Petrophysical analysis of downhole logs requires accurate knowledge of matrix properties, commonly referred to as matrix adjustments. In organic-rich shale, the presence of abundant kerogen (solid and insoluble sedimentary organic matter) has a disproportionate impact on matrix properties because kerogen is compositionally distinct from all inorganic minerals that comprise the remainder of the solid matrix. As a consequence, matrix properties can be highly sensitive to kerogen properties. Moreover, the response of many downhole logs to kerogen is similar to their response to fluids. Relevant kerogen properties must be accurately known to separate tool responses to kerogen (in the matrix volume) and fluids (in the pore volume), to arrive at accurate volumetric interpretations. Unfortunately, relevant petrophysical properties of kerogen are poorly known in general and nearly always unknown in the formation of interest.
A robust method of “thermal maturity-adjusted log interpretation” replaces these unknown or assumed kerogen properties with a consistent set of relevant properties specifically optimized for the organic shale of interest, derived from only a single estimate of thermal maturity of the kerogen. The method is founded on the study of more than 50 kerogens spanning eight major oil- and gas-producing sedimentary basins, 300 Ma of depositional age, and thermal maturity from immature to dry gas (vitrinite reflectance, Ro, ranges from 0.5 to 4%). The determined kerogen properties include measured chemical (C, H, N, S, O) composition and skeletal (grain) density, as well as computed nuclear properties of apparent log density, hydrogen index, thermal- and epithermal-neutron porosities, macroscopic thermal-neutron capture cross section, macroscopic fast-neutron elastic scattering cross section, and photoelectric factor. For kerogens relevant to the petroleum industry (i.e., type II kerogen with thermal maturity ranging from early oil to dry gas), it is demonstrated that petrophysical properties are controlled mainly by thermal maturity, with no observable differences between sedimentary basins. As a result, universal curves are established relating kerogen properties to thermal maturity of the kerogen, and the curves apply equally well in all studied shale plays. Sensitivity calculations and field examples demonstrate the importance of using a consistent set of accurate kerogen properties in downhole log analysis. Thermal maturity-adjusted log interpretation provides a robust estimate of these properties, enabling more accurate and confident interpretation of porosity, saturation, and hydrocarbon in place in organic-rich shales.
Zhou, Xu (Louisiana State Unviersity) | Tyagi, Mayank (Louisiana State Unviersity) | Zhang, Guoyin (China University of Petroleum - Beijing) | Yu, Hao (Southwest Petroleum University) | Chen, Yangkang (Zhejiang University)
With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the statistical relationships between seismic attribute values from a 3D seismic survey and petrophysical properties from well logs. Such relationships and models can be further used for the optimization of exploration and production operations.
3D seismic data can be used to extract various seismic attribute values at all locations within the seismic survey. Well logs provide accurate constraints on the petrophysical values along the wellbore. Big Data Analytics methods are utilized to establish the statistical relationships between seismic attributes and petrophysical data. Since seismic data are at the reservoir scale and are available at every sample cell of the seismic survey, these relationships can be used to estimate the petrophysical properties at all locations inside the seismic survey.
In this study, the Teapot dome 3D seismic survey is selected to extract seismic attribute values. A set of instantaneous seismic attributes, including curvature, instantaneous phase, and trace envelope, are extracted from the 3D seismic volume. Deep Learning Neural Network models are created to establish the relationships between the input seismic attribute values from the seismic survey and petrophysical properties from well logs. Results show that a Deep Learning Neural Network model with multi-hidden layers is capable of predicting porosity values using extracted seismic attribute values from 3D seismic volumes. Ultilization of a subset of seismic attributes improves the model performance in predicting porosity values from seismic data.
Quintero, Harvey (ChemTerra Innovation) | Farion, Grant (Trican Well Service LTD.) | Gardener, David (ChemTerra Innovation) | O'Neil, Bill (ChemTerra Innovation) | Hawkes, Robert (Trican Well Service LTD.) | Wang, Chuan (ChemTerra Innovation) | Cisternas, Pablo (American Air Liquide) | Pruvot, Antoine (American Air Liquide) | McAndrew, James (American Air Liquide) | Tsuber, Leo (Badger Mining Corporation)
This study aims to demonstrate the true benefit of an innovative salt tolerant high viscosity friction reducer (HVFR) that excels at promoting extended proppant suspension and vertical distribution into the fracture when it is used as a base fluid for the Capillary Bridge Slurry (CBS) and other conventional fracturing fluid systems in combination with nitrogen.
The completion of super-lateral wells now being drilled in tight oil and gas shales in North America, with record lengths close to 4 miles, demand for greater carrying capability of low viscosity (slickwater) fracturing fluids, where significant sand settling can occur before the proppant even reaches the fractures. This has sparked recent interest in the development and application of salt tolerant polyacrylamide-based friction reducers, referred to as High Viscous Friction Reducers (HVFR). The downfall of these first generation HVFR's is the lack of compatibility with high salinity brines such as recycled and flowback water, and diminished ability to reduce friction pressure during hydraulic fracturing treatments when compared to industry standard FR's.
Herein, we report the field application of a unique salt tolerant HVFR (HVFR-ST), that consistently provides higher viscosity values (corresponding industry standard HVFR loading comparison) when tested in brines, without sacrificing friction reduction effectiveness. Additionally, a new concept of fracturing fluid referred to as Capillary Bridge Slurry (CBS) has been successfully implemented in North America, where through a surface modification to the proppant, the addition of a gas phase such as N2, and the use of a polyacrylamide-based friction reducer, the proppant becomes part of the fluid structure and is no longer the burden to be carried. The combination of HVFR's and the surface modified proppant can effectively combat the issues faced with proppant transport in long laterals.
This paper will highlight the results on the analysis of the governing proppant transport mechanisms (suspended and bed) of CBS system formulated with HVFR-ST, in the presence of nitrogen (N2), where no detrimental effect in the average distance traveled of the sand particle in the Proppant Transport Test Bench (PTTB) was observed when the brine concentration of the base fluid was increased from 1% to 5% in comparison to industry standard HVFR (HVFR-FW).
Field production data on wells stimulated with CBS show a significant upside (~ 50%) in liquid hydrocarbon production than offsetting wells over a ~ one year period of time.
Friction loop data carried out at 45 L/min (11.89 gals/min) flow rate in an internal diameter pipe of 0.305" shows a reduction on friction pressure in excess of 70%, when HVFR was tested in 5% API brine (4% (w/v) NaCl and 1% (w/v) CaCl2·2H2O) at loadings as low as 0.1%. Furthermore, dynamic measurements within the viscoelastic regime/behavior of the HVFR at different loadings in the oscillatory viscometer will provide learnings on the elasticity-proppant transport relationship of the different fracturing fluid systems.
Through the use of laboratory testing and field study cases, this paper will illustrate the true benefits on the use of salt tolerant HVFR's as a base fluid with the increasing demand of re-cycled and flowback water use in fracturing fluid systems.
Initial rate and decline are the two main parameters defining the economics of unconventional shale oil development. To improve economics, companies drill longer horizontal wells with more than twenty equidistant stages, different completion strategies and various additives such as surfactants and nano surfactants. This procedure evolves to factory mode in which tasks are optimized in timing and performance without attention to the matrix aspects of improving the recovery. Here, we report the design of a mutual solvent injection pilot in the Vaca Muerta unconventional reservoir during the completion of four unconventional shale oil wells. Reducing
Vaca Muerta has been long regarded as a water wet shale because of the limited water backflow post-fracking job. Alternating water injection was implementing assuming that the well productivity is driven by spontaneous imbibition, but this strategy has been unsuccessful as capillary pressure hysteresis drives this mechanism. We started studying Vaca Muerta from the rock microstructure using energy-dispersive spectrometry and focused gallium Ion Beam ablation FIB SEM images. The microstructure varied widely from millimeters in the same plug which could be expected because in shale rocks millimeters represent more years of deposition than in a conventional reservoir. We identified intercalations of massive water wet zones and strongly oil wet zones in the Vaca Muerta kitchen zone. The oil wet intercalations have high porosity and adsorption isotherm indicating 100 to 1000 times more permeability than the water wet zone. The water wet intercalations are highly saturated with water, and on the contrary, the oil wet intercalations are highly saturated with oil. The pilot designed consisted of four wells in which we will test different injection concentrations but keeping the total mass constant. In this manner, we will estimate the volume contacted by the solvent.
The laboratory protocol indicates a large percentage of macro and meso-pores. We implemented the dimethyl-ether injection which changes the interfacial tension, viscosity and wettability and we obtained the modified relative permeabilities which were the injection of dimethyl ether at 30% concentration along with the hydraulic fracture stimulation stages doubled the initial oil production rate.
The pilot consisted of five wells in which we will test different injection concentrations but keeping the total mass constant. In this manner, using the numerical simulation, we will estimate the volume contacted by the solvent.
Intercalations of high porosity high permeabilities zones in which the injection of a mutual solvent that reduces viscosity and could change wettability in oil wet/water-wet Vaca Muerta improving matrix connectivity.
Cui, Xiaona (Texas A&M University and Northeast Petroleum University) | Song, Kaoping (China University of Petroleum - Beijing) | Yang, Erlong (Northeast Petroleum University) | Jin, Tianying (Texas A&M University) | Huang, Jingwei (Texas A&M University) | Killough, John (Texas A&M University) | Dong, Chi (Northeast Petroleum University)
The phase behavior shifts of hydrocarbons confined in nanopores have been extensively verified with experiments and molecular dynamics simulations. However, the impact of confinement on large-scale reservoir production is not fully understood. This work is to put forward a valid method to upscale the pore-scale fluid thermodynamic properties to the reservoir-scale and then incorporate it into our in-house compositional simulator to examine the effect of confinement on shale reservoir production.
Firstly, a pore-scale fluid phase behavior model is developed in terms of the pore type and pore size distribution (PSD) in the organic-rich shale reservoir using our modified Peng-Robinson equation of state (PR-C EOS) which is dependent on the size-ratio of fluid molecule dynamic diameter and the pore diameter. And the fluid composition distribution and PVT relation of fluids in each pore can be determined as the thermodynamic equilibria are achieved in the whole system. Results show that the initial fluid composition distribution is not uniform for different pore types and pore sizes. Due to the effect of confinement, heavier components are retained in the macropore, and lighter components are more liable to accumulate in the confined nanopores. Then an upscaled equation of state is put forward to model the fluid phase behavior at the reservoir-scale based on our modified PR-C EOS using a pore volume-weighted average method. This upscaled EOS is validated with the pore-scale fluid phase behavior simulation results and can be used for compositional simulation. Finally, two different reservoir fluids from the Eagle Ford organic-rich shale reservoir are simulated using our in-house compositional simulator to investigate the effect of confinement on production. In addition to the critical property shift which can be described by our upscaled PR-C EOS, capillary pressure is also taken into account into the compositional simulation. Results show that the capillary pressure has different effects on production in terms of the fluid type, leading to a lower producing Gas/Oil ratio (GOR) for black oil and a higher GOR for gas condensate. Critical property shift has a consistent effect on both the black oil and gas condensate, resulting in a lower GOR. It should be noted that the effect of capillary pressure on production is suppressed for both fluids with the shifted critical property.
With the recent tremendous development in algorithms, computations power and availability of the enormous amount of data, the implementation of machine learning approach has spurred the interest in oil and gas industry and brings the data science and analytics into the forefront of our future energy. The idea of using automated algorithms to determine the rock facies is not new. However, the recent advancement in machine learning methods encourages to further research and revisit the supervised classification tasks, discuss the methodological limits and further improve machine learning approach and classification algorithms in rock facies classification from well-logging measurements. This paper demonstrates training different machine learning algorithms to classify and predict the geological facies using well logs data. Previous and recent research was done using supervised learning to predict the geological facies.
This paper compares the results from the supervised learning algorithms, unsupervised learning algorithms as well as a neural network machine learning algorithm. We further propose an integrated approach to dataset processing and feature selection. The well logs data used in this paper are for wells in the Anadarko Basin, Kansas. The dataset is divided into training, testing and evaluating wells used for testing the model. The objective is to evaluate the algorithms and limitations of each algorithm. We speculate that a simple supervised learning algorithm can yield score higher than neural network algorithm depending on the model parameter selected. Analysis for the parameter selection was done for all the models, and the optimum parameter was used for the corresponding classifier.
Our proposed neural network algorithm results score slightly higher than the supervised learning classifiers when evaluated with the cross-validation test data. It is concluded that it is important to calculate the accuracy within the adjacent layers as there are no definite boundaries between the layers. Our results indicate that calculating the accuracy of prediction with taking account the adjacent layers, yield higher accuracy than calculating accuracy within each point. The proposed feed-forward neural network classifier trains using backpropagation (gradient descent) provides accuracy within adjacent layers of 88%. Our integrated approach of data processing along with the neural network classifier provides more satisfactory results for the classification and prediction problem. Our finding indicates that utilizing simple supervised learning with an optimum model parameter yield comparable scores as a complex neural network classifier.
In this study, we propose a new method for estimating average fracture compressibility during flowback process, and apply it on flowback data from thirty multi-fractured horizontal wells completed in Eagle Ford, Horn River, Montney and Woodford formations. We conduct complementary diagnostic flow regime analyses and calculate by combining a flowing material balance equation with rate-decline analysis. We observe two production signatures during flowback: (1) single-phase water production followed by hydrocarbon breakthrough and (2) immediate production of hydrocarbon with water. Water rate-normalizedpressure plots show pronounced unit slopes, suggesting pseudo-steady state flow. Water decline curves follow a harmonic trend during multiphase flow; from which we forecasted ultimate water production as an estimate of initial fracture volume.
Fu, Xuebing (Goolsby Finley & Associates) | Bonifas, Paul (Goolsby Finley & Associates) | Finley, Andy (Goolsby Finley & Associates) | Lemaster, Julia (Goolsby Finley & Associates) | He, Zhiyong (ZetaWare, Inc) | Venepalli, Kiran (CMG Inc.)
Over the last decade, tight oil production has become significant with the success of horizontal drilling and hydraulic fracturing. However, the recovery factor of tight oil production remains very low and no standard secondary recovery method exists after primary depletion. We propose a new secondary recovery method: to use existing hydraulic fractures (every other fracture) in a horizontal well as gas injection and oil production sites to conduct
An ideal process of this method envisions a horizontal well centered in an enclosed reservoir, where the hydraulic fractures are evenly distributed along the well, parallel to each other. If the hydraulic fractures can be effectively isolated, and injection and production can be conducted through alternate fractures at the wellbore, then highly efficient flooding patterns can be created. Key questions include: Is there adequate injectivity and productivity in a sub-microdarcy reservoir? How far are the ideal reservoir conditions from reality? How difficult is it to isolate individual fractures within the wellbore?
Is there adequate injectivity and productivity in a sub-microdarcy reservoir?
How far are the ideal reservoir conditions from reality?
How difficult is it to isolate individual fractures within the wellbore?
In addressing these key questions, first, the success of a flooding process relies on reasonable injectivity and response time between the injector and producer – in this case the injector being one hydraulic fracture and the producer being an adjacent hydraulic fracture; both economical rates and reasonable communication time between adjacent fractures are demonstrated through analytical calculations and reservoir simulations in a typical well setting; nearly 100% recovery is achieved in the reservoir units between the fractures in a miscible flooding process. Second, actual reservoir conditions are incorporated in our study, focusing on direct fracture communications; the effects are demonstrated, and comparisons among different methods are made. Finally, potential challenges in operations are summarized and current technologies are reviewed; the gaps between the current settings and the required settings are demonstrated. Economic discussions are made, indicating positive scenarios with large tolerances.
With the rapid development of tight oil reservoirs, Enhanced Oil Recovery (EOR) technologies are urgently required to improve the recovery factor beyond primary depletion. An effective flooding process may be conducted if the hydraulic fractures can be used as injection and production ports. As a first attempt to envision an inter-fracture flooding process, key aspects are defined and examined, showing promising results. Inter-fracture gas flooding may become a standard secondary recovery technique for tight oil reservoirs and add significant reserves.
For years, an unresolved question for those drilling horizontal wells has been: Does it matter if it is toe-up or toe-down? Horizontal wells generally follow an up or down slope following the most productive rock with some large changes in elevation from the heel--the curve from vertical to horizontal--to the toe. Results from modeling did not settle the matter. Recently, Devon Energy offered its response based on the performance of more than 300 similar wells drilled in the Cana-Woodford Shale in Oklahoma. The comparison was possible because the company had mass-produced similar wells, giving it a large sample of wells to study with 4,800-ft laterals where it fractured 10 stages with 40 perforation clusters using 3.5 million lb of proppant located in a compact area with similar geology in the three depths studied.
Electromagnetic images can show where water flows during a hydraulic fracture. A test in the Anadarko Basin showed a fault there was a bigger hazard than expected. Electromagnetic (EM) reservoir imaging is likely to get more attention from operators thanks to a collaboration between Halliburton and a leader in this emerging technology, GroundMetrics.