Oil displacement by injection of chemical solutions is a widespread Enhanced Oil Recovery method. Analytical models for one-dimensional displacement of oil by water with chemical components have been developed since the 1960´s. This problem involves complex physical-chemical processes of interphase mass transfer, phase transitions and transport properties changes. The continuous injection of this kind of fluid would be very expensive, so, the injection of chemical slugs is an attractive alternative to improve the recovery of mature oil fields. The continuous injection of chemical solutions is a Riemann problem, easily solved by the introduction of a self-similar variable. Nevertheless, the slug injection is not self-similar, and the problem becomes much more complex from the mathematical point of view. This paper presents the analytical solution of chemical slug injection in an oil reservoir. A flow potential associated with the conservation of the aqueous phase is introduced as a new independent variable instead of time. This change of variables allows the system splitting into one equation (lifting equation) and a thermodynamic system (auxiliary system). The number of auxiliary equations is less than the number of equations in the compositional model by one. In this paper different adsorption isotherms were analyzed. The transport equation solution, or lifting problem solution involves interaction between waves of different families and allows the design of slug sizes in order to obtain the maximum efficiency of this process. Another important application of these solutions is the prediction of chemical flooding regardless of the transport properties (relative permeabilities and viscosities). If the mobility ratio is close to one, this model may be applied in the development of streamlines simulators.

Recent developments in bottomhole data acquisition techniques, such as distributed temperature sensing systems (DTS), have brought attention to the potential increase of information that can be obtained from temperature data. Studies have shown the application of temperature surveys to estimate flowrate profiles, resolve the kind of damage around the well, improve the robustness of the history matching, among others. Nonetheless, Temperature Transient Analysis (TTA) is not a mature technique and its capabilities have not been explored fully yet. In order to investigate the application of temperature analysis to the hydraulic fracturing problem, in addition to pressure analysis, a numerical model was developed to calculate pressure and temperature responses. Regarding the fracture and reservoir fluid flow, a general approach can be adopted, where the formation permeability and fracture characteristics dictate how the fluids flow during and after fracture growth. We developed a comprehensive model, which accounts for the pressure effect on the temperature response, as well as a dynamic fracture that grows and eventually is allowed to close during falloff. In this research we analyzed the temperature and pressure responses during and immediately after hydraulic fracturing in order to improve our knowledge of this complicated physical problem. Based on this study, we can better understand not only the fracture properties, but also the reservoir itself. In addition, sensitivity analysis shows how reservoir permeability can impact final fracturing performance, as well as pressure and temperature responses. The developed model is also applied to simulate minifrac analysis, and a field example is presented that shows a good agreement with the simulated behavior during fracture closure. This paper highlights the potential of Temperature Transient Analysis, expanding the application of temperature in addition to pressure transient analysis to improve the characterization of fractured wells: TTA has potential to reduce uncertainty related to fracture length and reservoir permeability. TTA also adds value to DTS information, which is commonly measured but often not fully used. Temperature analysis has the potential to give reliable information about the flow dynamics of the reservoir and especially about near well zone.

Since the beginning of the 80´s, more and more Pressure Downhole Gauges (PDG´s) were installed in production and injection wells. The main purpose was well monitoring and evaluation of reservoir performance. This kind of gauge provides a large amount of data, but there are different problems that do not allow their complete use. These problems are related to quantity and quality. Most of the softwares are not able to handle with all the data available in a long recording period, the main objective of the use of PDG´s. Besides that, gauges present noise and strange data (outliers). It becomes necessary to treat the data before its use in the study of reservoir behavior. The concept of wavelet transform was developed about a century ago, but it was during the 80´s that it received practical application. Now it is used in a wide variety of applications, and, especially in data treatment, it presents two main features that make it attractive: smoothing of the signal and retention of the details. In this paper we use the wavelet transform to overcome the main problems and enhance the reliability of the information contained in the data. We tested different wavelets with several decomposition levels against actual and synthetic data. The outliers must be removed before the wavelet analysis in order to enhance its performance. We show that there is no unique "correct" wavelet transform to apply in any flow period. For example, for some datasets the Daubechies 1 wavelet at decomposition level 6 was used for flow periods, and level 3 for build up. It is important to notice that the use of high decomposition levels may cause loss of information, as it is shown in some examples. The wavelet analysis was also used to recognize transients from its ability to enhance the details. This is particularly useful to identify flow rate changes when they have not been reported, for example. Finally, we propose a methodology to treat the data before its use in posterior interpretation. Introduction Reservoir characterization has been a major research subject in reservoir engineering. The main goal is to estimate the spatial distribution of the reservoir properties, e.g., permeability and porosity, by integration of all kinds of available information1. Pressure data from pressure downhole gauges (PDG) provide more information than traditional pressure tests. The long period data can indicate how reservoir properties are changing during exploitation. The use of these instruments is very recent and a specific methodology to data interpretation is not available yet. The use of long-term data requires special handling and interpretation techniques due to the instability of in-situ permanent data acquisition systems, extremely large volume of data, incomplete flow rate history caused by unmeasured and uncertain rate changes, and dynamic changes in reservoir conditions and properties throughout the life of the reservoir2. The reservoir pressure is probably the most important information to monitor reservoir condition, to characterize the reservoir, to find best recovery methods and to determine future behavior of reservoir. The characteristics of simulated pressure transient data were investigated in the frequency domain3. Wavelet transform was introduced to accomplish the time-frequency analysis of long-term pressure transient data. A set of data can be treated as a signal. Most of the signals in practice are time-domain signals in their raw format, but frequently the information that cannot be readily seen in the time-domain can be seen in the frequency domain. Mathematical transformations are applied to signals to obtain further information from signals that is not readily available in the raw signal. There is a huge list of transforms and the Fourier Transform is probably the most commonly used. The Wavelet Transform is a kind of transform which provides time-frequency representation. It is a useful tool for analyzing time series with many different timescales or changes in variance. Wavelet Transform is now used in a wide variety of applications in the areas of medicine, biology, data compression, etc. A significant benefit provided by Wavelet Transform is its capability to provide smoothing of the basic signal, and retention or even enhancement of the details4. The Wavelet Transform is a band pass filter with a known response function. The inverse filter may reconstruct the original time series. This filter is capable to remove amplitude regions at all frequencies. Due to this property, it can be used to remove noise, isolate single events or multiply events that present varying frequency, for example.

Summary Hydraulic fracturing is a widely applied well-stimulation technique. Recently, the application of multiple hydraulic fractures along horizontal wells has made possible the exploitation of unconventional reservoirs, such as shale gas and shale oil. However, the process of horizontal-well fracturing is uncertain and it is not fully understood yet. There exist many questions about the number and position of created fractures and whether the fracture stays contained within the main reservoir thickness. This work presents the modeling and analysis of temperature data during and after the creation of multiple fractures along a horizontal well. Our model accounts for fracture growth and closure, the well effects, and interaction between multiple fractures. The interference between multiple fractures growing simultaneously and the presence of reservoir-permeability heterogeneity were investigated. Capabilities and limitations of information carried by temperature data are presented through different geometry analyses. A special consideration of fracture growth out of zone was addressed. For this case, it was considered that one of the hydraulic fractures activates a pre-existing fault and interconnects the reservoir with other zones hundreds of feet away. Our work shows the advantage of the use of continuous-distributed-temperature data in comparison with the traditional single-point-pressure analysis. The local characteristic of temperature allows us to identify not only which of the created hydraulic fractures has interconnected the main reservoir with a different zone, but also the location of this zone. We also account for the effect of the interconnection on the growth of the other hydraulic fractures along the horizontal wellbore. The simultaneous multiple fracture growth shows that falloff-distributed-temperature data can predict the number of created fractures. More than that, in the presence of heterogeneity, the interaction between the contained fractures can be captured by the temperature analysis. Different from the pressure analysis, distributed-temperature data can differentiate between heterogeneity locations along the wellbore. Throughout this study, we present a series of examples in which the temperature can provide information that would not be obtained from the traditional pressure analysis.