Browsing by Subject "Reservoir engineering"
Now showing 1 - 9 of 9
Results Per Page
Sort Options
Item Analyzing databases using data analytics(2015-12) Lee, Boum Hee; Lake, Larry W.; Mohanty, Kishore KThere are many public and private databases of oil field properties the analysis of which could lead to insights in several areas. The recent trend of Big Data has given rise to novel analytic methods to effectively handle multidimensional data, and to visualize them to discover new patterns. The main objective of this research is to apply some of the methods used in data analytics to datasets with reservoir data. Abstract Abstract Using a commercial reservoir properties database, we created and tested three data analytic models to predict ultimate oil and gas recovery efficiencies, using the following methods borrowed from data analytics: linear regression, linear regression with feature selection, and Bayesian network. We also adopted similarity ranking with principal component analysis to create a reservoir analog recommender system, which recognizes and ranks reservoir analogs from the database. Abstract Among the models designed to estimate recovery factors, the linear regression models created with variables selected with sequential feature selection method performed the best, showing strong positive correlations between actual and predicted values of reservoir recovery efficiencies. Compared to this model, Bayesian network model, and simple linear regression model performed poorly. Abstract For the reservoir analog recommender system, an arbitrary reservoir is selected, and different distance metrics were used to rank analog reservoirs. Because no one distance metric (and hence the given reservoir analog list) is superior to the other, the reservoirs given in the recommended list are compared along with the characteristics of distance metrics.Item Development of a two-phase flow coupled capacitance resistance model(2014-12) Cao, Fei, active 21st century; Lake, Larry W.The Capacitance Resistance Model (CRM) is a reservoir model based on a data-driven approach. It stems from the continuity equation and takes advantage of the usually abundant rate data to achieve a synergy of analytical model and data-driven approach. Minimal information (rates and bottom-hole pressure) is required to inexpensively characterize the reservoir. Important information, such as inter-well connectivity, reservoir compressibility effects, etc., can be easily and readily evaluated. The model also suggests optimal injection schemes in an effort to maximize ultimate oil recovery, and hence can assist real time reservoir analysis to make more informed management decisions. Nevertheless, an important limitation in the current CRM model is that it only treats the reservoir flow as single-phase flow, which does not favor capturing physics when the saturation change is large, such as for an immature water flood. To overcome this limitation, we develop a two-phase flow coupled CRM model that couples the pressure equation (fluid continuity equation) and the saturation equation (oil mass balance). Through this coupling, the model parameters such as the connectivity, the time constant, temporal oil saturation, etc., are estimated using nonlinear multivariate regression to history match historical production data. Incorporating the physics of two-phase displacement brings several advantages and benefits to the CRM model, such as the estimation of total mobility change, more accurate prediction of oil production, broader model application range, and better adaptability to complicated field scenarios. Also, the estimated saturation within the drainage volume of each producer can provide insights with respect to the field remaining oil saturation distribution. Synthetic field case studies are carried out to demonstrate the different capabilities of the coupled CRM model in homogeneous and heterogeneous reservoirs with different geological features. The physical meanings of model parameters are well explained and validated through case studies. The results validate the coupled CRM model and show improved accuracy in model parameters obtained through the history match. The prediction of oil production is also significantly improved compared to the current CRM model. A more reliable oil rate prediction enables further optimization to adjust injection strategies. The coupled CRM model has been shown to be fast and stable. Moreover, sensitivity analyses are conducted to study and understand the impact of the input information (e.g., relative permeability, viscosity) upon the output model parameters (e.g., connectivity, time constants). This analysis also proves that the model parameters from the two-phase coupled model can combine both reservoir compressibility and mobility effects.Item Gas storage facility design under uncertainty(2009-12) Ettehadtavakkol, Amin, 1984-; Jablonowski, Christopher J.; Lake, Larry W.In the screening and concept selection stages of gas storage projects, many estimates are required to value competing projects and development concepts. These estimates are important because they influence which projects are selected and which concept proceeds into detailed engineering. In most cases, there is uncertainty in all of the estimates. As a result, operators are faced with the complex problem of determining the optimal design. A systematic uncertainty analysis can help operators solve this problem and make better decisions. Ideally, the uncertainty analysis is comprehensive and includes all uncertain variables, and simultaneously accounts for reservoir behavior, facility options, and economic objectives. This thesis proposes and demonstrates a workflow and an integrated optimization model for uncertainty analysis in gas storage. The optimization model is fast-solving and eliminates most constraints on the scope of the uncertainty analysis. Using this or similar workflows and models should facilitate analysis and communication of results within the project team and with other stakeholders.Item An integrated geologic model of Valhall oil field for numerical simulation of fluid flow and seismic response(2007-05) Chakraborty, Samarjit; Ferguson, Robert J., Ph. D.Time-lapse seismic monitoring promises to be a valuable tool for reservoir engineering as it provides dynamic data over the entire field rather than the spatially limited production data. In this thesis, I develop a link between computerized reservoir simulation, rock physics, and seismic analysis. I present an example study of time-lapse seismic effects in a sequence of reservoir simulation, rock physics, and seismic forward modeling. The thesis includes a case-study of the Valhall field which I propose be used for an integrated geologic model for fluid flow and seismic simulation. I combine fluid flow simulation studies with a parallel flow simulation code IPARS to obtain computed pore pressure and oil saturation at different spatial location as a function of time. The reservoir model for fluid flow simulation input is linear and isotropic. The reservoir model has an injection well below the oil-water contact and a producer well at a shallower level. The variations of pore pressure due to injection and production cause 3-D multi-phase fluid flow in the reservoir with time. I develop a rock physics mapping code to estimate the P-wave and S-wave seismic velocities and densities for seismic forward modeling from pore pressure and water and oil saturation obtained by fluid flow simulation. The rock physics code uses Gassmann's relations for fluid substitution to compute the seismic rejection parameters. Migrated depth sections show brightening of amplitude values near the producer well as a function of time. Rejections from the production zone appear stronger indicating high oil saturation values with increasing production. I develop a case-study of the Valhall Field to make an integrated geologic model for fluid flow and seismic simulation. Based on an initial description of reservoir geology, I combine rock-physics measurements, fluid properties, geomechanics,seismic, well, and checkshot data, to build an integrated model for simulations of subsurface fluid-flow and surface seismic data.Item Investigation of analytical models incorporating geomechanical effects on production performance of hydraulically and naturally fractured unconventional reservoirs(2014-08) Aybar, Umut; Sepehrnoori, Kamy, 1951-; Patzek, Tadeusz W.Petroleum and Geosystems EngineeringItem Quantification of production recovery using probabilistic approach and semi-analytical model for unconventional oil reservoirs(2015-12) Choi, Bong Joon; Srinivasan, Sanjay; Sepehrnoori, Kamy, 1951-Decline curve analysis is widely applied for production forecasting in oil & gas industry. However, many models do not work for super-tight, unconventional wells with dominant fracture flows. Some novel decline models have been introduced for unconventional plays, but the transition time between the transient and pseudo-steady flow period is difficult to model with such pure empirical relations. Consequently, the decline projections are often inaccurate and furthermore, they are difficult to quantify the uncertainty associated with the predictions. To address these issues, a combined probabilistic approach is proposed that uses a dual-porosity semi-analytical decline model within an extended bootstrap framework in order to provide estimates for the P10, P50 and P90 production profiles. The probabilistic method employed in this research is a data-generative approach that employs modified bootstrap method to generate multiple decline model projections. The semi-analytical model is an approximate decline model that optimizes parameters describing flow in matrix-fracture systems using the observed production profile. In the proposed method, probabilistic approach and semi-analytical decline model are combined. The modified approach is compared to the performances developed with Arps’ hyperbolic model. Both models are fitted by optimizing respective parameters and 50 synthetic data sets are used to draw confidence interval projections. The probabilistic approach is extended by proposing alternate blocking techniques – variance of the mean and analysis of the variance (ANOVA), in place of a scheme based on the autocorrelation exhibited by the decline data, originally implemented by other researchers. The cumulative production and forecast period production errors are calculated for these alternative schemes. For all proposed applications, two unconventional, horizontal oil wells are used to test the results. Both these wells exhibit sharp decline in production rate in the first few months that is related to fracture flow regimes. The results show that the proposed application of semi-analytical model with probabilistic approach significantly improved the projections. The implementation of alternate blocking techniques also show improvement in confidence interval projections, The resultant uncertainty distributions are more accurate and precise than those obtained using the autocorrelation based schemes. The combined results show that ANOVA blocking technique outperformed the other two techniques.Item A sensitivity study on modified salinity waterflooding and its hybrid processes(2016-05) Bissakayev, Beibit; Sepehrnoori, Kamy, 1951-; Kazemi Nia Korrani, AboulghasemWaterflood is one of the most widely used techniques in enhanced oil recovery. In 1990s researchers came to conclusion that the chemistry of the injected water can be important in improving oil recovery. The low salinity water injection (LoSal® ) has become one of the promising topics in the oil industry. It is believed that the main mechanism for incremental oil recovery in low salinity flooding is wettability alteration. Several papers discussed that the wettability alteration from oil-wet to mixed- or water-wet takes place due to clay swelling and expanding of double layer in sandstones and calcite dissolution along with rock surface reactions in carbonates. However, there is no consensus on a single main mechanism for the low salinity effect on oil recovery. The main objective of this research is to conduct sensitivity analysis on main parameters in low salinity waterflooding and its hybrid processes affecting oil recovery in carbonates. We compare results by using coupled reservoir simulator UTCOMP-IPhreeqc. UTCOMP is the compositional reservoir simulator developed at the Center for Petroleum and Geosystems Engineering in The University of Texas at Austin. IPhreeqc is the module-based version of the PHREEQC geochemical package, a state-of-the-art geochemical package developed by the United States Geological Survey (USGS). We investigate the effect of low salinity water and carbon dioxide on oil recovery from carbonates by modeling the processes through the UTCOMP-IPhreeqc simulator. We perform sensitivity analysis on continuous gas injection (CGI), water-alternating-gas (WAG) flooding, and polymer-water-alternate-water (PWAG) flooding. We study the significance of reservoir parameters, such as reservoir heterogeneity (Dykstra-Parsons coefficient, Vdp, and crossflow, kv/kh), the salinity of injected water, the composition of gas, and polymer concentration in polymer-water solution on cumulative oil recovery. Moreover, we study the importance of inclusion of the hydrocarbon CO2 impact on the aqueous-rock geochemistry by comparing two scenarios where in one scenario the hydrocarbon CO2 effect is included in UTCOMP-IPhreeqc whereas in the other one the effect is neglected. Finally, we perform sensitivity analysis on PWAG flooding for most influential design parameters using Design of Expert software. The reservoir parameters, such as average reservoir permeability, reservoir heterogeneity, and crossflow and injected polymer-water solution parameters, such as polymer concentration and salinity of injected water are optimization parameters in this study.Item Simple mechanistic modeling of recovery from unconventional oil reservoirs(2015-05) Ogunyomi, Babafemi Anthony; Lake, Larry W.; Sepehrnoori, Kamy; Srinivasan, Sanjay; Jablonowski, Christopher J; Bickel, James EDecline curve analysis is the most widely used method of performance forecasting in the petroleum industry. However, when these techniques are applied to production data from unconventional reservoirs they yield model parameters that result in infinite (nonphysical) values of reserves. Because these methods were empirically derived the model parameters are not functions of reservoir/well properties. Therefore detailed numerical flow simulation is usually required to obtain accurate rate and expected ultimate recovery (EUR) forecast. But this approach is time consuming and the inputs in to the simulator are highly uncertain. This renders it impractical for use in integrated asset models or field development optimization studies. The main objective of this study is to develop new and “simple” models to mitigate some of these limitations. To achieve this object field production data from an unconventional oil reservoir was carefully analyzed to identify flow regimes and understand the overall decline behavior. Using the result from this analysis we use design of experiment (DoE), numerical reservoir simulation and multivariate regression analysis to develop a workflow to correlate empirical model parameters and reservoir/well properties. Another result from this analysis showed that there are at least two time scales in the production data (existing empirical and analytical model do not account for this fact). Double porosity models that account for the multiple time scales only have complete solutions in Laplace space and this make them difficult to use in optimization studies. A new approximate analytical solution to the double porosity model was developed and validated with synthetic data. It was shown that the model parameters are functions of reservoir/well properties. In addition, a new analytical model was developed based on the parallel flow conceptual model. A new method is also presented to predict the performance of fractured wells with complex fracture geometries that combines a fundamental solution to the diffusivity equation and line/surface/volume integral to develop solutions for complex fracture geometries. We also present new early and late time solutions to the double porosity model that provide explicit functions for skin and well/fracture storage, which can be used to improve the characterization of fractured horizontal wells from early-time production data.Item Understanding unstable immiscible displacement in porous media(2015-05) Doorwar, Shashvat; Mohanty, Kishore Kumar; Pope, Gary; DiCarlo, David; Huh, Chun; Weerasooriya, Upali; Hidrovo, CarlosOur global heavy and viscous oil reserves are immense. 70% of our current global oils reserves are viscous or heavy. For an energy secure future, exploitation of heavy oil reserves is necessary to mitigate the impact of steadily declining conventional reserves. Though most viscous and heavy oils are produced by thermal stimulation, several cases do exist where thermal methods are neither technically feasible nor economically profitable. In such cases, non-thermal EOR methods have to be applied. Any displacement process at such high viscosity ratio will be influenced by viscous fingering. Polymers are typically added to the water to stabilize the displacement but for oils above a couple of 100 cp viscosity a stable displacement is not feasible. As unstable displacements are not very well understood, visualization along with experimentation is critical for understanding and modeling the process. In this study, multi-scale experimental strategy was employed; experiments were conducted in cores at lab-scale to generate quantifiable data and were repeated in small micro-fluidic cells for visualization of the mechanism. Polymer flood as an alternative non-thermal process in a structurally complex carbonate formation was tested. In carbonates formations, thermal methods are not preferred as mineral dissolution and precipitation lead to formation damage. Effect of timing of polymer flood was studied in great details. Result from both the micromodels and core-floods indicate that for heavy oils, unlike light oils, timing of polymer injection is not critical and a tertiary polymer flood at the completion of waterflood can also produce significant incremental oil. In some cases, tertiary polymer flood even out-performs a secondary polymer flood. A major problem with modeling and predicting the performance of an unstable flood is largely due to our inability to accurately capture viscous fingering or its effects. Viscous fingering is a complex phenomenon and is dependent on several parameters such as injection rate, viscosity ratios, heterogeneity and dimensions. The micromodels were used to visualize the variation in flow pattern at different viscosity ratio and injection rates while core floods provided essential modeling data. Based on the results two new models were developed: a simplified network model that could accurately predict the viscous fingers for all viscosity ratios and a lumped model that capture the effect of viscous fingers at larger scales through pseudo-relative permeability functions. A dimensionless scaling parameter similar to the instability parameter of Peters and Flock (1981) was also developed that is useful in predicting the recoveries of all unstable displacement at various viscosity ratios, injection rate, permeability and width. The scaling parameter showed excellent fit with experimental data of over 60 experiments.