Birol Dindoruk

University of Houston, Petroleum Engineering, Faculty Member

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Papers by Birol Dindoruk

Modeling of High Pressure and Temperature Microemulsion Experiments using HLD-NAC Based Equation of State

Surfactant flooding is a promising technique that can reduce interfacial tension (IFT) between oi... more Surfactant flooding is a promising technique that can reduce interfacial tension (IFT) between oil and water to ultra-low values, mobilizing previously trapped oil. For reservoirs at moderate to high pressures, understanding and modeling how pressure affects the phase behavior of a surfactant-brine-oil system is important to the design and implementation of an efficient/cost-effective surfactant flooding project. Typically, however, phase behavior experiments and models of that phase behavior are made only at low pressures. The main objective of this paper is to show how to model experimental data in a unified way for a large range of pressure, temperature, and other parameters, using hydrophilic-lipophilic deviation (HLD) and net-average curvature (NAC) based equation-of-state (EoS). Pressure and temperature scans show that pressure has a significant effect on the surfactant microemulsion phase behavior, shifting it from an optimal three-phase system at low pressure to a non-optimal two-phase system at high pressure. Further, multiple scans at different oil-water ratios show a shift in the optimum indicating that phase behavior partitioning of the various components is changing with oil saturation. We obtained good fits of all experimental data including all two and three-phase regions using a single tuned HLD-NAC EoS. This is the first attempt to model such complex phase behavior with a large variation in T, P, and compositions, which also demonstrates the type of data needed for an accurate EoS. When input into a numerical simulator, the tuned EoS improves the predictions of the resulting phase behavior (size and shape of the two-phase lobes and three-phase regions) and IFTs with changing pressure, temperature, salinity, oil-water ratios, and surfactant/alcohol concentrations.

Modeling Impact of Geochemical Reactions on Phase Behavior of Hydrocarbons during CO2 EOR

CO2 injection in oil reservoirs has the dual benefit of sequestration as well as enhancing oil re... more CO2 injection in oil reservoirs has the dual benefit of sequestration as well as enhancing oil recovery. Accordingly, efforts are underway to inject CO2 in carbonate reservoirs in the Middle East. The CO2 injected in carbonate reservoirs can react with ions present in the brine and the solid calcite in the carbonate rocks. These geochemical reactions impact the overall mole numbers and in some extreme cases, even the number of phases at equilibrium, thereby changing oil recovery predictions using compositional simulations. Hence, it is important to model and quantify the impact of geochemical reactions on the phase behavior of real oil samples. In this research, Gibbs free energy function is used to integrate phase behavior computations and geochemical reactions. The Gibbs free energy minimization method using an elemental balance constraint is used to obtain equilibrium compositions arising out of phase and chemical equilibrium. All reactions are assumed to be at equilibrium. The changes in the phase behavior of real oil samples during CO2 injection due to geochemical reactions are presented. The new phase behavior of real oil samples is used as input in compositional simulations to compare how oil recovery predictions are impacted. A Gibbs free energy framework for integration of phase equilibrium calculations as well as geochemical reactions has been developed. This approach is adaptable to different reservoir brines as well as hydrocarbons. The presented approach helps quantify the impact of geochemical reactions on the phase behavior of real oil samples. It also helps determine apriori, if geochemical reactions should even be considered during CO2 injection in reservoirs.

Case Study of Gas Injection and EOR Potential in Unconventional Marginal Permian Horizontal Wells

Quantification of the impact of pore pressure on relative permeability curves utilizing coreflood unit with gamma ray scanning capability

Geoenergy Science and Engineering, Aug 1, 2023

A Novel Approach to Combine Models to Evaluate Interwell Connectivity in a Waterflooded Reservoir with Limited Injection History

Waterflooding is one of the most widely implemented enhanced recovery in mature oil fields. In th... more Waterflooding is one of the most widely implemented enhanced recovery in mature oil fields. In the absence of a reliable reservoir model, waterflood optimization can be a challenge. The availability of continuous recording of production, injection and well data can be utilized to improve reservoir management in this novel approach. This study presents a new approach using Machine Learning (ML) technique through multiple signal analysis to optimize waterflood operation in a brownfield offshore Caspian Sea. To evaluate injection efficiency on oil production, firstly the interwell connectivity between injectors and producers are determined. However, because of the complexities associated with the reservoir and the data, it has been achieved through analyzing various available signal types which are informative and responsive to injection rates. Results obtained from multiple signals are then aggregated to identify the injector-producer pair connectivity. Next, production well performances are evaluated through multiple diagnostic models. Finally, the impact of injectors on oil production rates are analyzed and injector efficiencies are determined to establish a more efficient waterflooding strategy. The proposed methodology has been applied to a reservoir with around 50 producers and 7 injectors. The interwell connectivity between pairs have been identified and ranked. Using data analytics techniques on multiple surveillance data sets, the analysis of the waterflood is achieved more swiftly and accurately. It was observed that for this specific case, the most informative signals that help determine connectivity are the water cut, and water production rate. The identified injector-producer connections obtained from these models were further verified and compared well with additional available surveillance data on tracers for this reservoir. Understanding these leads to devising optimum waterflooding strategies such as diverting more injection water to the more efficient injectors and less injection water to the inefficient injectors. A novel multi-signal analysis using ML techniques is proposed that combines multiple data being collected as part of surveillance. The presented approach can be extended to similar waterfloods to help with optimizing the waterflooding strategy. This new approach helps with current digitization strategies in oil companies that seek to obtain faster and consistent solutions to accelerate decision making and as an alternative to cases especially where reservoir model is poorly defined.

Physics guided data-driven model to estimate minimum miscibility pressure (MMP) for hydrocarbon gases

Geoenergy Science and Engineering, May 1, 2023

Quantification of the Impact of Pore Pressure on Relative Permeability Curves Utilizing Automated Unit with Gamma Ray Scanning Capability

Day 3 Wed, October 05, 2022, Sep 26, 2022

Physics of multiphase flow in porous media heavily relies on the concept of relative permeability... more Physics of multiphase flow in porous media heavily relies on the concept of relative permeability. Moreover, relative permeability is an important input parameter for any numerical reservoir simulation representing multiphase flow in porous media. Relative permeability curves are also often used as tuning parameters to match the elements of the production history. Many times it is possible to see a single set of fixed relative permeability curves applied for the entire complex large-scale reservoir models. In this study, we are experimentally focusing on investigating the effect of high pressures on relative permeability curves. We are using a state-of-the-art custom-made relative permeability steady-state flow system with a gamma-ray source. The setup is capable of handling pressures from atmospheric values up to 10000 psi, and temperatures up to 200 °C. For this study we limit the fluids to a model oil and brine, such as n-hexane and sodium iodide aqueous solution. Selected porous media is a core cut from Berea sandstone rock. Core dimensions are 12 inch length and 1.5 inch diameter. Such choice of simple fluids and the rock is done to avoid any secondary effects of fluid-rock interactions, such as wettability alteration, asphaltenes, and gas-dissolution, so that we can clearly identify the impact of the pressure on the outcome. Moreover, by using simple fluid systems we avoid fluid-fluid interactions, miscibility and interaction of phase behavior and flow. We run the relative permeability scans at a fixed temperature (isotherm) and at several pressure values (isobars), such as 2000, 4000, 6000, and 8000 psi. The resulting relative permeability curves are then compared to each other to examine the impact of pressure. There are two main possible outcomes for this study. The first outcome is that there is no significant effect of pressure on relative permeability curves. Such an outcome confirms the generally practiced processes, where fixed relative permeability curves are used for the entire simulation study. The second possible outcome of the study is that there is a considerable effect of pressure on relative permeability curves. Such an outcome fundamentally questions the common assumption of pressure independent relative permeability curves that is broadly applied in the industry. Regardless of the two main outcomes of the study, both will contribute to a better understanding of the multiphase flow in porous media under high-pressure/variable pressure conditions. To be able to perform the analysis more diligently, we are also observing the in-situ phase saturations by scanning the core using gamma-ray. Such monitoring of the core, simultaneous with relative permeability measurements, will improve the quantification of the in-situ phase saturations at realistic conditions. Systematic analysis of the pressure effect on relative permeability is extremely scarce in the literature, even though the pressure varies significantly in the reservoir during the lifetime of the field. Therefore, it is essential to understand pressure effect on relative permeability under well controlled laboratory conditions. The outcomes of this paper may help engineers to improve their model predictions during field development and therefore decision-making processes.

Gas flooding: Gas Enhanced Oil Recovery (G-EOR) to CO2 sequestration

Elsevier eBooks, 2023

A New and Practical Oil Characterization Method for Thermal Projects: Application to Belridge Diatomite Steam Flood

All Days, Apr 19, 2013

Most of the oil-characterization approaches for thermal recovery are designed for heavy oils at m... more Most of the oil-characterization approaches for thermal recovery are designed for heavy oils at moderate temperatures, in which oil can be represented in very simplistic ways (such as "gas" and "oil"). However, when oil is exposed to very high steam temperatures (i.e., 550 F), and/or the oil is lighter than the classical range defined for heavy oils and is exposed to a wide spectrum of thermal effects, such as distillation of the lighter ends, the conventional methods of representing the interaction of steam and the in-situ fluids are not accurate. In many cases, we have to first evaluate the quality of the data, and then represent the average behavior with a single most likely fluid model per reservoir segment (plus other scenarios, as needed) to simulate the production performance. There is a need to develop a streamlined approach to bring such data into industrial simulators in a practical way. In this study, we have developed a fit-for-purpose approach to generate a consistent pressure/volume/temperature (PVT) model over the whole reservoir, reflecting both pressure and temperature changes through the entire oil accumulation. The model represents the oil viscosity for a wide spectrum of temperatures, from reservoir temperature to steam temperature (thermal-process range). A systematic lumping scheme enables conversion of the characterized PVT model for numerical simulators with the minimal number of pseudocomponents while still capturing the essence of thermal physics. To our knowledge, there is no systematic study of this nature available in the literature. We have tested this approach in the Belridge diatomite steamdrive project. The study confirmed that steamflood incremental oil production in a light-oil reservoir is sensitive to the componentlumping scheme because of distillation of the lighter ends. We also found that a five-component PVT model, representing the physics in "fit-for-purpose" dynamic simulation, best compromises between minimal number of components and physical description of the light-oil behavior.

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Interpretation of Hydraulic Fracturing Events by Analyzing the Energy of Rate and Pressure Signals

Hydraulic fracturing rate and pressure data are interrelated signals and can be subjected to sign... more Hydraulic fracturing rate and pressure data are interrelated signals and can be subjected to signal processing techniques. The energy of a range of frequency band can be computed from the wavelet transform, which is a powerful technique in signal processing. The signal energies of the rate (cause) and pressure (effect) are used to detect the fracture events in time, such as height growth, screen-out, and hydraulic fracture-natural fracture interactions. The objective of this study is to relate the signal energies obtained from the wavelet analysis to the real physical phenomena of fracturing propagation events during injection. With this technique, the fluid injection during hydraulic fracturing process may be monitored in real-time for early diagnosis and to take preventive steps during the fracturing treatmentWavelet transform decomposes a signal into various levels of frequency components. The transform can be repeatedly applied to obtain a multiresolution analysis of the signal. Such repeated application of the wavelet transform yields the signal detail coefficients at each decomposition level, where each level represents a band of frequency. Using this approach, the pressure and rate signals from hydraulic fracturing were decomposed. Then, the pressure and rate signals energies at each frequency band were computed and compared to using energy density plots (EDP). Since wavelet transform preserves the time localization, all events on the EDP are obtained with respect to time. Finally, the energy distribution of the frequency bands for pressure and rate signals of different decomposition levels and using different wavelet types were studied with respect to time to distinguish between the rock-related and rate-related events in time. The moving reference point (MRP) technique is used to verify the results of our proposed approach.Several field cases were analyzed in this study to show the robustness of the proposed approach. The events identified using the proposed approach were in good agreement with the established techniques in the literatures such as Nolte-Smith and moving reference point (MRP). The identified events were related to physical events happening during fracture propagation, such as height growth, the opening of the natural fracture system, screen-out, and proppant entering the formation. The main advantage of the developed methodology is its ability to identify fracturing events more accurately and earlier in time than other techniques.The novelty of this research is detecting the fracturing events in time independently of the assumptions of fracture and well geometry. Ultimately, this technique helps to improve treatment designs and efficiency by analyzing fracture and formation behavior of the treatment and enhancing decision-making during execution, by providing real-time indications of fracture behavior.

Reinfiltration in Fractured Porous Media: Part 2 - Two Dimensional Model

SPE Advanced Technology Series, Apr 1, 1994

The fractional flow equation in a two-dimensional space with mixed boundary conditions is solved,... more The fractional flow equation in a two-dimensional space with mixed boundary conditions is solved, and the expression for the reinfiltration rate across the faces of a matrix block is derived. The results show that there is a considerable variation in rate of reinfiltration across a vertical face of a matrix block. For a matrix block with an inclined face, the lower portion could drain liquid, but the upper portion could be under reinfiltration. The results also show that rein:filtration could be effective in a small area at the top portion of a matrix block.

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Development of miscibility in four-component vaporizing gas drives

Experimental and Numerical Study of Compositional Two-Phase Displacements in Layered Porous Media

All Days, Oct 9, 2011

This paper presents an experimental and numerical study to investigate the effects of viscous, ca... more This paper presents an experimental and numerical study to investigate the effects of viscous, capillary, and gravity forces on compositional two-phase displacements in layered porous media. We use glass bead packs of different sizes to construct a quasi 2-D porous medium of three layers with different uniform permeabilities. We adapt an analogue ternary fluid system (isooctane, brine, and isopropanol) which allows us to control interfacial tension between the phases at ambient laboratory conditions. Flooding experiments use immiscible and near miscible fluids using the analogue ternary system. We report recovery of phases, compositional analysis, and snapshots of saturation distributions during each experiment.We show that the high flow capacity domain as defined by the high permeability layer and its thickness always dominates displacements. However, its degree depends on the location of the high-permeability layer and the balance between the driving forces. Compositional analysis of effluent indicates that the compositional path is slightly different for the low and high injection rates in the high interfacial tension (IFT) immiscible displacements whereas it is sensitive to the location of high-permeability layer in the low-IFT near-miscible floods. The Peclet numbers indicate the presence of dispersion in the experiments and crossflow affects the recoveries.We also perform black oil simulations to validate the experimental observations. In general, we obtain consistent results between the experimentally measured and numerically obtained data. However, in order to account for the displacement physics and accurately predict the recovery performance, one may need to employ a fully compositional simulation model that captures the tie-line slopes correctly along with the transport properties and gravity. The compositional simulations can provide additional details in terms of component recoveries.

Subsurface Technology Pivoting from Oil and Gas to Geothermal Resources: A Sensitivity Study of Well Parameters

SPE production & operations, Feb 22, 2022

Summary There is enough geothermal energy several miles underneath to supply more power globally ... more Summary There is enough geothermal energy several miles underneath to supply more power globally than the entire oil and gas industry. Geothermal energy is renewable, abundant, and has a minimal carbon footprint. However, the current geothermal energy use is only 1% of the global energy production. To scale it up, several technical problems need to be solved, addressing how, where, and when to harness it safely, efficiently, and cost-effectively. In this paper, the synergies and differences between the hydrocarbon and geothermal wells are reviewed and an opportunity assessment and management framework are proposed to improve the outcomes of diverse geothermal hydrocarbon projects. Producing geothermal energy from existing hydrocarbon wells, as electricity and/or low-temperature waste heat, can yield significant advantages over traditional geothermal wells, especially in terms of reduced capital expenditure. Geothermal and hydrocarbon well studies related to geothermal hydrocarbon field development are reviewed, presenting the similarities and differences between the geothermal and hydrocarbon wells, and the challenges for economical geothermal hydrocarbon field development. To unlock the potential of geothermal hydrocarbon field development, several areas of research are identified and explored. There are many hydrocarbon wells drilled worldwide that are either abandoned or will be abandoned in the future. This inherently brings exit options repurposing these wells in terms of extracting residual value in an environmentally friendly way. There is a great potential to extract energy in the form of low-temperature waste heat and/or work with the right power fluids, including CO2. In this study, a numerical solution and several analytical solutions are presented for closed-loop wells (i.e., concentric pipes in wells). They can be extended to enhanced geothermal systems (EGSs) (i.e., injecting and producing casedhole wells with perforations along vertical, inclined, and horizontal sections) by allowing fluid inflow into the wells. The time- and space-dependent temperature solutions for all well configurations are obtained for time- and space-dependent fluid and flow properties. A sensitivity study is then performed, showing the effects of several parameters on the flowing fluid temperature for such parameters as the fluid flow rate, well length, inner tubing and annulus diameters, geothermal temperature, and overall heat transfer coefficients. Existing hydrocarbon fields produce larger volume of waste water over their lives, increasing their operating costs owing to greater water management expenditure. Conceptually, those operating costs could be reduced by electricity and/or heat coproduction. The proposed modeling approach is useful for the design of EGSs and closed-loop systems. It can also be used as an operational tool to monitor the well and reservoir performance over the life of a deep geothermal resource. Last but not least, it can be used to assess the conditions for economically converting abandoned oil and gas wells for sustainable geothermal energy production.

A Comparative Analysis of the Prediction of Gas Condensate Dew Point Pressure Using Advanced Machine Learning Algorithms

Dew point pressure (DPP) is a key variable that may be needed to predict the condensate to gas ra... more Dew point pressure (DPP) is a key variable that may be needed to predict the condensate to gas ratio behavior of a reservoir along with some production/completion related issues and calibrate/constrain the EOS models for integrated modeling. However, DPP is a challenging property in terms of its predictability. Recognizing the complexities, we present a state-of-the-art method for DPP prediction using advanced machine learning (ML) techniques. We compare the outcomes of our methodology with that of published empirical correlation-based approaches on two datasets with small sizes and different inputs. Our ML method noticeably outperforms the correlation-based predictors while also showing its flexibility and robustness even with small training datasets provided various classes of fluids are represented within the datasets. We have collected the condensate PVT data from public domain resources and GeoMark RFDBASE containing dew point pressure (the target variable), and the compositional data (mole percentage of each component), temperature, molecular weight (MW), MW and specific gravity (SG) of heptane plus as input variables. Using domain knowledge, before embarking the study, we have extensively checked the measurement quality and the outcomes using statistical techniques. We then apply advanced ML techniques to train predictive models with cross-validation to avoid overfitting the models to the small datasets. We compare our models against the best published DDP predictors with empirical correlation-based techniques. For fair comparisons, the correlation-based predictors are also trained using the underlying datasets. In order to improve the outcomes and using the generalized input data, pseudo-critical properties and artificial proxy features are also employed.

Theory of Gas Injection: Interaction of Phase Behavior and Flow

2015 AGU Fall Meeting, Dec 15, 2015

PVT Properties and Viscosity Correlations for Gulf of Mexico Oils

Proceedings of SPE Annual Technical Conference and Exhibition, Sep 1, 2001

New empirical pressure/volume/temperature (PVT) correlations for Gulf of Mexico (GOM) oils have b... more New empirical pressure/volume/temperature (PVT) correlations for Gulf of Mexico (GOM) oils have been developed as a function of commonly available field data. Correlations have been developed for the following: • Bubblepoint pressure. • Solution gas/oil ratio (GOR) at bubblepoint pressure. • Oil formation volume factor (FVF) at bubblepoint pressure. • Undersaturated isothermal oil compressibility. • Dead-oil viscosity. • Saturated-oil viscosity. • Undersaturated-oil viscosity. For the development of correlations, we have covered a wide range of data. More than 100 PVT reports from the GOM have been used in the development of correlations. Two of the published correlations (those of Standing 1 and Petrosky and Farshad 2) were tested with our GOM data set. Proposed correlations of this study predicted the PVT properties of GOM oils better than the correlations published in the literature, even when the coefficients of the published correlations are tuned. Using the correlations of this study, we have written a simple program that can generate PVT data sets for use in reservoir simulation. Laboratory-measured differentialliberation data for two different oil samples were also compared against our empirically generated differential-liberation data.

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Machine Learning Assisted Well Placement Optimization

Day 4 Thu, May 25, 2023

Well placement optimization is a complicated problem which is usually solved by direct combinatio... more Well placement optimization is a complicated problem which is usually solved by direct combination of reservoir simulators with optimization algorithm. However, depending on complexity of the reservoir model studied, thousands of simulations is usually needed for accurate and reliable results. In this research, we present a novel approach – machine learning (ML) assisted proxy model that combines reservoir simulations and reduced physics model to reduce computational cost. In the proposed model framework, first several (depending on the complexity of the problem) uniformly distributed random coordinates are selected. These chosen coordinates are considered as data points for ML model. For the chosen coordinates (training set) reservoir simulations are executed and NPV/recovery values are calculated (target variable). Spatial locations as well as petrophysical properties of the same coordinates extracted from simulation model are also used as an input to the ML model. ML model is fur...

A Comprehensive Analysis of Data Driven Techniques to Quantify Injector Producer Relationships

Day 3 Wed, March 15, 2023

Water flooding is an established method of secondary recovery to increase oil production in conve... more Water flooding is an established method of secondary recovery to increase oil production in conventional reservoirs. Analytical models such as capacitance resistance models (CRM) have been used to understand the connectivity between injectors and producers to drive optimization. However, these methods are not applicable to waterflood fields at the initial stage of life with limited data (less than 2 years of injection history). In this work, a novel approach is presented that combines analytics and machine learning to process data and hence quantify connectivity for optimization strategies. A combination of statistical (cross-correlation, mutual information) and machine learning (linear regression, random forest) methods are used to understand the relationship between measured injection and production data from wells. This workflow is first validated using synthetic simulation data with known reservoir heterogeneities as well as known connectivity between wells. Each of the four met...