1 - 3 / 3
ÖgePenetration rate optimization in heterogeneous formations with support vector machines method(Lisansüstü Eğitim Enstitüsü, 2021)The exploration of petroleum and natural gas resources is at topmost importance because of the unending demand for energy resources. Due to the oil companies' cost-efficient policy, the importance of reducing cost and increasing performance has ascended. Accordingly, there have been significant advances in drilling technology. Nowadays, cost and performance can be optimized thoroughly by using developing technology and computer science. The primary optimization problem in oil and gas exploration has always been related to minimize costs due to market volatility related to the decreasing oil prices. Drilling operations constitute a significant part of the exploration costs. Thus, the main objective of cost optimization is to reduce total well costs. One way to achieve this goal is to optimize or maximize the rate of penetration (ROP). The ROP starts to decrease as a drill bit wears within a run that cause additional cost. Practically, if the lithology is homogeneous, the optimum ROP can be achieved by adjusting controllable drilling parameters and considering the drilling cost starts to increase after a minimum value. On the other hand, this approach usually fails when drilling through complex lithological formations known as a heterogeneous environment distinctly non-uniform in lithological composition. There is no widely accepted model for defining the optimum ROP since various variables affect the cost and time relationship. It is clear that maximizing ROP or minimizing drilling costs is a multi-parameter optimization problem that requires optimization techniques. There exist some mathematical and statistical models to optimize ROP and various drilling parameters. The most commonly used ROP model in the drilling industry is Bourgoyne and Young Method (BYM). In BYM, the aim is to calculate eight regression coefficients related to standard drilling parameters by applying multiple linear regression (MLR) analysis to drilling data for predicting ROP. The BYM involves eight drilling parameters and requires statistically at least thirty inputs obtained from the formations with uniform lithology. The number of input data required to obtain such a large number of parameters may not always be possible. This is especially the case in drilling environments where there are not many shale zones, and where there are few wells in a field, or where complex lithology is dominant such as in the fields of Turkey. Thus, the results obtained by using the BYM in these limited conditions are meaningless, observations obtained from the field practice, and method results give very different values. On the other hand, the support vector machines (SVM) method effectively predicts ROP with higher sensitivity and conservation without decreasing the number of parameters. The main purpose of this study is to implement a different type of regression model as an alternative to multiple linear regression by modifying BYM by introducing several geomechanical parameters to estimate a relationship between the rock mechanical properties and the heterogeneity, which are shale index, uniaxial compressive strength, brittleness index, shear failure gradient, and torque. In this study, several predictive methods are used to build a model for ROP prediction: MLR, support vector regression (SVR), and artificial neural networks (ANN). Feature selection is performed via random forests method. The prediction accuracy of each predictive model is compared by using several statistical comparison criteria: root mean square error (RMSE), correlation coefficient, and statistical significance (p-value). The main focus of the applications in this study is to perform rate of penetration (ROP) predictions in heterogeneous formations. With a field data taken recently, it is possible to show the prediction performances of MLR, SVR, and ANN on the dataset taken from heterogeneous lithology. Since data quality is one of the essential process of data analysis. Hence, data cleansing and data selection are made precisely while constructing the cases and the datasets. In the results, it is clearly shown that BYM performs well in terms of ROP prediction in homogeneous formations. In heterogeneous formations, SVR with radial basis function (RBF) kernels gives better prediction results in terms of RMSE. On the contrary, for the homogeneous formations, linear models produce lower errors. ANN also performs well in heterogeneous formations in terms of ROP prediction. Adding some extra features related to the rock mechanics have different effects on prediction performances for all models. Torque is found as the key parameter to define the relationship between ROP and heterogeneity. Depth is another important parameter on ROP prediction in heterogeneous formations. Moreover, feature selection based on the random forests algorithm is applied. The cut-off value is found as formation-specific. Hence, it is stated that feature selection should be performed for each data set exclusively. The studies' findings in this dissertation are expected to provide significant time and cost savings since it is expected that faster and more accurate results will be achieved than those of currently available methods. The results will provide a basis for monitoring the condition of a drill bit on a real-time basis during any drilling operations and determining the best time to change the drill bit while avoiding a possible increase in the overall drilling cost.
ÖgeIdentifying the locations of observation wells for various boundary conditions for geothermal reservoirs(Lisansüstü Eğitim Enstitüsü, 2021)In the last two decades, the importance of renewable energy sources has increased due to global warming and environmental concerns. Having suitable geological conditions, Turkey's geothermal potential is high. In the last sixteen years, with new investments, the installed geothermal capacity of Turkey greatly increased. In the near future, geothermal energy can contribute more to the domestic energy needs and could be an important alternative energy resource for our country. In geothermal projects, optimum reservoir management is one of the most important aspects of sustainable production. It affects the project's economy and the sustainability of the resources. In order to manage the reservoir optimally, future predictions have to be made by using reservoir simulation techniques. In reservoir simulation, the volumetric average reservoir pressure is one of the most important data that can be obtained from the reservoir. One can use well tests or observation wells to determine the volumetric average reservoir pressure. This study aims to contribute to answering the best possible location for the observation well that represents the average reservoir pressure. The pressures observed from the observation well can then be used for reservoir engineering practices. In case the observation well location does not represent the average reservoir pressure behavior, reservoir engineering practices may lead to unreliable future performance predictions. Thus, placing an observation well at a place where actual volumetric average reservoir pressure can be recorded is crucial for the economic development of the geothermal reservoir. In this thesis, a numerical work is conducted to study the proper location of an observation well to be selected for different boundary conditions and various well configurations. In all examples a square-shaped reservoir is assumed and no-flow or constant pressure outer boundaries are used. The cases studied usually have either one production or reinjection well or a pair of them. Even though the results obtained in the studied cases show results from a single well or a pair of wells, they can be used to generalize to production and reinjection regions in a field. A production well can represent a production region (where all or most of the production wells in the system are located) or an injection well might represent an injection region. The results of this study provide decision maps for various boundary and well configurations from which the engineer may be able to determine qualitatively where to place the observation well. It is determined that in the case where there is a high reinjection ratio (most of the produced fluid is reinjected back into the reservoir) the effects of the outer boundary on the location of the observation well becomes negligible. The location of the observation well in such cases depends mostly on the configuration of how the production and reinjection wells are placed. Furthermore, it should also be noted that the location of an observation well may change depending on how the locations of production and reinjection wells change over time, and also on the production and reinjection amounts. It is important to note that the results obtained from this study should be interpreted qualitatively. A study that provides the proper locations of an observation well does not exist in the geothermal engineering literature. The original contribution of this study is to fill this gap which could be very important.
ÖgeNumerical simulation of transient sandface and wellbore temperature behaviors of wells in multilayer single-phase oil and geothermal reservoirs(Lisansüstü Eğitim Enstitüsü, 2022)The interpretation of dynamic temperature data acquired during well tests and distributed temperature sensors (DTS) has grown increasingly in the last decade. While research studies are ordinarily based on sandface solutions, actual field measurements are made in the wellbore, generally at a certain distance above the sandface for conventional well tests. There is still a need for further fundamental studies to emphasize the apparent differences between sandface and wellbore temperature responses especially when it comes to history matching and production optimization applications. The objective of this study is to develop and present applications of a two-dimensional (2-D) r-z, fully implicit, single-phase non-isothermal, transient coupled reservoir/wellbore model with a single well located at the center of a cylindrical reservoir. The model accounts for the Joule-Thomson (J-T), isentropic expansion, conduction and convection effects for predicting the transient temperature behavior and computing the wellbore temperature at different gauge depths. In this study, single phase fluid flow of oil or geothermal brine from a fully penetrating vertical or inclined well in an infinite-acting homogeneous reservoir is modeled. The coupled simulator solves mass, momentum, and energy conservation equations simultaneously for both reservoir and wellbore. The functional iteration procedure is used that updates fluid properties based on available correlations as a function of pressure and temperature at a given time step. Comparisons of the developed model for several syntetic cases with a commercial simulator are provided. We identify diagnostic characteristics of temperature transients at gauge locations at the sandface and above the sandface that may arise during a well test, we examine the sensitivity of the model parameters appearing in the coupled non-isothermal reservoir/wellbore model through synthetically generated test data sets and history matched field application. The drawdown and buildup sandface transient temperature data are obtained from the coupled model and used to interpret and analyze temperature transients. In addition to the J-T coefficient of fluid, we show that history matching transient temperature data provides estimates for the skin zone radius and permeability when analyzed jointly with the conventional pressure test analysis (PTA). An investigation on the effect of gauge location on temperature data shows that the early-time response is influenced by the wellbore phenomena while the J-T effects are clearly identified at later times at typical gauge locations up to 100 m above the top of the producing horizon (refers to total pay zone). Logarithmic time derivative of temperature transients is found as a useful diagnostic tool to differentiate the wellbore phenomena from the reservoir response. It is also shown that the temperature transient is more reflective of the properties of the near wellbore region (e.g., skin zone) than the pressure transient. For this reason, analyzing temperature transients together with the pressure transients could add more value to the analysis to better examine near wellbore characteristics. A comprehensive sensitivity study conducted for multi-layer systems by constructing a 2-D (r-z) coupled model indicates beneficial remarks on PLT data. We provide well profile outputs of pressure, temperature, and flow distributions along the wellbore to identify most influential parameters, such as the layer petrophysical properties and the layer thermal parameters. Several examples of regression on temperature and pressure from multi-layer systems are considered for demonstrating the utility of the developed simulator. Due to high number of parameters involved in multi-layer systems, a robust characterization on thermal and rock properties is required to be able to achieve a realistic regression on temperature profiles to compute inflow rates of individual layers.