ExxonMobil (Germany)

Carbon nanothreads are one-dimensional sp3-hybridized polymers that have been theorized to exhibit new mechanical, thermal, or electronic properties. When derived from polycyclic aromatic hydrocarbons, nanothreads with thicker diameters emerge, which may promote higher ultimate strengths and/or bending moduli. In this contribution, we investigate the thermally mediated polymerization of naphthalene and pyrene under uniaxial pressure. Both PAHs undergo successful polymerizations, affording crystalline solids with distinctly different interplanar spacings (dNap = 7.77, 6.64, and 6.24 Å; dPy = 8.81, 8.37, and 7.33 Å). The application of heat resulted in the reduction of maximum pressures (at least 8 GPa for naphthalene and at least 4 GPa for pyrene), with a more well-defined diffraction pattern being obtained in the polymerization of pyrene. Spectroscopic analysis supported the formation of predominantly C(sp3)-hybridized extended carbon materials, consistent with simulated crystalline nanothreads without cross-linking.

Wettability and fluid transport mechanisms in unconventional rocks have been a matter of continued debate, with broad implications on oil production from these reservoirs. The presence of microstructural heterogeneities makes the characterization of these rocks complicated. In this paper, the fluid transport properties in a tight sandstone and a silicaceous microfractured mudstone from the Permian Basin are quantified through spatial NMR imaging experiments on spontaneous imbibition of dodecane and water. The tight sandstone sample has a relatively homogeneous pore structure with mixed-wet pores and wettability features similar to conventional sandstones, leading to fluid transport governed by the fractional flow model. In contrast, the mudstone sample has bedding-parallel microfractures that are oil-wet due to the presence of organic matter, while the rock matrix is mixed-wet. The presence of these microfractures leads to nontrivial fluid transport features that require more advanced analysis. The microfractures enhance the transport of hydrocarbon fluids into the matrix by facilitating their movement along bedding planes. Hydrocarbon imbibition occurs along bedding planes, as well as normal to bedding planes from oil-wet microfractures to the matrix. In contrast, a similar transport normal to the bedding planes is not observed for water imbibition. Finally, transport rates along bedding planes are higher, implying anisotropy in transport parallel and perpendicular to the bedding planes.

Summary In this paper we demonstrate the methodology to build the velocity model for domain conversion of seismic image data with an aim to maximize value of seismic in unconventional assets. A velocity model is necessary to map depth and thickness of subsurface layers, interpreted from seismic reflection images. Where a seismic reflection image is produced by time or depth imaging, ensures the proper focusing and lateral positioning of the subsurface reflectors. Although a good seismic image is not enough for an exploration or field development. Good well ties and reliable depth conversion are also required (Etris et al, 2002). Time to depth conversion using vertical propagation velocities addresses vertical positioning of the reflectors to true depth. Also, depth conversion is a way to remove the structural ambiguity inherent in time and verify subsurface structure. A depth conversion consists of imaging first, to obtain the best seismic image, then depthing, to tie wells and predict depth away from the wells.

Abstract Oil and gas companies and the service sector continue to push the limits of technology. Operators are now drilling and completing 4-mile laterals in the Permian Basin and using Hydraulic Completion Units (HCU's) to complete and intervene in these ultra long laterals. This case study will present ExxonMobil and Deep Well Services’ preparation, execution and lessons learned from milling frac plugs and intervention operations on a set of 8 ultra-long lateral wells in the Permian Basin.

The capture of carbon dioxide (CO2) emissions using porous solids is challenging because polarized water molecules bind more strongly in most materials than non-polarized CO2 molecules. This is a challenge for both flue gas capture and for direct air capture alike. We developed a bottom-up computational screening workflow to calculate the binding energy of 27,446 diverse molecular fragments with both CO2 and water. Most molecules favoured water binding, but bent, ‘clip’-like aromatic molecules showed potential for the desired reverse selectivity. This suggested that aromatic macrocycles with specific shapes might promote multiple weak p-p interactions with CO2 that could surpass stronger but less numerous dipole-p interactions with water. We synthesized two water- and acid-stable molecular prisms with triangular and square geometries, as suggested by computation. Experiments confirmed that the CO2 capture capacity of these prisms is unaffected by high relative humidity, surpassing the performance of benchmark commercial porous materials.

<div class="section abstract"><div class="htmlview paragraph">The American Petroleum Institute’s (API) Single Technology Matrix (STM) is a data-based, Virtual Testing process and protocol (utilizes test data, characteristics and features of base stocks and blends coupled with statistical methods and analysis) used to predict the performance capability of a specific engine oil additive technology in a single specified base oil, in a given engine test. The concept was first introduced in 2002, codified and implemented by API in 2007, and updated in 2022. The previously published advantages of STM in the proof-of-performance of engine oils, remain relevant.</div><div class="htmlview paragraph">These advantages include a data space focused on interpolation, documented statistical analysis protocol, limitation to a specific formulation, flexibility in understanding complicated, interactive, or non-linear technology and base oil relationships, and timeliness.</div><div class="htmlview paragraph">There have been numerous changes to, and in, the engine oil industry since the introduction of STM in 2007. These include advances in base stock refining practices, improvement in additive technology, lower viscosity oils, supply chain issues due to local and global disruptive events, test capacity issues due to stalled industry growth and increased regulatory practices. The enhancement of the STM process for both 2022 and beyond is warranted and justified in response to these changes.</div><div class="htmlview paragraph">This paper serves as an educational tool, not an instructional manual, on the STM concept to enhance understanding and perspective of the current concept not provided elsewhere, as well as the improvement of the process to address the evolving performance testing and base oil characterization needs of the future. It is an important first step in the necessary continuous dialogue on the protocol and the future data needs to support the protocol.</div></div>

Abstract Robust well rate estimation, one of the most important requirements for production surveillance, is essential to understand the performance of a hydrocarbon field and plan optimization activities that can assist in additional production uplift. This paper is concerned with developing a novel framework to combine multiple data sources in order to produce highly reliable well rate estimates. The focus is on improving meter water-cuts (water to total liquid ratio) during measurements using manual spin cuts. Accuracy of in-line automated water-cut meter readings, which measures water-cut during well testing, gets reduced with presence of gas. Water-cuts calculated from spin cut samples, which are manually collected from well test set-up, are more reliable as they do not depend on gas content. Different machine learning modeling approaches were evaluated to estimate a new corrected meter water-cut reading for every well test, using historical spin cut data and closest meter water-cut and temperature readings recorded during collection of these spin cut samples. To account for fluctuations in meter readings resulting from manual calibration of meter, a weighted correction factor ("bias correction") was also added to the improve the predictions. While comparing raw meter reading and model estimates to water cut from spin cut samples, from one year of unseen data, a significant reduction (from 5.6% to 0.5%) in mean error was observed. From evaluations, Tree Bagger models, which are Random Forest models without random selection of features, were found to have the best performance. Moreover, the model was able to capture and respond to sudden shifts in meter readings, possibly originating from manual calibration of the meter. While considering individual wells (or well groups which are tested together through the same meter), poor model performance was observed for few wells where spin cuts consistently recorded values lower than the corresponding meter readings. Model performance was also impacted on well groups where (a) spin cuts taken in close time proximity showed higher variation in water-cuts and (b) spin cut data was sparse. The corrected water-cuts and corresponding phase rates received positive feedback from the operators, underscoring their significance in well rates estimation. This approach uses multiple data sources, real time and historical meter data and, latest and historical manual spin cut data for a surveillance system that runs every few minutes, thus correcting water-cut measurements and coupling it with real time well rate estimations. Additionally, an innovative bias correction method was implemented to improve accuracy of measurements. This framework generates substantial value for upstream businesses with higher well counts (1000+ wells) where data driven approaches are proven to enhance surveillance.

Abstract Surveillance and adjustments of fillbase and fillage are crucial for optimizing pump performance and maximizing production efficiency. Proper management of fillbase not only promotes longevity by preventing unnecessary wear and tear, but also helps in optimizing fluid handling efficiency, energy efficiency, rod-load management, and overall production of rod pumps. The primary objective of this paper is to propose a comprehensive and automated solution for surveillance and optimal management of fillbase with high number of wells. Our proposed method powered by combination of machine learning models enable efficient, data-driven monitoring and fillbase tuning. The pump cards (Position-Load on the rod) generated by the wellhead controllers along with other operational parameters including pump-speed, wellhead-pressure, temperature, etc. are first processed by a LightGBM classification model to classify cards as good or bad. If the card is bad, the operators are notified to help with supervision and taking remedial actions, and if the card is good, fillbase is predicted by a XGBoost regression model and the values are adjusted automatically based on the predictions. Different machine learning models were assessed for classifying pump cards and predicting fillage and fillbase. The best models with their respective hyperparameters were then tested in the field. These models were evaluated in real-time by senior operation specialists and feedback was collected. The card classification model had an accuracy score of approximately 99% on the test data and showed similar performance during real-time monitoring. The Fillbase model had R2 values of 98% and the fillbase model correctly detected the fillbase setting in around 95% of cases, as confirmed by senior operation specialists and production engineers. The controller calculates the fillage based on the given fillbase setting and there is an average deviation of around 4% from the fillage predicted by our model. The fluid rates recorded by the controllers after adjusting the fillbase were compared to well test rates, and there was a significant decrease (15%) in mean absolute error after implementing the ‘Auto Fill’ workflow, which serves as a quantitative validation for our models. Additionally, the model can adjust the fillbase settings even in the event of sudden changes in operating conditions, reducing the need for frequent manual interventions by operators. Using data driven systems and powerful machine learning models, the proposed approach aims to automate surveillance and fillbase management, reducing the need for manual effort from operators and production engineers. This process also enhances consistency in setting fillage or fillbase and allows for automated speed control of pumps, improving production and energy efficiency. The system is specifically designed to optimize operational productivity in large oil field settings with numerous wells by streamlining surveillance and fillbase adjustment processes.