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.
In the world of downhole sealing technology, there have been relatively few new developments in recent years. Traditional methods of cement and bridge plugs continue to be the standard but don't always provide an optimal solution. Thankfully there is a new technology on the market that provides a superior seal in wells when compared to traditional methods. That technology is comprised of bismuth and thermite.
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.
Intelligent multilateral well completions provide downhole flow rate, pressure, and temperature measurements at multiple well segments which allows for a continuous spatiotemporal data stream. Such an extensive data input poses a challenging task to decide on the optimal strategy of manipulating the inflow control valve (ICV) settings over time for best performance. This study investigated the use of machine learning to analyze and predict well performance given different ICV settings to ultimately maximize the well output.
A commercial reservoir simulator was used to generate two synthetic reservoir models: homogeneous (Case A) and heterogenous (Case B). These synthetic data were used to train, validate, and test machine learning models. The reservoir cases were generated based on a segmented, trilateral producer completed with three ICV devices installed at tie-in segments. The data used were measurements of wellhead and downhole flow rates across ICV segments over a period of 4,000 days. A total of 1,330 experiments were conducted with an eight-day timestep, generating a total of 667,660 sample data points for each of Case A and Case B. Fully connected neural networks were used to fit the data while model generalizability was enhanced using regularization techniques, namely L2 regularization and early stopping.
Both random sampling and Latin Hypercube Sampling (LHS) methods were evaluated in constructing the training, validation, and testing splits. Trained with different sample sizes drawn from the 1,330 simulated data histories for the two reservoir models, the proposed neural network showed excellent results. Given only ten simulated choices of ICV settings for training, the network proved capable of predicting oil and water production profiles at surface for both homogeneous and heterogeneous reservoir models with over 0.95 coefficient of determination (R2) when evaluated at unseen, test ICV settings. Extending the problem to downhole flow performance prediction, about 40 training simulated settings were necessary to achieve 0.95 R2. We observed that LHS was superior to random sampling in both R2 average and confidence interval. We also found that increasing the training and validation sample sizes increased the test R2 when testing against unseen cases. Study results suggest the applicability of machine reinforcement learning to estimate the well output at different ICV settings, where the neural network model depends fully on the real-time well feedback and production measurements.
By using a machine learning approach during the operation of a well with multiple ICV settings, it would be feasible to estimate the lateral-by-lateral output at unseen scenarios. Hence, it becomes possible to maximize the well output by using an optimization algorithm to determine the optimal ICV settings.
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.
Aminfar, Ehsan (University of Calgary) | Sequera-Dalton, Belenitza (University of Calgary) | Mehta, Sudarshan Raj (University of Calgary) | Moore, Gordon (University of Calgary) | Ursenbach, Matthew (University of Calgary)
The injection of air into mature steam chambers is a promising technology to reduce the steam-to-oil-ratios (SOR) in late stages of the Steam-Assisted-Gravity-Drainage (SAGD) recovery process in Athabasca oil sand reservoirs in Alberta, Canada. Air injection allows sustaining steam chamber pressures with reduced steam injection rates. The steam capacity that becomes available due to the replacement of steam with air in mature well-pairs or pads could serve new pads optimizing steam utilization and decreasing the overall environmental footprint of the project. A novel large scale three-dimensional (3-D) physical model was designed to evaluate the prospect of the "hybrid" air and steam injection technology in a SAGD configuration utilizing up to three well-pairs. This paper discusses the 3-D model design, commissioning, experimental procedure and main results of the first tests.
For each test, the 3-D model was packed with a low oil saturation core or lean zone, representing the reservoir portion swept by steam, and a high oil saturation core or rich zone representing the un-drained zone between two coalesced steam chambers. These zones were made with preserved native "lean" and "rich" cores from Athabasca reservoirs. Once the model was packed, it was placed inside a pressure jacket where it was pressurized to reservoir pressure. Steam was injected into the model to develop a representative steam chamber in the lean zone. Once steam conditions were attained in the lean zone, steam injection was switched to air injection. Temperatures distributed in the 3-D model as well as injection and production pressures and produced gas compositions were monitored constantly and recorded during the test. Produced liquid samples were regularly captured and stored for subsequent analysis. Post-processing analyses of produced fluids and residual extracted core material allowed for determination of clean-burned zones, material balance, upgrading of the produced bitumen samples and efficiency of the process.
High peak temperatures, gas compositions, clean-burned sand in post-test cores and significant oil production indicate the development of a high temperature combustion front in the 3-D experiments. The test results confirm the injection of air into mature SAGD chambers is a very promising method not only to reduce the cumulative steam-to-oil-ratios (CSOR) and to sustain the steam chamber pressures but also to increase oil production in SAGD late life.