Investigation of the permeability of carbonate rocks is essential and challenging due to the heterogeneity of carbonates at all scales. At the micro-scale, pore geometry, pore size distribution, and pore connectivity are important factors controlling permeability. This study focuses on the influence of pore size distribution and pore structure on permeability to better understand the fluid flow in carbonate rocks.
In this paper, we use micro-computer tomography (micro-CT) to capture the microscopic heterogeneity in the pore structure. Firstly, we collected seven 1 x 6 inch carbonate rock samples including Indiana Limestone, Desert Rose, and Travertine with various porosities and permeabilities. The porosity was measured gravimetrically, and permeability was measured with core plug flooding experiments. Cubic centimeter size core samples were scanned with enhanced micro-CT imaging with the resolution of 6-8 μm/voxel, then scanned 2D images were processed with image processing software to distinguish the pore system from the matrix. The pore size distribution for each rock sample was determined by fitting a statistical function based on the binarized images. We defined a concept of equivalent pore radius to characterize the pore system, which effectively filters out the non-contributing small pores and preserves the pores actually contributing to fluid flow. The relationship between the equivalent pore radius of each rock and permeability was investigated. Based on the 2D image stack, we also constructed the 3D pore network to observe the pore structure, quantify connectivity and specific surface ratio to study their influence on permeability.
We found that laboratory measured permeability from core plugs was strongly correlated to the equivalent pore radius calculated from micro-CT scanned images among the investigated carbonate rock samples. The semilogarithmic correlation between permeability and effective pore radius fit the measured permeability data very well over a permeability range of more than two orders of magnitude. The findings of pore-scale pore structure and pore size distribution in this study are helpful for carbonate rock analysis, and the proposed new correlation between equivalent pore radius and permeability is practical for permeability estimation for a wide range of carbonate rocks.
Rojas, Pedro A. Romero (Weatherford International) | Cristea, Alexandrina (Weatherford International) | Pavlakos, Paul (Weatherford International) | Ergündüz, Okan (ARAR AS) | Kececioglu, Tayfun (ARAR AS) | Alpay, Server Fatih (ARAR AS)
Nuclear magnetic resonance wireline logging and data post-processing technologies are continuously evolving, making significant contributions to rock, fluid typing, formation evaluation and characterization of the near-wellbore zone. In heavy oil fields, however, nuclear magnetic resonance (NMR) logging is known to provide an underestimated permeability, poor reliable oil typing and thus poor oil saturation and viscosity determinations, especially when the evaluation is based only on the spectra of transverse magnetic relaxation times (T2) (one-dimension NMR) [Romero et al., 2009]. Several attempts have been made to improve NMR results, mostly with limited success [Fang et al., 2004], especially in separating the oil component from the contribution of other fluids to the T2 spectra. The main reason lies not necessarily in the selection of the data acquisition parameters and sequences for a single-frequency or multi-frequency tool, but in the way how the data is post-processed.
The present study refers to a well drilled through the Derdere formation, a limestone/dolomite heavy oil reservoir in Turkey. The NMR data was acquired in with a centralized, single-frequency wireline tool in a 6-in. borehole, drilled with water-based mud in a freshwater carbonate reservoir. The generated T2 log was analyzed in a traditional way to obtain the NMR total porosity and its partitions based on standard cutoff values. For the given 12 API oil gravity, reservoir temperature (76 °C) and gas-oil-ratio (GOR) the T2Oil peak appears around 170 ms, right from the T2 cutoff for limestones; therefore, no corrections were needed on the permeability calculated from the Timur-Coates and Schlumberger-Doll-Research (SDR) equations. In the present well, only a diffused separation between oil and free water could be observed on the T2 distribution log from field data.
In the broader concept of Artificial Intelligence, the newly proposed post-processing steps to obtain the oil saturation start by deconvolving the T2 spectra, using blind source separation (BSS) based on independent component analysis (ICA) [Romero, 2016; Romero Rojas et al., 2018]. Based on its T2 peak value —the expected T2Oil peak response— calculated from the prejob planner/simulator, the deconvolution results show that one specific independent component corresponds to the oil, from which the oil saturation was determined.
Results demonstrated the usefulness of NMR logging technology in the characterization and evaluation of this reservoir. Data post-processing based on BBS-ICA enable adequate differentiation between fluid components from T2 spectra. For the reasons above, NMR has been proposed for additional wells in the same field.
The new-generation oil-base mud (OBM) microresistivity imagers provide photorealistic high-resolution quantified formation imaging. One of the existing interpretation methods is based on composite processing providing an apparent resistivity image largely free of the standoff effect. Another one is the inversion-based workflow, which is an alternative quantitative interpretation, providing a higher quality resistivity image, button standoff, and formation permittivities at two frequencies. In this work, a workflow based on artificial neural networks (NNs) is developed for quantitative interpretation of OBM imager data as an alternative to inversion-based workflow.
The machine learning approach aims to achieve at least the inversion-level quality in formation resistivity, permittivity, and standoff images an order of magnitude faster, making it suitable for implementation on automated interpretation services as well as integration with other machine learning based algorithms. The major challenge is the underdetermined problem since OBM imager provides only four measurements per button, and eight model parameters related to formation, mud properties, and standoff need to be predicted. The corresponding nonlinear regression problem was extensively studied to determine tool sensitivities and the combination of inputs required to predict each unknown parameter most accurately and robustly. This study led to the design of cascaded feed-forward neural networks, where one or more model parameters are predicted at each stage and then passed on to following steps in the workflow as inputs until all unknowns are accurately obtained.
Both inverted field data sets and synthetic data from finite-element electromagnetic modeling were used in multiple training scenarios. In the first strategy, field data from few buttons and existing inversion results were used to train a single NN to reproduce standoff and resistivity images for all other buttons. Although the generated images are comparable to images coming from inversion, the method is dependent on the availability of field data for variable mud properties, which at the moment limits the generalization of the NNs to diverse mud and formation properties.
In the second strategy, we utilized the synthetic responses from a finite element model (FEM) simulator for a wide range of standoffs, formation, and mud properties to develop a cascaded workflow, where each stage predicts one or more model parameters. Early stages of the workflow predict the mud properties from low formation resistivity data sections. NNs then feed the estimated mud angle and permittivities at two frequencies into next stages of the workflow to finally predict standoff, formation resistivity, and formation permittivities. Knowledge of measurement sensitivities was critical to design the efficient parameterization and robust cascaded neural networks not only due mathematically underdetermined nature of the problem but also the wide dynamic range of mud and formation properties variation and the measurements. Results for processed resistivity, standoff, and permittivity images are presented, demonstrating very good agreement and consistency with inversion-generated images. The combination of two strategies, training on both synthetic and field data, can lead to further improvement of robustness allowing customization of interpretation applications for specific formations, muds, or applications.
The initial high cost of exploitation of the sustained, increasingly growing development of unconventional resources in Argentina has resulted in concentrating all efforts to increase well productivity while reducing construction and completion costs. The optimization of hydraulic fracture (HF) treatments is vitally important. It is the primary strategy used to achieve an optimal reservoir drainage area, consequently characterizing the fracture geometry, including the height, for the continuous improvement of HF treatment and planning.
Several types of technologies and methodologies are used to estimate fracture height during and after a hydraulic stimulation treatment. These technologies can provide information about the fracture geometry and extension in the near-wellbore (NWB) and far-field areas. The determination of a reliable correlation between those methodologies represents a challenge as a result of formation complexity, heterogeneity, and limitations of evaluation technologies. It is well-known that some areas in the Vaca Muerta formation contain layers that can act as fracture barriers and are responsible for fracture containment.
This paper presents a fast and simple methodology that uses conventional well logs [gamma ray (GR), sonic, and density] from pilot wells to identify potential fracture barriers. This approach establishes a means to evaluate the degree to which the rock will have the ability to control fracture height growth. This methodology was determined useful for planning perforation intervals or clusters placement, particularly in those formations with stress profile showing reduced stress contrast and, when complemented with geological information, this method also provides useful information for horizontal well trajectory. Case studies are provided to illustrate examples of the proposed fracture barrier index (FBI) being calibrated or compared to other fracture height assessment. Additionally, the benefits of adding this new approach to current methodologies and technologies to aid completion design optimization and decision making is discussed.
Hwang, Jongsoo (The University of Texas at Austin) | Sharma, Mukul (The University of Texas at Austin) | Amaning, Kwarteng (Tullow Ghana Limited) | Singh, Arvinder (Tullow Ghana Limited) | Sathyamoorthy, Sekhar (Tullow Ghana Limited)
Understanding injectivity is a critical element to ensure that sufficient volumes of water are being injected into the reservoir to maintain reservoir pressure, to ensure good reservoir sweep and minimize well remediation. It is, however, challenging to describe the large injectivity changes that are sometimes observed in injectors operating under fracturing conditions. This study presents a field case study with the following objectives: 1) explain the complicated injectivity changes caused by fracture opening/closure with injection-rate variations, 2) define a safe operating envelope (for injection pressure and rate) that ensures fracture containment and injection into the target zone, and 3) prescribe how the injection rate should be changed to achieve higher injectivities. Injector operating conditions are developed using results from a full 3-dimensional fracture growth simulation to ensure fracture containment in a multi-layered reservoir.
We present field injectivity observations, a comprehensive simulation workflow and its results to explain injector performance in a deep-water turbidite sand reservoir with multiple splay sands. Understanding the impact on fracture propagation and containment allows us to make quantitative suggestions for the operating envelopes for long-term injection-production management. Strategies for high-rate injection to sustain the injection well performance long-term are discussed.
Simulation results show that, at injection rates over 5,000 bwpd, injection induced fractures propagate. Fracture closure induced by injection shut-down is used to compute the bottom-hole pressure decline as a function of time. The fracture opening/closure events and the thermally induced stress were the primary factors impacting injectivity. The simulation results suggested several ways to improve the injectivity while ensuring fracture containment. Injection under fracturing conditions into a single zone at a high rate is shown to be feasible and this allows us to support a substantial increase in injectivity. This must, however, be done at pressures that will not cause a breach in the bounding shales. The 3-dimensional fracture simulations identified the operating pressure and rate envelope to maximize the injection rates while minimizing the risk of breaching the cap rock and inter-zone shales.
As an enhanced oil recovery method (EOR), chemical flooding has been implemented intensively for some years. Low Salinity WaterFlooding (LSWF) is a method that has become increasingly attractive. The prediction of reservoir behaviour can be made through numerical simulations and greatly helps with field management decisions. Simulations can be costly to run however and also incur numerical errors. Historically, analytical solutions were developed for the flow equations for waterflooding conditions, particularly for non-communicating strata. These have not yet been extended to chemical flooding which we do here, particularly for LSWF. Dispersion effects within layers also affect these solutions and we include these in this work.
Using fractional flow theory, we derive a mathematical solution to the flow equations for a set of layers to predict fluid flow and solute transport. Analytical solutions tell us the location of the lead (formation) waterfront in each layer. Previously, we developed a correction to this to include the effects of numerical and physical dispersion, based on one dimensional models. We used a similar correction to predict the location of the second waterfront in each layer which is induced by the chemical's effect on mobility. In this work we show that in multiple non-communicating layers, material balance can be used to deduce the inter-layer relationships of the various fronts that form. This is based on similar analysis developed for waterflooding although the calculations are more complex because of the development of multiple fronts.
The result is a predictive tool that we compare to numerical simulations and the precision is very good. Layers with contrasting petrophysical properties and wettability are considered. We also investigate the relationship between the fractional flow, effective salinity range, salinity dispersion and salinity retardation.
This work allows us to predict fluids and solute behaviour in reservoirs with non-communicating strata without running a simulator. The recovery factor and vertical sweeping efficiency are also very predictable. This helps us to upscale LSWF by deriving pseudo relative permeability based on our extension of fractional flow and solute transport into such 2D systems.
Oil price forecasting has been shown to be challenging if not impossible for the long-term. However, the oil price has a major impact on Exploration and Production projects.
Historical Project Realized Oil Price (PROP) can be calculated for example projects by summing up the total project revenue using the actual oil prices and dividing through the total amount of oil produced. For different starting dates of example projects, the PROP changes. Determining the PROP for different starting times, a Cumulative Distribution Function (CDF) can be derived. Adjusting this CDF for expected "half cycle breakeven costs" for the low limit and demand considerations for the high case leads to a PROP range that can be used for future project evaluation.
Including PROP ranges into project evaluation allows for the selection of the most attractive development option, Value of Information analysis and project Probability of Economic Success (PES) calculation including oil price uncertainty.
Furthermore, using PROP ranges rather than oil price scenarios enables a distinction between short-term budget planning and long-term project development. For budget planning, a scenario approach is suggested while for long-term planning PROP ranges should be used. Applying long-term planning on PROP ranges leads to less fluctuation in staff planning and small annual adjustments in PROP range forecasting. Also, using PROP ranges results in increasing PES project hurdles at low oil prices and lower PES hurdles at high oil prices. Hence, at low oil prices the risk averseness of the company is increased. Another effect of using PROP ranges is that at high oil prices robustness of projects to low oil prices is included in the assessment.
To investigate the effect of PROP ranges on portfolio PES hurdles and project PES hurdles, a simplified linear-fit-model was developed. The results of the model showed that the project PES hurdles in a Value at Risk assessment can be determined applying the linear-fit-model to quantify the oil price dependency. The required individual project PES hurdles can be adjusted using the linear-fit-model to account for oil price uncertainty.
Bagheri, Mohammadreza (Research Centre for Fluid and Complex Systems, Coventry University) | Shariatipour, Seyed M. (Research Centre for Fluid and Complex Systems, Coventry University) | Ganjian, Eshmaiel (School of Energy, Construction and Environment, Built & Natural Environment Research Centre, Coventry University)
The fluid pressure, the stress due to the column of the cement in the annulus of oil and gas wells, and the radial pressure exerted on the cement sheath from the surrounding geological layers all affect the integrity of the cement sheath. This paper studies the impact of CO2-bearing fluids, coupled with the geomechanical alterations within the cement matrix on its integrity. These geochemical and geomechanical alterations within the cement matrix have been coupled to determine the cement lifespan. Two main scenarios including radial cracking and radial compaction, were assumed in order to investigate the behaviour of the cement matrix exposed to CO2-bearing fluids over long periods. If the radial pressure from the surrounding rocks on the cement matrix overcomes the strength of the degraded layers within the cement matrix, cement failure can be postponed, while on the other hand, high vertical stress on the cement matrix in the absence of a proper radial pressure can lead to a reduction in the cement lifespan. The radial cracking process generates local areas of high permeability around the outer face of the cement sheath. Our simulation results show at the shallower depths the cement matrices resist CO2-bearing fluids more and this delays exponentially the travel time of CO2-bearing fluids towards the Earth's surface. This is based on the evolution of CO2 gas from the aqueous phase due to the reduction in the fluid pressure at shallower depths, and consumption of CO2 in the reactions which occur at the deeper locations.
Application of polymer flooding as a chemical Enhanced Oil Recovery (EOR) has increased over recent years. The main type of polymer used is partially hydrolyzed polyacrylamide (HPAM). This polymer still has some challenges especially with shear stability and injectivity that restrict its utility, particularly for low permeability reservoirs. Injectivity limits the possible gain by acceleration in oil production due to polymer flooding. Hence, good polymer injectivity is a requirement for the success of the operation. This paper aims to investigate the influence of formation permeability on polymer flow in porous media.
In this study, a combination of core flooding with rheological studies is presented to evaluate the influence of permeability on polymer in-situ rheology behavior. The in-situ flow of HPAM polymers has also been studied for different molecular weights. The effect of polymer preconditioning prior to injection was studied through exposing polymer solutions to different extent of mechanical degradation.
Results from this study reveal that the expected shear thinning behavior of HPAM that is observed in rheometer measurements is not observed in in-situ rheology in porous media. Instead, HPAM in porous media exhibits near-Newtonian behavior at low flow rates representative of velocities deep in the reservoir, while exhibiting shear thickening behavior at high flow rates representative of velocities near wellbore region. The pressure build-up associated with shear thickening behavior during polymer injection is significantly higher than pressure differential during water injection. The extent of shear thickening is high during the injection of high Mw polymer regardless of cores' permeability. In low permeable Berea cores, shear thickening and mechanical degradation occur at lower velocities although the degree of shear thickening is lower in Berea to that observed in high permeable Bentheimer cores. This is ascribed to high polymer retention in Berea cores that results in high residual resistance factor (RRF). Results show that preshearing polymer before injection into porous media optimizes its injectability and transportability through porous media. The effect of preshearing becomes favorable for the injection of high Mw polymers into low permeability formation.
This study discusses polymer in-situ rheology and injectivity, which is a key issue in the design of polymer flood projects. The results provide beneficial information on optimizing polymer injectivity, in particular, for low permeability porous media.
Schumi, Bettina (OMV E&P) | Clemens, Torsten (OMV E&P) | Wegner, Jonas (HOT Microfluidics) | Ganzer, Leonhard (Clausthal University of Technology) | Kaiser, Anton (Clariant) | Hincapie, Rafael E. (OMV E&P) | Leitenmüller, Verena (Montan University Leoben)
Chemical Enhanced Oil Recovery leads to substantial incremental costs over waterflooding of oil reservoirs. Reservoirs containing oil with a high Total Acid Number (TAN) could be produced by injection of alkali. Alkali might lead to generation of soaps and emulsify the oil. However, the generated emulsions are not always stable.
Phase experiments are used to determine the initial amount of emulsions generated and their stability if measured over time. Based on the phase experiments, the minimum concentration of alkali can be determined and the concentration of alkali above which no significant increase in formation of initial emulsions is observed.
Micro-model experiments are performed to investigate the effects on pore scale. For injection of alkali into high TAN number oils, mobilization of residual oil after waterflooding is seen. The oil mobilization is due to breaking-up of oil ganglia or movement of elongated ganglia through the porous medium. As the oil is depleting in surface active components, residual oil saturation is left behind either as isolated ganglia or in down-gradient of grains.
Simultaneous injection of alkali and polymers leads to higher incremental oil production in the micro-models owing to larger pressure drops over the oil ganglia and more effective mobilization accordingly.
Core flood tests confirm the micro-model experiments and additional data are derived from these tests. Alkali co-solvent polymer injection leads to the highest incremental oil recovery of the chemical agents which is difficult to differentiate in micro-model experiments. The polymer adsorption is substantially reduced if alkali is injected with polymers compared with polymer injection only. The reason is the effect of the pH on the polymers. As in the micro-models, the incremental oil recovery is also higher for alkali polymer injection than with alkali injection only.
To evaluate the incremental operating costs of the chemical agents, Equivalent Utility Factors (EqUF) are calculated. The EqUF takes the costs of the various chemicals into account. The lowest EqUF and hence lowest chemical incremental OPEX are incurred by injection of Na2CO3, however, the highest incremental recovery factor is seen with alkali co-solvent polymer injection. It should be noted that the incremental oil recovery owing to macroscopic sweep efficiency improvement by polymer needs to be taken into account to assess the efficiency of the chemical agents.