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38 records · Page 3

Inter-well connectivity detection in CO 2 WAG projects using statistical recurrent unit models

Routine well-wise injection and production measurements contain significant information on subsurface structure and properties. Data-driven technology that interprets surface data into subsurface structure or properties can assist operators in making informed decisions by providing a better understanding of field assets. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO 2 EOR projects utilizing the water-alternating-gas (WAG) process. SRU is a special type of recurrent neural network (RNN) that allows for better characterization of temporal trends, by learning various statistics of the input at different time scales. In our application, the complete states (injection rate, pressure and cumulative injection) at injectors and pressure states at producers are fed to SRU as the input and the phase rates at producers are treated as the output. Once the SRU is trained and validated, it is then used to assess the connectivity of each injector to any producer using permutation variable importance method, wherein inputs corresponding to an injector are shuffled and the increase in prediction error at a given producer is recorded as the importance (connectivity metric) of the injector to the producer. This method is tested in both synthetic and field-scale cases. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. This significantly improves confidence in our data-driven procedure. The novelty of this work is that it is purely data-driven method and can directly interpret routine surface measurements to intuitive subsurface knowledge. Furthermore, the streamline-based validation procedure provides physics-based backing to the results obtained from data analytics. This study results in a reliable and efficient data analytics framework that is well-suited for large field applications.

42 ENGINEERING↗

Selective Detection of Diglycolamide Diastereomers By Metal-Ligand Cluster Formation

Introduction (120 words): Differentiation of isomers using mass spectrometry is a challenge, as these species tend to produce ions of identical mass-to-charge ratio. Therefore, separation with mass spectrometry must rely on chemical differences between isomeric species. Separation of stereoisomers is especially challenging, as these species often have very similar chemistry. However, recent investigations of the solvent extraction behavior of diastereomers of diglycolamide molecules have shown that different stereoisomers can have significantly different lanthanide extraction efficiencies. These studies showed that the R,S stereoisomer of 2,2'-oxybis(N,N-dioctylpropanamide efficiently extracts lanthanides, while the S,S stereoisomer does not. Thus, gas-phase metal complexation may provide a pathway for differentiating these species using mass spectrometry. Methods (120 words): Europium, samarium, and holmium clusters with the R,S, and the S,S diastereomers of 2,2'-oxybis(N,N-didecylpropanamide (trivially tetradecyl diglycolamide, or mTDDGA) were generated in gas-phase using the electrospray ionization source of a Bruker (Billerica, MA, USA) micrOTOF-Q II quadrupole time-of-flight mass spectrometer. Solutions of aqueous lanthanide nitrate and diglycolamide ligands in 2-propanol were added to 50%:50% by volume acetonitrile:2-propanol to generate the spray solutions. Ligand concentration was maintained at approximately 6 µM, while lanthanide concentration was maintained at either 6 µM or 30 µM . High resolution, high mass accuracy spectra were recorded using the time-of-flight. Mass accuracy was ensured using external calibration with Agilent (Santa Clara, CA, USA) ESI-L Low Concentration tuning mix. Preliminary data (300 words): Electrospray of solutions containing only R,S-mTDDGA or S,S-m-TDDGA yield abundant peaks at m/z= 721.7, corresponding to [mTDDGA+H]+1, m/z = 743.7, corresponding to [mTDDGA+Na]+1, and m/z = 1464.4, corresponding to the sodium-bound dimer, [Na(mTDDGA)2]+1. Spray of solutions containing 6 µM europium (Eu) with 3 µM R,S-mTDDGA produced an abundant cluster ion with an isotopic envelope centered at m/z=772.0, corresponding to [Eu(R,S-mTDDGA)3]3+. Europium-containing cluster ions with isotopic envelopes apexing at m/z = 1012.3 and 1189.0 correspond to the [Eu(R,S-mTDDGA)4]3+ and [Eu(R,S-mTDDGA)5]3+ cluster ions, respectively. Protonated and sodiated R,S-mTDDGA are present as well, along with the sodium-bound dimer. In contrast, spray of solutions containing 6 µM europium (Eu) with 3 µM S,S-mTDDGA diastereomer yielded only protonated and sodiated ligand ions; no Eu- S,S-mTDDGA ions were present. This mirrors the solution-phase behavior, where the S,S diestereomer extracts lanthanides with significantly less efficiency than the R,S diestereomer. Experiments with holmium and samarium yielded the same results. Increasing the lanthanide concentration to 30 µM resulted in limited formation of lanthanide-S,S-mTDDGA clusters. However, the abundance of the [Ln(S,S-mTDDGA)3]3+ clusters remained low compared to the abundance of the protonated and sodiated ligand. Additionally, the abundance of the [Ln(R,S-mTDDGA)3]3+ cluster ion was significantly higher than that of the protonated and sodiated ligand in experiments with spray solutions containing 30 µM lanthanide with R,S-mTDDA, supporting the hypothesis that R,S-mTDDGA has a higher intrinsic affinity for the lanthanides than S,S-mTDDGA. Thus, the intrinsic complexation with lanthanides appears to offer a route to differentiate diglycolamide diestereomers in the gas-phase. Novelty (20): Stereoisomers can be differentiated in the gas phase by exploitation of differences in metal complexation chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗