No abstract

The application of chemical scale inhibitors (SI) in a squeeze treatment is one of the most commonly used techniques to prevent downhole scale formation. This paper presents a sensitivity analysis of the treatment design parameters, to assist with the automated optimization of squeeze treatments in single wells in an offshore field. Two wells were studied with different constraints on total SI neat volume (VSI) and total injected volume (VT) including main pill and overflush volumes, followed by a field case squeeze optimization to demonstrate the sensitivity to lifetime and the cost function per treated volume of water. A purpose-designed squeeze software model was used to simulate the squeeze treatments and perform the sensitivity analysis. In the course of this optimization procedure, a "Pareto Front" is calculated which represents cases that cannot in principle be improved upon. An analysis of these results also shows that this Pareto Front can be generated by a semi-analytical method, as shown for the first time in this paper. It was demonstrated at fixed values of VSI and VT (resulting in almost a fixed total cost for squeeze), the squeeze lifetime can be improved by increasing the scale inhibitor concentration in the main treatment slug; however, the increase in squeeze lifetime is greatly reduced at very high concentrations. Four generic scale inhibitors were used with different adsorption isotherms to validate these calculations. In cases where either VSI or VT is fixed, it is shown that the squeeze life does not monotonically increase by the other parameter and the cost function can be used to determine the optimum design. Well squeeze optimization was performed and these recommendations were applied in the field. It was shown that a well-executed sensitivity study can prevent misleading results that miss the global optimum. A lesson learned was that the optimal designs entail injecting as much of the inhibitor as possible as early in the squeeze design as possible - provided formation damage effects are avoided. Also, our semi-analytical construction of the Pareto Front greatly helps to simplify and streamline the overall squeeze optimization process.

How to estimate operational controls so as to optimize economic returns in CO2-WAG projects and reduce calcite scale risk? The reactivity and heterogeneity intrinsic to carbonate reservoirs make CO2-WAG (Water Alternating Gas) injection a big challenge. While miscibility effects greatly increase oil recovered, the presence of CO2 can cause severe flow assurance issues. The aim of this paper is to introduce a simulation-based methodology to optimize the design of CO2-EOR operations, considering economics, mineral scaling risk and environmental impact. A compositional reservoir model was built to simulate a reactive 3-phase miscible flow in porous media. Aiming at maximizing the Net Present Value (NPV), we optimized the following operational variables: duration of waterflooding period; injection rates; producer bottomhole pressure (BHP); WAG ratio, gas half-cycle duration and number of cycles for different WAG stages (tapered WAG). We then used the forecasted data to quantify calcium carbonate scaling tendency for the scenarios of interest and design scale management strategies (squeeze treatments) with the lowest costs. The optimal WAG design found through the methodology increased NPV by 55.6% compared to a base-case waterflooding scenario. We also found that performing a waterflood in a carbonate reservoir with high CO2 content will result in more severe calcite scale risk than applying equivalent WAG schemes. A lower production BHP can reduce the potential for precipitation, by allowing the CO2 to evolve from the aqueous solution within the reservoir, before it arrives at the production wellbore. On the other hand, a lower producer BHP can increase water production rates and, therefore, scale risk. The proposed workflow provides valuable insights on the procedures involved in simulating and optimizing CO2-WAG projects with high calcite scale risk. Its application demonstrated the importance of an integrated analysis that seeks for higher economic returns in a sustainable manner, with reduced production issues caused by CO2 speciation.

The objective of this study is to design a series of squeeze treatments for 20 years of production of a Brazilian pre-salt carbonate reservoir analogue, minimizing the cost of scale inhibition strategy. CO2-WAG (Water-Alternating-Gas) injection is implemented in the reservoir to increase oil recovery, but it may also increase the risk of scale deposition. Dissolution of CaCO3 as a consequence of pH decrease during the CO2 injection may result in a higher risk of calcium carbonate precipitation in the production system. The deposits may occur at any location from production bottom-hole to surface facilities. Squeeze treatment is thought to be the most efficient technique to prevent CaCO3 deposition in this reservoir. Therefore, the optimum WAG design for a quarter 5-spot model, with the maximum Net Present Value (NPV) and CO2 storage volume identified from a reservoir optimization process, was considered as the basis for optimizing the squeeze treatment strategy, and the results were compared with those for a base-case waterflooding scenario. Gradient Descent algorithm was used to identify the optimum squeeze lifetime duration for the total lifecycle. The main objective of squeeze strategy optimization is to identify the frequency and lifetime of treatments, resulting in the lowest possible expenditure to achieve water protection over the well's lifecycle. The simulation results for the WAG case showed that the scale window elongates over the last 10 years of production after water breakthrough in the production well. Different squeeze target lifetimes, ranging from 0.5 to 6 million bbl of produced water were considered to optimize the lifetime duration. The optimum squeeze lifetime was identified as being 2 million bbl of protected water, which was implemented for the subsequent squeeze treatments. Based on the water production rate and saturation ratio over time, the optimum chemical deployment plan was calculated. The optimization results showed that seven squeeze treatments were needed to protect the production well in the WAG scenario, while ten treatments were necessary in the waterflooding case, due to the higher water rate in the production window. The novelty of this approach is the ability to optimize a series of squeeze treatment designs for a long-term production period. It adds valuable information at the Front-End Engineering and Design (FEED) stage in a field, where scale control may have a significant impact on the field's economic viability.

Scale Inhibitor Squeeze treatments are some of the most common techniques to prevent oilfield mineral scale deposition in oil producers. A squeeze treatment design's effectiveness and lifespan is determined by the scale inhibitor (SI) retention, which can be described using a pseudo-isotherm adsorption, commonly derived from coreflooding experiments, although in some certain circumstances a new isotherm will need to be re-derived to match the field return concentration profile, once the treatment is deployed and samples are collected to measure SI return concentration. This new isotherm is used to design the next treatment. The objective of this manuscript is to quantify the uncertainty, which depends of the number of samples analyzed. In any inverse problem, there might not be a unique solution, which is in our context a pseudo-isotherm matching the return concentration profile. As a consequence, there will be a certain level of uncertainty predicting the next squeeze treatment lifetime. Solving this inverse problem in Bayesian formulation, incorporating the prior information, and the likelihood involving the return concentration profile, it is possible to quantify the posterior distribution, and therefore calculate the uncertainty range, commonly known as P90/P50/P10, based on the Randomized Maximum Likelihood (RML) approach. The P90/P50/P10 was calculated as a function of the number of samples available, differentiating from the early production and late production. The results suggest that there is a correlation between the P90/P50/P10 interval and the number of samples, i.e. the differences between the P10 and P90 in terms of the forecast squeeze lifetime was wider the smaller number of samples. The methodology proposed may be used to determine the number of samples required to reduce the level of uncertainty predicting the lifetime of the next squeeze treatment. Although taking more samples may increase the cost per barrel for a treatment, the ability to predict accurately treatment lifetime will be more cost effective in the long term, as production might not be affected.