and secondary oil recovery methods (water injection). Recently, several methods have been studied based on simulations to decrease the residual oil trapped by capillary forces and consequently improve the oil recoverability. Additionally, Norne E-segment is severely affected by stratigraphic barriers and faults of nature not sealing, semi sealing and completely sealing. Water Alternating Gas (WAG) and Simultaneously Water Alternating Gas (SWAG) injection techniques are presented as potential candidates to increase oil productivity in the Norne E-Segment by decreasing the gas mobility and capillary forces guarantying effective microscopic displacement due to gas flooding and macroscopic sweep created by water injection.In the first part of this study, based on simulations (Eclipse 100, Black oil simulator), sensitivity analyses of WAG cycles and WAG ratio were investigated combining with low injection rate and high injection rate. However, three WAG cycle were suggested (3 months, 6 months and 1years injection cycles) and different values of WAG ratio were studied based on low and high injection rates of water and gas. According to the results, WAG cycle doesn’t affect the fluids rates productions when low injection rate is used, but a slightly effect is noticed when high injection rate is applied, thus a slightly optimal WAG ratio was found to be 1:3 when high WAG ratio is used.As a sequence, examination of three different injection patters scenarios were simulated to optimize the oil recoverability using both techniques WAG and SWAG, namely: injection studies using the injection wells already existed; injection studies using the injection wells already existed by doing a new completion within Ile and Tofte formations; injection studies placing a new injection well plus new completion of the injection well. As a result, the last scenario using SWAG technique presented oil recovery around 73%, whose was approximately 5% higher than oil recoverability when WAG injection technique (68%), when high injection rate is applied.