Generating in-situ foam is regarded as one of the most promising techniques to overcome gas mobility issues and improve sweep efficiency in both miscible and immiscible gas injection enhanced oil recovery (EOR) processes. Gravity override, viscous fingering and channeling through permeable zones are the major limiting factors that can impair the efficiency of gas floods, mainly due to low density and viscosity of the gas relative to reservoir fluids. Generating strong and stable foam while injecting gas is one way to achieve in-depth conformance improvement in the reservoir.
In this study, a tailored water chemistry (formulated low salinity water) has been evaluated in comparison to using typical high salinity injection water (i.e. seawater) and deionized water in surfactant solutions to determine its overall effect on the produced foam. Using bulk foam tests, foam rheology apparatus and microfluidics device, the foam stabilization factors were analyzed and quantified by measuring the foam-life over time of different surfactants in varying salinity water solutions. In addition, the foam rheological properties were measured under high pressure. The microfluidics device was also used to examine the generated foam strength in porous media.
The results from laboratory experiments clearly demonstrated that the use of tailored water chemistry can improve the stability of produced foam when compared to both high salinity water and deionized water. Low salinity tailored water chemistry solutions resulted in a longer lasting foam, by almost 1.8-3.0 times depending on the surfactant type. The foam rheology results showed that the produced foams with the tailored low salinity water are of higher apparent viscosity when compared to those obtained with deionized water. Both longer foam-life and higher apparent viscosity are indicative of better, stronger and more stable foam. The higher resistance to gas flow was observed in porous media with foams generated using the low salinity tailored water chemistry solutions when compared to those foams obtained with deionized water and high salinity water.
This experimental study, for the first time, demonstrated substantial improvements in the foam stability by using a tailored water chemistry aqueous solution. Such huge foam stabilization improvements obtained with tailored water chemistry has the promising potential to increase the apparent viscosity of injected gas and subsequently more effectively mitigate gas mobility issues encountered in EOR applications.
Hydraulic fracturing and horizontal drilling are keys to unlocking unconventional hydrocarbon resources. The efficiency and success of hydraulic fracturing require solid understanding of fundamental physics involved with injection so controlling factors can be identified and optimized for completion and operation designs.
This paper describes and quantifies critical geomechanical, geological, and engineering variables for fracture initiation and propagation in horizontal wells. Formation breakdown pressure and net pressure are evaluated and compared with varying rock stresses, laminations, and perforations. Recognizing each unconventional formation has its own geomechanical and geological characteristics, this study highlights the importance of adapting engineering designs to accommodate the formation difference.
Through analytical, numerical, and case studies, this paper finds the following:
These findings help advance the fundamental understanding of the physics involved in hydraulic fracturing processes. Results and learnings can be applied to optimize completion and operation designs, minimize horsepower requirements, and improve stimulation efficiency.
Forecasting production performance for horizontal wells with multiple fractures in low to ultra-low permeability unconventional reservoirs is challenging due to the long duration of transient flow regimes. The widely-used Arps decline curve relationships were proposed for conventional reservoirs that quickly reach pseudosteady-state (or more appropriately, boundary-dominated flow). In this work we present conceptual decline curve models which conform to the long-term transient flow behavior observed in unconventional reservoirs. Validation is shown by matching production rate, loss ratios, and the b-factors.
Historically, decline curve models were derived based on observations of production, loss ratios, and b-factors. Specifically, we observed quadratic changes in loss ratio and power law changes in inverse loss ratio as the primary motivation in deriving these decline curve models. One of our proposed models is an extension of the existing stretched exponential model where the primary difference is that this extension accounts for curvature in the inverse loss ratio plot while the stretched exponential model is represented by a straight line on the inverse loss ratio plot. Detailed descriptions are provided for each model.
Using production data from the Barnett Shale, the proposed models are compared to existing models using statistical methods (i.e., total sum-of-squared deviations for production, loss ratios, and b-factors). Results show that the b-factor varies in time, as would generally be expected for transient flow. As a result, the constant b-factor assumption put forth by Arps for the traditional hyperbolic decline curve fails to properly match (and forecast) the production observed in low to ultra-low permeability reservoirs.
In contrast, our results show that the power-law based models conformvery well to production performance trends observed for unconventional gas wells. These power-law models include the stretched exponential and the proposed multi-segment or variable extension of the stretched exponential model. Through this work, it was found that an empirical understanding of the loss ratio and b-factor are essential in creating a robust decline curve. Any mischaracterization of the loss ratio behavior can result in inadequate matches of production, loss ratios, and b-factor; and result in erroneous production forecasts and inaccurate reserve estimates.
The novelty of the proposed decline curve models is in the simultaneous understanding of the production, loss ratio, and b-factor relationships for wells in unconventional reservoirs and the corresponding impact on reserve analysis. Specifically, the variable power-law model for the b-factor provides a unique level of flexibility in fitting production data.
The mechanical properties of kerogen, the organic constituent of shale source rocks, change as it becomes progressively buried under sediment over geologic time. While these changes are due to both mechanical and chemical mechanisms, the individual impact of these mechanisms is poorly understood. In this work, we use atomistic models to isolate how the elastic properties of kerogen are affected by one of these mechanisms: changes in density due to mechanical compaction. We use atomistic models of kerogen at four different maturity levels – immature, top of the oil window, middle-end of the oil window, and over-mature. At each maturity level, we construct representative kerogen structures at densities ranging from 0.9 gm/cm3 to 1.5 gm/cm3 using molecular dynamics simulations. Subsequently, the elastic moduli are calculated at 0 K, 300 K, and 500 K using molecular statics and molecular dynamics simulations.
Kerogen exhibits an amorphous structure with a short-range order up to 6 Å and no discernable long-range order. Increases in kerogen density upon burial are accommodated by proportional increases in the stacking of poly-aromatic islands present in its structure. We show that the increased stacking leads to the formation of π-π stacking bonds, which correlates to the increases in the elastic moduli. We also find that Poisson's ratio measured from atomistic simulations changes linearly with changes in density but is invariant to changes in chemical composition. For all of these properties, the values measured via simulation show good agreement with results from nano-indentation, atomic force microscopy (AFM), and ultrasonic measurements.
These results are useful for several reasons. First, they provide an estimate of Poisson's ratio for kerogen over a range of densities and maturities. This estimate is useful in AFM and nano-indentation experiments, where Poisson's ratio is difficult to measure but is needed to calculate Young's modulus from the reduced modulus. Second, the results demonstrate how atomistic modeling can be applied to gain new insight into the relationship between kerogen structure and its mechanical properties. Third, the agreement between the elastic moduli measured via simulation and experiment shows that atomistic methods can be utilized to accurately characterize kerogen, which is important for building accurate rock models for hydraulic fracturing simulation. Finally, the atomistic models of kerogen developed in this work, constrained by their mechanical properties, can be employed to study other processes such as crack propagation and surface adsorption.
Nuclear Magnetic Resonance (NMR) is a standard technique used in the oil and gas industry to determine porosity and fluid saturation for conventional reservoirs. However, in source rock reservoirs, NMR based interpretations of these properties are often uncertain due to the presence of complex multiphase fluids saturating pores that vary in size and surface wettability. To address this challenge, NMR signatures from individual matrix components, which make up the source rock are investigated with the purpose of gathering data to develop an improved estimate of producible hydrocarbons from logging data.
Quantitative bitumen extraction of Middle Eastern source rock samples using dichloromethane was conducted to provide the soluble component content of the organic matter. The remaining rock matrix was then acid digested to isolate and recover kerogen, the insoluble organic matter. Further separation of the kerogen fractions according to density differences due to the presence of pyrite was conducted using density liquid separation. These isolated components were then analyzed via low field lab NMR to investigate the individual contribution from each to the overall relaxation spectrum. Crushed rocks and core plugs from the same depth were also measured via NMR to obtain the surfaced samples total relaxation spectrum.
The NMR relaxation spectrum associated with the extracted bitumen, isolated kerogen, and crushed rocks were obtained and compared to develop a method to differentiate and quantify the amount of each component within the rock. The overall signature of the laboratory measurements shows influences from a combination of water and bitumen. These signatures allow for better identification of each component. This knowledge lends itself to quantifying and providing better estimates of water and oil saturations for unconventional reservoirs using NMR logging data.
Extracted bitumen and isolated kerogen are measured via low field NMR to determine the relaxation spectrum of each component. This is used to deconvolve the whole rock sample into the various components and can be further used in NMR log interpretation.
Water saturation and permeability are crucial petrophysical properties to evaluate unconventional reservoirs. However, there is no agreement on accurately estimating these properties from logs. Thus, there is a need to develop scale dependent petrophysical correlations to improve the estimation of these properties. As a result, this work aims to use digital rock properties from high-resolution images of unconventional carbonate mudrock samples to develop petrophysical correlations to improve water saturation and permeability estimates. Focused ion beam scanning electron microscopy (FIB-SEM) images were obtained from four carbonate mudrock samples from the Middle East and were segmented into the individual components: calcite, organic matter, pore space, and pyrite. Each image was subdivided into eight sub-sections to study scale dependence. Image analysis provided component details such as porosity, pore size distribution, connectivity, and geometric tortuosity. The impact of varying fluid saturation was investigated by introducing two fluid phases in the segmented pore space. Digital rock (DR) simulations were performed to estimate absolute permeability and electrical resistivity. The results from the DR methodology are discussed with reference to porosity and permeability data from the Gas Research Institute (GRI) method and a relative comparison to the log data is included. The DR results were used to develop petrophysical correlations to predict water saturation and permeability from electrical resistivity. The results show that a limited amount of pyrite and organic matter within a non-conductive calcite framework can change the electrical resistivity by several orders of magnitude. When combined with low porosity, high salinity water, and changing saturation, the results show that some variations in log responses may be attributed to the changing rock matrix and fabric as opposed to saturation. The permeability results also demonstrate that the low porosity, limited connectivity, and resulting tortuosity have a significant impact. These DR-guided correlations may improve the estimates of water saturation and permeability using resistivity logs.
Multiphase flow meters are available from sometime, however, there still remain unresolved challenges. Dependable flow sensing is essential for reservoir management and production optimization. Most commercial water-cut and multiphase flow meters have limitations while measuring over the full range of flow conditions. Exiting meters need recurrent calibration, and have significant capital and operational overheads. In this paper an ultrasonic tomography based meter for water holdup measurement is presented and the the experiences and challenges of testing the system in the field are shared. The designed system has the potential to resolve the shortcomings of available multiphase metering solutions.
This work is an ongoing effort to design a numerical platform based on machine learning algorithms to characterize, predict, optimize and guide the interaction of [high power] electromagnetic (HPEM) sources (laser, microwave, RF, etc.) with subsurface matter (e.g. rocks, oils, brines, etc.). Advanced statistical analysis routines are essential to identify key variables and relations in the thermal- mechanical-electromagnetic coupling in heterogeneous and anisotropic materials.
Advanced statistical analysis and machine learning have been recently used to evince relations in complex environments and physical dynamics; e.g. fluid dynamics, P&ID analytics, and drill cuttings classification, to cite a few. The methods make use of sophisticated algorithms to classify and model problems in multiple areas, from image processing to certain optimization problems. In the realm of subsurface photonics, and in particular for high power electromagnetic (HPEM) interaction with subsurface matter, these routines could become essential to identify key variables, assess the environment and process, and evince models to predict the outcome of an inherently multiphysics and multi-dimensional problem.
Numerical models that capture the interaction between HPEM sources and subsurface matter are essential to predict, optimize, adapt, and evaluate the process prior to, and during, deployment in subsurface. These models can come as the solution to a set of coupled partial differential equations that fully describe the physical dynamics, or as the result of supervised-learning algorithms and analysis of experimental and field data. The former is highly sensitive to dynamic material properties, environmental conditions, and source parameters. In addition, it can be challenging to characterize the properties of subsurface materials over the wide range of temperatures and pressures observed in the process. Thus, a machine learning method could provide an ever-improving alternative that learns from the available data to build a numerical platform that can predict, optimize, and guide the process.
Machine learning and advanced statistics provide a compelling alternative to build numerical tools to predict, optimize, and control physical processes. This work introduces a variety of numerical approaches to identify essential variables, predict their impact, and optimize the outcome for subsurface applications. Combined, the methods described in this work can help guide the control of the governing dynamics and parameters for use in multiple applications. This numerical platform can be extended to other applications, enhance experimental prototypes, and advance the design of a comprehensive numerical tool for downhole HPEM operations.
An advanced wellbore stability analysis software product has been developed in-house at Aramco. This product offers three analysis modules: (1) the classical mechanical module (elastic); (2) the time-dependent analysis module (poroelasticity); and (3) the time-dependent analysis of naturally fractured rock module (dual-porosity and dual permeability poroelasticity). The stress and pressure analyses are integrated with four rock failure criteria (Mohr-Coulomb, Drucker-Prager, Modified Lade, and Hoek-Brown) to calculate critical mud densities. The basic mechanical module is similar to the wellbore stability module provided in the most-frequently-used drilling geomechanics software. What sets this product apart from the others is that no commercial drilling software to date has the time-dependent stress and pressure analyses modeled by this product's poroelastic and dual-porosity poroelastic modules, which can capture real-time phenomena introduced by the time-dependent fluid pore pressure perturbation and the wellbore time-dependent failures in tension and/or compression.
Thomas, Gawain (Aramco Services Company) | Ow, Hooisweng (Aramco Services Company) | Chang, Sehoon (Aramco Services Company) | Shi, Rena (Aramco Services Company) | Wang, Wei (Aramco Services Company) | Chen, Hsieh (Aramco Services Company) | Poitzsch, Martin (Aramco Services Company) | Shateeb, Hussain (Saudi Aramco) | Abdel-Fattah, Amr (Saudi Aramco)
A novel barcoded advanced tracers system has been developed and field-validated in a hydrocarbon reservoir. With a detection scheme that supports automation, this cost-effective tracers system is designed to enable potentially ubiquitous long-term full-field injections in the interest of using the high fidelity tracer data to increase the oil recovery factor through improved optimization of the water injection and oil production.
Our Advanced Tracers system uses real-time chromatographic separation and optical detection to achieve ultra-low limits of detection (LOD) at 1 part per billion (ppb) or better. Such low LOD facilitates small injection quantities, making full field deployment of Advanced Tracers cost-effective compared to state-of-the-art interwell tracers. Additionally, the detection strategy that enables automation of the analysis process for real-time tracer detection is outlined, paving way for minimal manual sample collection and work-up.
Results on recently field-validated real-time optically-detectable tracers in a hydrocarbon reservoir, detectable at ultra-low LODs, are described. This is achieved even in the presence of background oil by means of an intrinsically oil-blind detection method. The material exhibits high mobility in high-salinity high-temperature carbonate reservoirs, with tracer breakthrough successfully detected at concentrations of less than 1 ppb using chromatographic separation followed by an optical detection method. The novel methodology as outlined highlights its simplicity, safety and cost-effectiveness relative to current practices in the field.
Fluorobenzoic acid based tracer technology requires chemical derivatization of the samples for ultra-low detection limits in a GC-MS. Our novel tracer detection strategy omits the need for chemical derivatization. In addition, it enables a compact, portable, optical-based automated wellsite detection system to be used in the field, with tens of unique barcodes possible. These innovative steps are keys to near real-time tracer detection in the field, realizing one of the intelligent oil field monitoring and the reservoir management applications.