Turbulent Drag Reduction with an Ultra-High-Molecular-Weight Water-Soluble Polymer in Slick-Water Hydrofracking

Water-soluble polymers as drag reducers have been widely utilized in slick-water for fracturing shale oil and gas reservoirs. However, the low viscosity characteristics, high operating costs, and freshwater consumption of conventional friction reducers limit their practical use in deeper oil and gas reservoirs. Therefore, a high viscosity water-soluble friction reducer (HVFR), poly-(acrylamide-co-acrylic acid-co-2-acrylamido-2-methylpropanesulphonic acid), was synthesized via free radical polymerization in aqueous solution. The molecular weight, solubility, rheological behavior, and drag reduction performance of HVFR were thoroughly investigated. The results showed that the viscosity-average molecular weight of HVFR is 23.2 × 106 g⋅mol−1. The HVFR powder could be quickly dissolved in water within 240 s under 700 rpm. The storage modulus (G′) and loss modulus (G″) as well as viscosity of the solutions increased with an increase in polymer concentration. At a concentration of 1700 mg⋅L−1, HVFR solution shows 67% viscosity retention rate after heating from 30 to 90 °C, and the viscosity retention rate of HVFR solution when increasing CNaCl to 21,000 mg⋅L−1 is 66%. HVFR exhibits significant drag reduction performance for both low viscosity and high viscosity. A maximum drag reduction of 80.2% is attained from HVFR at 400 mg⋅L−1 with 5.0 mPa⋅s, and drag reduction of HVFR is 75.1% at 1700 mg⋅L−1 with 30.2 mPa⋅s. These findings not only indicate the prospective use of HVFR in slick-water hydrofracking, but also shed light on the design of novel friction reducers utilized in the oil and gas industry.

Applied Transfer Learning for Production Forecasting in Shale Reservoirs

There are documented cases of machine learning being applied to different segments of the oil and gas industry with different levels of success. These successes have not been readily transferred to production forecasting for unconventional oil and gas reservoirs because of sparsity of production data at the early stage of production. Sparsity of unconventional production data is a challenge, but transfer learning can mitigate this challenge. Application of machine learning for production forecasting is challenging in areas with insufficient data. Transfer learning makes it possible to carry over the information gathered from well-established areas with rich data to areas with relatively limited data. This study outlines the background theory along with the application of transfer learning in unconventionals to aid in production forecasting. Similarity metrics are utilized in finding candidates for transfer learning by using key drivers for reservoir performance. Key drivers include similar reservoir mechanisms and subsurface structures. After training the model on a related field with rich data, most of the primary parameters learned and stored in a representative machine or deep learning model can be re-used in a transfer learning manner. By employing the already learned basic features, models with sparse data have been enriched by using transfer learning. The approach has been outlined in a stepwise manner with details. With the help of the insights transferred from related sites with rich data, the uncertainty in production forecasting has decreased, and the accuracy of the predictions increased. As a result, the details of selecting a related site to be used for transfer learning along with the challenges and steps in achieving the forecasts have been outlined in detail. There are limited studies in oil and gas literature on transfer learning for oil and gas reservoirs. If applied with care, it is a powerful method for increasing the success of models with sparse data. This study uses transfer learning to encapsulate the basics of the substructure of a well-known area and uses this information to empower the model. This study investigates the application to unconventional shale reservoirs, which have limited studies on transfer learning.

Geochemical Investigation of CO2 Injection in Oil and Gas Reservoirs of Middle East to Estimate the Formation Damage and Related Oil Recovery

The injection performance of carbon dioxide (CO2) for oil recovery depends upon its injection capability and the actual injection rate. The CO2–rock–water interaction could cause severe formation damage by plugging the reservoir pores and reducing the permeability of the reservoir. In this study, a simulator was developed to model the reactivity of injected CO2 at various reservoir depths, under different temperature and pressure conditions. Through the estimation of location and magnitude of the chemical reactions, the simulator is able to predict the effects of change in the reservoir porosity, permeability (due to the formation/dissolution) and transport/deposition of dissoluted particles. The paper also presents the effect of asphaltene on the shift of relative permeability curve and the related oil recovery. Finally, the effect of CO2 injection rate is analyzed to demonstrate the effect of CO2 miscibility on oil recovery from a reservoir. The developed model is validated against the experimental data. The predicted results show that the reservoir temperature, its depth, concentration of asphaltene and rock properties have a significant effect on formation/dissolution and precipitation during CO2 injection. Results showed that deep oil and gas reservoirs are good candidates for CO2 sequestration compared to shallow reservoirs, due to increased temperatures that reduce the dissolution rate and lower the solid precipitation. However, asphaltene deposition reduced the oil recovery by 10%. Moreover, the sensitivity analysis of CO2 injection rates was performed to identify the effect of CO2 injection rate on reduced permeability in deep and high-temperature formations. It was found that increased CO2 injection rates and pressures enable us to reach miscibility pressure. Once this pressure is reached, there are less benefits of injecting CO2 at a higher rate for better pressure maintenance and no further diminution of residual oil.

Research on Test and Logging Data Quality Classification for Gas–Water Identification

Tight sandstone oil and gas reservoirs are widely distributed, rich in resources, with a bright prospect for exploration and development in China. Due to multiple evolutions of the structure and sedimentary system, the gas–water distribution laws are complicated in tight sandstone gas reservoirs in the northern Ordos area. It is difficult to identify gas and water layers in the study area. In addition, in the development and production, various factors, such as the failure of the instrument, the difference in construction parameters (injected sand volume, flowback rate), poor test results, and multi-layer joint testing lead to unreliable gas test results. Then, the inaccurate logging responses will be screened by unreliable gas test results for different types of fluids. It is hard to make high-precision fluid logging identification charts or models. Therefore, this article combines gas logging, well logging, testing and other data to research the test and logging data quality classification. Firstly, we select reliable standard samples through the initial gas test results. Secondly, we analyze the four main factors which affect the inaccuracy of gas test results. Thirdly, according to these factors, the flowback rate and the sand volume are determined as the main parameters. Then, we establish a recognition chart of injected sand volume/gas–water ratio. Finally, we proposed an evaluation method for testing quality classification. It provides a test basis for the subsequent identification of gas and water through the second logging interpretation. It also provides a theoretical basis for the exploration and evaluation of tight oil and gas reservoirs.

Concealed-Fault Detection in Low-Amplitude Tectonic Area—An Example of Tight Sandstone Reservoirs

Fault detection is important to seismic interpretation, especially for tight oil and gas reservoirs. Generally speaking, large-scale faults can be accurately imaged and are easy to detect by conventional methods, but the concealed ones in low-amplitude structural regions are difficult to find. In these areas, the scale and displacement of concealed faults are usually very small. Due to the good uniform and weak amplitude disturbances in the seismic events, the traditional discontinuity attributes extracted from seismic data are always not effective. This is because the discontinuous features of large faults are very significant, and the weak anomalies caused by hidden faults are very close to the continuous background. This paper takes a tight sandstone reservoir in the Ordos Basin of China as an example to explore the detection method of subtle faults in low-amplitude structural areas. With the phase congruency analysis method, we extract edge features from the post-stack coherence attributes to identify hidden faults. Practice shows that this idea has outstanding performance in mining hidden fracture features and improving the accuracy of fracture recognition. The results successfully predict a shear fault zone in the northeast of the work area, find a new fracture zone in the center of the survey and a series of hidden faults in non-target strata. It would be beneficial to extend the strata and area of oil and gas reservoirs.

Assessing the effect of proppant compressibility on the conductivity of heterogeneously propped hydraulic fracture

Hydraulic fracturing is a technology that is widely used in the development of oil and gas formations. Given that the fracture closure has a strong impact on production, quantifying the resulting fracture conductivity is critical for optimizing treatment design. The goal of this paper is to better understand the influence of the closing stress on the fracture conductivity when the proppant distribution is heterogeneous. In addition to the spatial proppant distribution, the conductivity of the propped fracture is affected by proppant deformation and embedment. Numerical results indicate that compressibility of proppant can significantly change the residual fracture aperture and, consequently, production performance in oil and gas reservoirs

Introduction to this special section: Quantitative interpretation

Quantitative interpretation (QI) is the geophysicist's endeavor to go beyond reservoir architecture. It is the effort to use geophysical measurements in understanding reservoir properties such as rock type, porosity, and fluid composition. QI often refers to the use of seismic amplitude analysis to predict lithology, porosity, and pore fluids away from the wellbore in oil and gas reservoirs. However, we can generalize and expand the concept of QI beyond seismic methods and beyond oil and gas reservoirs. In this special section, we feature five papers and cover not only seismic and well-log data, but also gravity and magnetic data. We address a hydrothermal reservoir in addition to several oil and gas reservoirs.