(356c) A Computational Framework for Optimizing and Evaluating Critical Mineral Opportunities in Produced Water Networks

Many of minerals have recently been classified by the DOE as critical minerals, i.e. minerals important to energy security and considered a supply risk [1]. Produced water – an oil & gas waste or byproduct – presents a potential source of critical minerals and rare earth elements (i.e., Lithium); however, depending on the concentration, infrastructure, development, among other challenges, not all the produced water networks are economically feasible to recover CM/REEs.

Produced water networks consist of multiple network nodes (production pads, completion pads, storage, injection, and treatment sites), and the main goal is to identify the optimal water management across the network and in some cases infrastructure buildout [2]. The main challenges associated with evaluating the PW network for the economic feasibility of CM/REEs arise from i) tracking water quality across the network, ii) identifying the optimal location of the treatment site (i.e., water desalination or evaporation systems), iii) and optimal water blending to maximize efficiency of the treatment technology. We propose a multi-period deterministic quadratically constrained program (QCP) to determine the optimal flow schedule of a given produced water network while meeting minimum lithium concentration requirements. The model tracks flows, inventory, and concentration of lithium throughout the network and can be extended to track other components like Total Dissolved Solids (TDS).

This work presents a computational framework for evaluating and optimizing CM/REE recovery from produced water networks. Since, Lithium is a top priority for renewable energy (i.e., batteries) a case study based on a Permian-basin produced water network is presented, the results show the feasibility of the proposed approach to evaluate a given PW network and its economic feasibility for the recovery of CM/REEs. We successfully solved such problems to local optimality and showed how optimization tools aid in meeting minimum concentration requirements that were otherwise considered challenging with normal operation.

Finding global solutions to such problems is still a challenge with today’s off-the-shelf solvers, despite recent advancements in quadratic solvers. A similar class of problems, known as the pooling problems, have been extensively studied but most networks tackled in these works have been simpler feed-forward networks [3]. Produced water networks are more complex systems where even achieving feasibility is non-trivial. Given the structure of produced water network problems, we believe temporal decompositions would improve computational performance.

Despite being developed primarily for nonlinear stochastic programs, Cao and Zavala’s (2019) novel global optimization algorithm can be extended to deterministic nonlinear programs for problems where first-stage and second-stage variables can be distinctly recognized [4]. In our current work, we look to apply this algorithm on a produced water network problem to partition the feasible region temporally and solve the model to global optimality.

Acknowledgments

We gratefully acknowledge support from the U.S. Department of Energy, Office of Fossil Energy and Carbon Management, through the Environmentally Prudent Stewardship Program.

Disclaimer

This project was funded by the Department of Energy, National Energy Technology Laboratory an agency of the United States Government, through a support contract. Neither the United States Government nor any agency thereof, nor any of its employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof.

References

[1] US Department of Energy, “Critical Materials Assessment,” tech. rep., 2023.

[2] M.G. Drouven, A.J. Calderon, M.A. Zamarripa, and K. Beattie, “PARETO: An open-source produced water optimization framework,” Optim Eng 24, 2229-2249, 2023.

[3] R. Misener, and C.F. Floudas, “Advances for the Pooling Problem: Modeling, Global Optimization, and Computational Studies,” App. Comp. Math, 8, pp. 3-22, 2009.

[4] Y. Cao, and V. Zavala, “A scalable global optimization algorithm for stochastic nonlinear programs”, J Glob Optim, 75:393-416, 2019.