The Dykstra-Parsons model describes layer 1-D oil displacement by water in multilayered reservoirs. The main assumptions of the model are: piston-like displacement of oil by water, no crossflow between the layers, all layers are individually homogeneous, constant total injection rate, and injector-producer pressure drop for all layers is the same. Main drawbacks of Dykstra-Parsons method are that it does not take into account Buckley-Leverett displacement and the possibility of different oil-water relative permeability for each layer. A new analytical model for layer 1-D oil displacement by water in multilayered reservoir has been developed that incorporates Buckley-Leverett displacement and different oilwater relative permeability and water injection rate for each layer (layer injection rate varying with time). The new model employs an extensive iterative procedure, thus requiring a computer program. To verify the new model, calculations were performed for a two-layered reservoir and the results compared against that of numerical simulation. Cases were run, in which layer thickness, permeability, oil-water relative permeability and total water injection rate were varied. Main results for the cases studied are as follows. First, cumulative oil production up to 20 years based on the new model and simulation are in good agreement. Second, model water breakthrough times in the layer with the highest permeability-thickness product (kh) are in good agreement with simulation results. However, breakthrough times for the layer with the lowest kh may differ quite significantly from simulation results. This is probably due to the assumption in the model that in each layer the pressure gradient is uniform behind the front, ahead of the front, and throughout the layer after water breakthrough. Third, the main attractive feature of the new model is the ability to use different oil-water relative permeability for each layer. However, further research is recommended to improve calculation of layer water injection rate by a more accurate method of determining pressure gradients between injector and producer.

The reserve growth potential of existing conventional oil reservoirs is huge. This research, through numerical simulation, aims to evaluate pattern size reduction as a strategy for improving waterflood recovery in undersaturated oil reservoirs. A plethora of studies have reported improvements in waterflood recovery resulting from pattern size reduction in heterogeneous reservoirs. The dependence of waterflood recovery on pattern size was attributed to factors such as areal reservoir discontinuity, preferential flooding directions, “wedge-edge” oil recovery, irregular pattern geometry, communication with water-bearing zones, vertical reservoir discontinuity, and project economics (Driscoll, 1974). Though many of these publications relied on decline curve analysis in estimating ultimate oil recovery, simulations completed in this thesis support their findings, specifically for compartmentalized reservoirs, fractured reservoirs, and layered reservoirs. Geostatistically-generated permeability fields were employed in the creation of various types of reservoir models. These models were populated with vertical production and injection wells. Sensitivity analysis was then performed on three development scenarios: 160, 40, and 10 acre five-spots. Based on assigned production and injection constraints, the quantity of oil recovered at simulation termination was used to calculate ultimate recovery efficiency. In homogeneous reservoir models, simulation results suggest that waterflood recovery was independent of pattern size. Similar results were also obtained from models with highly-variable non-zero permeabilities. On the other hand, pattern size reduction was found to enhance oil recovery from reservoir models with a high degree of permeability anisotropy. In such reservoirs, recovery was found to be highly dependent on bottom-hole injection pressures. The higher the injection pressure the larger the quantity of oil bypassed by widely spaced patterns. Likewise, high infill potential exists for reservoir models exhibiting areal discontinuity. In these types of models, the improvement in waterflood recovery resulting from pattern size reduction was directly related to the percentage of imbedded zero-permeability grid blocks. Ultimate oil recovery depended on the percolation of permeable grid blocks between production and injection wells. Increasing well density also enhanced waterflood recovery in vertically discontinuous reservoir models. In such layered reservoirs, the amount oil unswept with large patterns was considerably diminished because of the improved injection profiles associated with tighter patterns. / text

Reservoir characterization is very important in reservoir management to plan, monitor, predict and optimize oil production. Reservoir simulation is well-accepted in reservoir management but it requires many inputs, needs months to set up and complete a set of simulation runs, and contains large uncertainty in physical and geological properties. Therefore, simpler methods that provide quick results to complement or substitute reservoir simulation are important in decision making. Capacitance resistance model (CRM) is one of the methods. CRM is an input-output model derived from a continuity equation to quantify producer-injector connection strength during waterflood using solely production data. This work improves the CRM application method for waterflood and develops CRM theories and application methods for other recovery periods such as primary recovery and water-CO2 flood. A West Texas field test was carried out to validate CRM for a waterflood. The CRM fit was evaluated and used to optimize the oil production by changing injection rates. Through this first field experiment, a CRM application procedure was developed. With the CRM optimized injection schedule, the field gained 5372 bbls of additional oil production increase after one year. This research also quantitatively validates the CRM gain and time constant using synthetic fields and compares them to parameters of the streamline model, a complex model with similar purposes to the CRM. The CRM provides similar results as the streamline model with fewer inputs. The CRM was extended to primary recovery and water-CO2 flood. A new CRM equation – the integrated CRM (ICRM) - for primary recovery was developed and validated on many synthetic fields and an Oman field. The model can estimate dynamic pore volume, productivity index and average reservoir pressure that compare closely to simulated values and field knowledge. Additionally, the ability of CRM to quantify injector-producer connection strength and predict fluid production was examined on a synthetic water-CO2 flood field. A new oil production model to be used with CRM application in water-CO2 flood was developed and validated on synthetic data. The model predicts oil production from injection rate and relative permeability. CRM has successfully optimized waterflood on a West Texas field by reallocating the water from ineffective to effective injectors. New interpretations of the CRM parameters enable quantitative validation and integration of the CRM results with other methods. In primary recovery, the ICRM can estimate reservoir properties without requiring well testing which can cause loss of production. The CRM and the new oil production model can quickly characterize water-CO2 flood for short term production monitoring. / text

It is estimated that the shallow reservoirs of Ugnu, West Sak and Shraeder Bluff in the North Slope of Alaska hold about 20 billion barrels of heavy oil. The proximity of these reservoirs to the permafrost makes the application of thermal methods for the oil recovery very unattractive. It is feared that the heat from the thermal methods may melt this permafrost leading to subsidence of the unconsolidated sand (Marques 2009; Peyton 1970; Wilson 1972). Thus it is necessary to consider the development of cheap non-thermal methods for the recovery of these heavy oils. This study investigates non-thermal techniques for the recovery of heavy oils. Chemicals such as alkali, surfactant and polymer are used to demonstrate improved recovery over waterflooding for two oils (A:10,000cp and B:330 cp). Chemical screening studies showed that appropriate concentrations of chemicals, such as alkali and surfactant, could generate emulsions with oil A. At low brine salinity oil-in-water (O/W) emulsions were generated whereas water-in-oil (W/O) emulsions were generated at higher salinities. 1D and 2D sand pack floods conducted with alkali surfactant (AS) at different salinities demonstrated an improvement of oil recovery over waterflooding. Low salinity AS flood generated lower pressure drop, but also resulted in lower oil recovery rates. High salinity AS flood generated higher pressure drop, high viscosity emulsions in the system, but resulted in a greater improvement in oil recovery over waterfloods. Polymers can also be used to improve the sweep efficiency over waterflooding. A 100 cp polymer flood improved the oil recovery over waterflood both in 1D and 2D geometry. In 1D geometry 1PV of polymer injection increased the oil recovery from 30% after waterflood to 50% OOIP. The tertiary polymer injection was found to be equally beneficial as the secondary polymer injection. It was also found that the combined application of AS and polymer did not give any major advantage over polymer flood or AS flood alone. Chemical EOR technique was considered for the 330cp oil B. Chemical screening studies showed that microemulsions could be generated in the system when appropriate concentrations of alkali and surfactant were added. Solubilization ratio measurement indicted that the interfacial tension in the system approached ultra-low values of about 10-3 dynes/cm. The selected alkali surfactant system was tested in a sand pack flood. Additionally a partially hydrolyzed polymer was used to provide mobility control to the process. The tertiary injection of ASP (Alkali-Surfactant-Polymer) was able to improve the oil recovery from 60% OOIP after the waterflood to almost 98% OOIP. A simple mathematical model was built around viscous fingering phenomenon to match the experimental oil recoveries and pressure drops during the waterflood. Pseudo oil and water relative permeabilities were calculated from the model, which were then used directly in a reservoir simulator in place of the intrinsic oil-water relative permeabilities. Good agreement with the experimental values was obtained. For history matching the polymer flood of heavy oil, intrinsic oil-water relative permeabilities were found to be adequate. Laboratory data showed that polymer viscosity is dependent on the polymer concentration and the effective brine salinity. Both these effects were taken into account when simulating the polymer flood or the ASP flood. The filtration theory developed by Soo and Radke (1984) was used to simulate the dilute oil-in-water emulsion flow in the porous media when alkali-surfactant flood of the heavy oil was conducted. The generation of emulsion in the porous media is simulated via a reaction between alkali, surfactant, water and heavy oil. The theory developed by Soo and Radke (1984) states that the flowing emulsified oil droplets clog in pore constrictions and on the pore walls, thereby restricting flow. Once captured, there is a negligible particle re-entrainment. The simulator modeled the capture of the emulsion droplets via chemical reaction. Next, the local water relative permeability was reduced as the trapping of the oil droplets will reduce the mobility of the water phase. This entrapment mechanism is responsible for the increase in the pressure drop and improvement in oil recovery. The model is very sensitive to the reaction rate constants and the oil-water relative permeabilities. ASP process for lower viscosity 330 cp oil was modeled using the UTCHEM multiphase-multicomponent simulator developed at the University of Texas at Austin. The simulator can handle the flow of three liquid phases; oil, water and microemulsion. The generation of microemulsion is modeled by the reaction of the crude oil with the chemical species present in the aqueous phase. The experimental phase behavior of alkali and surfactant with the crude oil was modeled using the phase behavior mixing model of the simulator. Oil and water relative permeabilities were enhanced where microemulsion is generated and interfacial tension gets reduced. Experimental oil recovery and pressure drop data were successfully history matched using UTCHEM simulator. / text

Waterflooding is currently the most commonly used method to improve oil recovery after primary depletion. The reservoir heterogeneity such as permeability distribution could negatively affect the performance of waterflooding. The presence of high permeability streaks could lead to an early water breakthrough at the producers and thus reduce the sweep efficiency in the field. One approach to counteract the impact of heterogeneity and to improve waterflood sweep efficiency is through optimal rate allocation to the injectors and producers. Through optimal rate control, we can manage the propagation of the flood front, delay water breakthrough at the producers and also increase the sweep and hence, the recovery efficiency. The arrival time optimization method uses a streamline-based method to calculate water arrival time sensitivities with respect to production and injection rates. It can also optimize sweep efficiency on multiple realizations to account for geological uncertainty. To extend the scope of this optimization method for more general conditions, this work utilized a finite difference simulator and streamline tracing software to conduct the optimization. Apart from sweep efficiency, another most widely used optimization method is to maximize the net present value (NPV) within a given time period. Previous efforts on optimization of waterflooding used optimal control theorem to allocate injection/production rates for fixed well configurations. The streamline-based approach gives the optimization result in a much more computationally efficient manner. In the present study, we compare the arrival time optimization and NPV optimization results to show their strengths and limitations. The NPV optimization uses a perturbation method to calculate the gradients. The comparison is conducted on a 4- spot synthetic case. Then we introduce the accelerated arrival time optimization which has an acceleration term in the objective function to speed up the oil production in the field. The proposed new approach has the advantage of considering both the sweep efficiency and net present value in the field.

Stable dispersions of magnetic nanoparticles that are already in use in biomedicine as image-enhancing agents, also have potential use in subsurface applications. Surface-coated nanoparticles are capable of flowing through micron-size pores across long distances in a reservoir with modest retention in rock. Tracing these contrast agents using the current electromagnetic tomography technology could potentially help track the flood-front in waterflood and EOR processes and characterize the reservoir. The electromagnetic (EM) tomography used in the petroleum industry today is based on the difference between the electrical conductivity of reservoir fluids as well as other subsurface entities. The magnetic nanoparticles that are considered in this study, however, change the magnetic permeability of the flooded region, which is a novel application of the existing EM tomography technology. As the first fundamental step, the magnetic permeability change in rock due to injecting magnetic nanoparticles is quantified as a function of particle and reservoir properties. Subsequently, a new formulation is devised to compute the sensitivity of magnetic measurements to magnetic permeability perturbations. The results are then compared with the sensitivity to conductivity perturbations to identify the application space of magnetic contrast agents. Using numerical simulations, the progress of magnetic nanoparticle bank is monitored in the reservoir through time-lapse magnetic tomography measurements that are expected. Initially, simple models for displacement of injection banks are assumed and the level of complexity is gradually increased to incorporate the realities of fluid flow in the reservoir. The fluid-flow behavior of the nanoparticles is dynamically integrated with time-lapse magnetic response. Since the nanoparticles could help illuminate the flow paths, they could be used to indirectly measure reservoir heterogeneities. Therefore, numerous case studies are demonstrated where reservoir heterogeneity could potentially be inferred. Finally, fundamental pore-scale models are developed as a first step towards the multiple fluid phases extension of the EM tomography application. Using magnetic nanoparticles to improve electromagnetic tomography provides several strategic advantages. One key advantage is that the magnetic nanoparticles provide high resolution measurements at very low frequencies where the conductivity contrast is hardly detectable and casing effect is manageable. In addition, the sensitivity of magnetic measurements at the early stages of the flood is significantly improved with magnetic nanoparticles. Moreover, the vertical resolution of magnetic measurements is significantly enhanced with magnetic nanoparticles present in the vicinity of source or receiver. The fact that the progress of the magnetic slug can be detected at very early stages of the flood, that the traveling slug’s vertical boundaries can be identified at low frequencies, that the reservoir heterogeneities could potentially be characterized, and that the magnetic nanoparticles can be sensed much before the actual arrival of the slug at the observer well, provides significant value of using magnetic contrast agents for reservoir illumination. / text

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. / text

Much effort of reservoir engineers is devoted to the time-consuming process of history matching in a simulator to understand the reservoir complexity. Its accuracy is debatable because only a few inputs are known. Several analytical tools have been developed to investigate reservoir heterogeneity. The reciprocal productivity index (RPI) is a tool to measure the pressure support observed at a producer. The log (water-oil ratio or WOR) plot can be used to indicate the presence of a channel. A capacitance-resistance model (CRM) is a simple tool to estimate the connectivity between a producer-injector pair from the production/injection and pressure data. Generally field operators implement an improved recovery plan such as water-alternating-gas (WAG) flood to improve displacement efficiency. However, the existence of heterogeneity compromises its performance. The first objective of this study is to improve the assessment of tertiary flood performance by integrating the CRM with other analytical tools. The integrated method was applied to a miscible flood field in West Texas. The results suggest strong interwell connectivity found more frequently in the NE-SW direction and the different preferential flow paths of injected CO2 and water. Overall, the results provide insights into the current flood status. The operating conditions of a producer dynamically change because of well/field constraints. These changes can induce significant interference in other wells, which cannot be captured by CRM. The second objective of this study is to develop a capacitance-resistance model with producer-producer interaction (CRMP-P). The CRMP-P, derived from the continuity and Darcy’s equations, accounts for producer-producer interactions. The CRMP-P was applied to data from three different reservoir models. The results suggest that the CRMP-P could fit the data with higher precision than CRM. Consequently, the CRMP-P estimates of reservoir properties are more accurate. Moreover, the estimated transmissibility between producers is in agreement with the reservoir models. The CRMP-P was also applied to Omani field data. The transmissibility results are consistent with previous study and the drilling information. The more accurate information on producer-producer interactions and reservoir properties can assist in history-matching, locating infill wells, and reservoir management planning. / text