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Duncan, Ian J.

Publications and source records attributed to Duncan, Ian J..

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗
DOE OSTI2022

Economic analysis of CCUS: Accelerated development for CO 2 EOR and storage in residual oil zones under the context of 45Q tax credit

Residual oil zones (ROZ) undergoing CO 2 Enhanced Oil Recovery (CO 2 -EOR) may benefit from specific strategies to maximize their value. We evaluated several strategies for producing from a Permian Basin, West Texas, USA field’s ROZ. This ROZ lies below the main pay zone (MPZ) of the field. Such brownfield ROZs occur in the Permian Basin and elsewhere. Since brownfield ROZs are hydraulically connected to the MPZs, development sequences and schemes influence oil production, CO 2 storage, and net present value (NPV). We conducted economic assessments of various CO 2 injection/production schemes in the stacked ROZ-MPZ reservoir based on flow simulations of a high-resolution geocellular model built from wireline logs and core data and calibrated through production history matching. Flow simulations of water alternating gas (WAG) injection, such as switching injection from the MPZ to the ROZ and commingled production, were studied. Simulation results showed that simultaneous CO 2 injection into the MPZ and ROZ lead to the largest oil production and, generally, the largest NPV. If instead, CO 2 was simultaneously injected into the MPZ and ROZ, then into the ROZ alone, this maximized CO 2 storage. CO 2 storage can be used as a tax credit under the Internal Revenue Code, Section 45Q. Storage performance depends on the development approach and WAG ratio. Developing the ROZ increased storage compared to only producing from the MPZ. The WAG ratio to maximize oil production did not always yield the largest NPV. These findings are potentially applied to other Brownfield ROZs, which are common below San Andres reservoirs in the Permian Basin and other basins. ROZ development can increase oilfields’ NPV and carbon storage potential. Our study can serve as an analog for similar reservoirs. Here this work provides valuable insights into the further optimization of brownfield ROZ development and information for operators to plan to develop stacked ROZ-MPZ reservoirs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Maximizing oil production from water alternating gas (CO 2 ) injection into residual oil zones: The impact of oil saturation and heterogeneity

Residual oil zones (ROZs) are widespread reservoirs, characterized by oil at residual saturation, either underlying oil fields (brownfield) or lateral (greenfield) to such fields. These reservoirs have the potential to produce volumes of oil sufficiently significant to make appreciable impacts on the US’s oil reserves and associated incidental CO 2 sequestration. The objective of this study is to improve our understanding the impact of heterogeneous and low oil saturations, in brownfield ROZs, on the effectiveness of water alternating gas (WAG) injection strategies. ROZs occur in the Permian Basin and elsewhere, and operators are using CO 2 injection for enhanced oil recovery (EOR) in these zones. The consensus model for the formation of ROZs is that they were formed by the effect of faster regional aquifer flow, acting over millions of years. Both the magnitude of oil saturation and the spatial distribution of oil differ from water-flooded main pay zones (MPZs). To explore the most effective injection strategies, we conducted simulations of CO 2 injection into synthetic geologic reservoirs. These simulations focused on injection into reservoirs subject to either man-made waterflooding or long-term natural waterflooding. By exploring the impact of varying: oil saturation; well patterns; reservoir heterogeneity; and permeability anisotropy, we attempt to quantify the factors that most influence the effectiveness of WAG injection. WAG ratios (the ratio of injected water and CO 2 , in reservoir volumes) of interest are those that either minimize the net CO 2 utilization ratios or maximize oil production rates. In general, the most effective WAG ratios for ROZs, are consistently less than those observed undergoing CO 2 injection in the same geologic reservoir models after traditional (man-made) waterflooding. This work demonstrates that most favorable WAG ratios for oil production in ROZs are different from those in traditional MPZs because of oil saturation differences. Therefore, CO 2 injection into both zones or directly copying WAG injection designs from MPZs to ROZs might not maximize oil production.

04 OIL SHALES AND TAR SANDS↗