Chemical solutions for restoring scaling-induced injectivity impairment in offshore produced water re-injection.

Produced water re-injection (PWRI) is a promising and sustainable strategy to manage substantial quantities of produced water for subsurface energy production systems. This approach offers an alternative to the environmentally harmful practice of marine disposal. Nonetheless, produced water re-injection may lead to considerable reductions in the injectivity. The injectivity loss can be attributed to several factors, including inorganic scaling, which can obstruct the flow pathway through porous media near the wellbore as well as subsurface facilities (e.g., tubing). Scaling can also contribute to the formation of mixed organic-inorganic schmoo-like complexes. Iron-containing (FexSy, FexOy-FexOyHz), carbonate-, and sulfate-based scales (e.g., BaSO4, SrSO4, and CaCO3) are the primary precipitates that have disruptive effects during PWRI scheme, especially in reservoirs suffering from microbial souring activities. In this work, we first screened the mineral scales that may form under the relevant re-injection conditions using the composition of produced water and seawater samples from the Danish North Sea. Subsequently, we assessed the efficiency of a commercial scale inhibitor against the scaling of targeted mineral phases through a series of batch experiments, followed by the development of a model to simulate its inhibitory performance. To reduce the precipitation or deposition of different minerals in water injection applications, we evaluated the combined effect of adding other chemicals (i.e., an acid, an oxidizer, and a chelating agent) to the injection water along with the scale inhibitor. To do this, we described the relevant mineral-aqueous interactions (dissolution, precipitation, and solution complexation) in PHREEQC. This predictive model represents an alternative to time- and resource-intensive experiments and may aid in achieving optimized chemical recipes required to mitigate mineral scaling in water injection systems under various physiochemical conditions. This work can contribute to the development of more sustainable and efficient strategies for managing produced water, ultimately helping to reduce the environmental impacts of hydrocarbon production.

Electrokinetic behavior of artificial and natural calcites: A review of experimental measurements and surface complexation models.

The surface charge of calcite in aqueous environments is essential to many industrial and environmental applications. Electrokinetic measurements are usually used to assess the calcite charging behavior and characterize its electrical double layer (EDL). Numerous surface complexation models (SCMs) have been proposed to interpret the effect of different surface interactions on the zeta potential. Because of their versatility, SCMs have also become important tools in reactive transport modeling. The research on enhanced oil recovery within the last decade has led to an increased number of publications reporting both zeta potential measurements and SCMs for calcite. Nonetheless, the measurements are often inconsistent and the reasons for choosing one model over another are unclear. In this work, we review the models proposed for calcite and address their main differences. We first collect a large number of published zeta potential measurements and then we fit a Diffuse Layer, Basic Stern, and Charge-Distribution Multi-Site Complexation models to a selected reliable dataset. For each model, we maintain a similar number of adjustable parameters. After optimizing the parameters of the models, we systematically compare their prediction capabilities against data obtained in monovalent and divalent electrolyte systems containing calcium, magnesium, sulfate, or carbonate. We show that, often, the discrepancies between the models and the experimental data can be explained by different levels of disequilibrium. Nonetheless, assumptions used in the development of the models may significantly reduce their extrapolability to variable chemical conditions. The poor agreement between the models tuned to electrokinetic data with surface charge measurements and dynamic retention from single-phase flowthrough tests show that zeta potential may not be the best type of data to characterize ion binding at the calcite surface. Including the effect of mineral impurities and temperature on the calcite surface speciation and electrokinetic behavior prevail as main challenges for reactive transport modeling.

Importance of thermodynamics dependent kinetic parameters in nitrate-based souring mitigation studies.

Souring is the unwanted formation of hydrogen sulfide (H2S) by sulfate-reducing microorganisms (SRM) in sewer systems and seawater flooded oil reservoirs. Nitrate treatment (NT) is one of the major methods to alleviate souring: The mechanism of souring remediation by NT is stimulation of nitrate reducing microorganisms (NRM) that depending on the nitrate reduction pathway can outcompete SRM for common electron donors, or oxidize sulfide to sulfate. However, some nitrate reduction pathways may challenge the efficacy of NT. Therefore, a precise understanding of souring rate, nitrate reduction rate and pathways is crucial for efficient souring management. Here, we investigate the necessity of incorporating two thermodynamic dependent kinetic parameters, namely, the growth yield (Y), and FT, a parameter related to the minimum catabolic energy production required by cells to utilize a given catabolic reaction. We first show that depending on physiochemical conditions, Y and FT for SRM change significantly in the range of [0-0.4] mole biomass per mole electron donor and [0.0006-0.5], respectively, suggesting that these parameters should not be considered constant and that it is important to couple souring models with thermodynamic models. Then, we highlight this further by showing an experimental dataset that can be modeled very well by considering variable FT. Next, we show that nitrate based lithotrophic sulfide oxidation to sulfate (lNRM3) is the dominant nitrate reduction pathway. Then, arguing that thermodynamics would suggest that S° consumption should proceed faster than S0 production, we infer that the reason for frequently observed S0 accumulation is its low solubility. Last, we suggest that nitrate based souring treatment will suffer less from S0 accumulation if we (i) act early, (ii) increase temperature and (iii) supplement stoichiometrically sufficient nitrate.

A novel relationship for the maximum specific growth rate of a microbial guild.

One of the major parameters that characterizes the kinetics of microbial processes is the maximum specific growth rate. The maximum specific growth rate for a single microorganism (${\mu _{max}}$) is fairly constant. However, a certain microbial process is typically catalyzed by a group of microorganisms (guild) that have various ${\mu _{max}}$ values. In many occasions, it is not feasible to breakdown a guild into its constituent microorganisms. Therefore, it is a common practice to assume a constant maximum specific growth rate for the guild ($\acute{\mu}_{max}$) and determine its value by fitting experimental data. This assumption is valid for natural environments, where microbial guilds are stabilized and dominated by microorganisms that grow optimally in those environments' conditions. However, a change in an environment's conditions will trigger a community shift by favoring some of the microorganisms. This shift leads to a variable ${\acute{\mu}_{max}}$ as long as substrate availability is significantly higher than substrate affinity constant. In this work, it is illustrated that the assumption of constant ${\acute{\mu}_{max}}$ may underestimate or overestimate microbial growth. To circumvent this, a novel relationship that characterizes changes in ${\acute{\mu}_{max}}$ under abundant nutrient availability is proposed. The proposed relationship is evaluated for various random microbial guilds in batch experiments.

The Impact of Spatially Correlated Heterogeneity and Adsorption on Modified Salinity Water in Carbonates.

Modified salinity water (MSW) core flooding tests conducted in carbonates often exhibit a delay in the additional oil recovery. It has been suggested that the ionic adsorption process controls this delay. In this study, we examine the adverse effect of the adsorption process on the performance of MSW flooding in various models categorized as layered and heterogeneous reservoirs and a North Sea field sector model. To evaluate the impact of porous media's heterogeneity on the delay caused by the adsorption, we introduce the net present volumetric value based on which the cost of delay is calculated. This evaluation is achieved by comparing the calculated cost of delay for heterogeneous systems and that of their equivalent homogeneous porous media. It is found that, as the level of reservoir heterogeneity increases, the adverse effect of ionic adsorption on the improved oil production decreases. Further, computational results suggest that the connectivity index, which is defined as the effective permeability between injection and production wells divided by the average permeability, is a better alternative to the vorticity index to describe the impact of the delay of additional oil recovery in heterogeneous reservoirs subjected to MSW flooding.

Physics-informed neural networks for solving nonlinear diffusivity and Biot's equations.

This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy harvesting. Specifically, we investigate how to extend the methodology of physics-informed neural networks to solve both the forward and inverse problems in relation to the nonlinear diffusivity and Biot's equations. We explore the accuracy of the physics-informed neural networks with different training example sizes and choices of hyperparameters. The impacts of the stochastic variations between various training realizations are also investigated. In the inverse case, we also study the effects of noisy measurements. Furthermore, we address the challenge of selecting the hyperparameters of the inverse model and illustrate how this challenge is linked to the hyperparameters selection performed for the forward one.

A sulfur and nitrogen cycle informed model to simulate nitrate treatment of reservoir souring.

Nitrate treatment has been widely used in various seawater injection projects to treat biologic sulfate reduction or reservoir souring. To design a promising nitrate treatment plan, it is essential to have a comprehensive understanding of reactions that represent the microbial communities of the reservoir and mechanisms through which the souring process is inhibited. We employ a new approach of evaluating different reaction pathways to design reaction models that reflect governing microbial processes in a set of batch and flow experiments. Utilizing the designed models, we suggest dissimilatory nitrate reduction to ammonium is the main reaction pathway. Additionally, we illustrate nitrite inhibition is the major mechanism of nitrate treatment process; independent of nitrate reduction being autotrophic or heterotrophic. We introduce an inhibitory nitrate injection concentration that can inhibit souring regardless of nitrite inhibition effect and the distance between injection and production wells. Furthermore, we demonstrate that the ratio of the nitrite-nitrate reduction rate can be used to estimate nitrate treatment effectiveness. Our findings in regard to importance of nitrite inhibition mechanism and the inhibitory nitrate concentration are in accordance with the field observations.

An overview of the oil-brine interfacial behavior and a new surface complexation model.

The few existing surface complexation models (SCM) for the brine-oil interface have important limitations: the chemistry of each crude oil is not considered, they cannot capture the water/non-polar hydrocarbons surface charge, the interactions between Na+ and the acid sites are not included, and the equilibrium constants for the adsorption reactions are not validated against experimental data. We address the aforementioned constraints by proposing an improved diffuse-layer SCM for the oil-brine interface. The new model accounts for the chemistry of crude oils by considering surface sites linearly dependent on the TAN (total acid number) and TBN (total base number). We define weak sites to account for the negative surface charge observed for non-polar hydrocarbons in water. We optimize the parameters of our model by fitting the model to reported zeta potential measurements of oil in aqueous solutions. When we validate the optimized model against different experimental data sets, it generally shows a good performance in predicting the surface charge of oil in different brines with different pHs. We show that the acid and base numbers are only useful as a qualitative estimation of the distribution of polar groups at the oil surface, and more sophisticated analysis is necessary to quantify the chemistry of the oil-brine interface.