The global increase in energy demand and the decreasing number of newly discovered hydrocarbon reservoirs caused by the relatively low oil price means that it is crucial to exploit existing reservoirs as efficiently as possible. Optimization of the reservoir control may increase the technical and economic efficiency of the production. In this paper, a novel algorithm that automatically determines the intelligent control maximizing the NPV of a given production process was developed. The idea is to build an auto-adaptive parameterized decision tree that replaces the arbitrarily selected limit values for the selected attributes of the decision tree with parameters. To select the optimal values of the decision tree parameters, an AI-based optimization tool called SMAC (Sequential Model-based Algorithm Configuration) was used. In each iteration, the generated control sequence is introduced into the reservoir simulator to compute the NVP, which is then utilized by the SMAC tool to vary the limit values to generate a better control sequence, which leads to an improved NPV. A new tool connecting the parameterized decision tree with the reservoir simulator and the optimization tool was developed. Its application on a simulation model of a real reservoir for which the CCS-EOR process was considered allowed oil production to be increased by 3.5% during the CO₂-EOR phase, reducing the amount of carbon dioxide injected at that time by 16%. Hence, the created tool allowed revenue to be increased by 49%.

Oily brine from the gas and oil industries remains the most difficult wastewater to treat due to its complex chemical composition, which includes aromatic hydrocarbons. Even at low concentrations, the presence of BTX (benzene, toluene, xylenes) can be extremely harmful to aquatic ecosystems. Fixed-bed adsorption columns are recommended for oily water treatment due to their flexibility and easy operation. In this research, pine sawdust modified with polydimethysiloxane (PDMS) and hydrophobic nanosilica was applied as a sorbent in a filtration system. The surface modification of raw fiber allowed to change its morphology and increase the roughness of it. The Yoon–Nelson, Bohart–Adams, Clark, and Belter models were applied to simulate continuous biosorption. The Bohart–Adams model strongly correlated with the experimental data and described the whole dynamic behavior of the column. The effect of feed flow rate (10–50 mL/min) on breakthrough characteristics was determined. Both the breakthrough and saturation time decreased as the flow rate increased. This study indicated that hydrophobized pine sawdust is an effective low-cost potential biosorbent for the removal of BTX fraction from produced water in continuous column mode.

Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm’s efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir.

One of the possibilities to reduce carbon dioxide emissions is the use of the CCS method, which consists of CO₂ separation, transport and injection of carbon dioxide into geological structures such as depleted oil fields for its long-term storage. The combination of the advanced oil production method involving the injection of carbon dioxide into the reservoir (CO₂-EOR) with its geological sequestration (CCS) is the CCS-EOR process. To achieve the best ecological effect, it is important to maximize the storage capacity for CO₂ injected in the CCS phase. To achieve this state, it is necessary to maximize recovery factor of the reservoir during the CO₂-EOR phase. For this purpose, it is important to choose the best location of CO₂ injection wells. In this work, a new algorithm to optimize the location of carbon dioxide injection wells is developed. It is based on two key reservoir properties, i.e., porosity and permeability. The developed optimization procedure was tested on an exemplary oil field simulation model. The obtained results were compared with the option of arbitrary selection of injection well locations, which confirmed both the legitimacy of using well location optimization and the effectiveness of the developed optimization method.