Injection of CO₂ to enhance oil recovery is widely used due to its multiple advantages such as mobilizing the oil and sequestration of carbon dioxide. Injection of CO₂ can enhance oil recovery by reducing oil viscosity and improving overall fluid mobility. However, several problems are associated with CO₂ injection such as viscous fingering, gravity override, and CO₂ channeling that results in early gas breakthrough, low sweep efficiency, and low ultimate oil recovery. In this study, dual benefits of CO₂ injection are presented: enhancing oil recovery and sequestering carbon dioxide. In this work, different scenarios of field scale simulation were conducted to evaluate oil recovery during CO₂ injection, and the CMG (Computer Modeling Group) software package was used. Three main scenarios were examined which are CO₂ injection into the reservoir, CO₂ injection into the aquifer, and CO₂ injection into the aquifer followed by waterflooding. Also, three well configurations were utilized—all injectors and producers are drilled vertically, all wells are drilled horizontally, and vertical injectors and horizontal producers are used. Therefore, the oil recovery profiles were examined for nine scenarios over a 20-year period. In all simulated models, CO₂ injection was started at the residual oil saturation (S_or) conditions, to represent the cases of depleted oil reservoirs. The results indicated that the highest oil recovery of 73% of the original oil-in-place (OOIP) can be achieved by injecting CO₂ into the reservoir, utilizing vertical injectors and producers. While injecting CO₂ into aquifers can significantly enhance oil recovery by around 68–70% of the OOIP, using horizontal wells can provide more oil recovery (67.7%) than that using vertical wells (54.8%), in the same conditions. Moreover, around 7,928 tons of carbon dioxide can be sequestered in underground formations, on average. Finally, CO₂ injection outperformed the conventional waterflooding, where 68 and 12% of the OOIP were obtained, respectively. Overall, injection of CO₂ into the depleted reservoir can provide dual benefits of CO₂ sequestration and improved oil recovery. CO₂ can be injected into the water zone resulting in a slow release of CO₂ which will reduce the fluid viscosity, enhance oil recovery, and reduce the greenhouse effect.

Carbon dioxide (CO₂) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO₂ flooding. Several methods are used to determine the MMP, including slim tube tests, analytical models and empirical correlations. However, the experimental measurements are costly and time-consuming, and the mathematical models might lead to significant estimation errors. This paper presents a new approach for determining the MMP during CO₂ flooding using artificial intelligent (AI) methods. In this work, reliable models are developed for calculating the minimum miscibility pressure of carbon dioxide (CO₂-MMP). Actual field data were collected; 105 case studies of CO₂ flooding in anisotropic and heterogeneous reservoirs were used to build and evaluate the developed models. The CO₂-MMP is determined based on the hydrocarbon compositions, reservoir conditions and the volume of injected CO₂. An artificial neural network, radial basis function, generalized neural network and fuzzy logic system were used to predict the CO₂-MMP. The models’ reliability was compared with common determination methods; the developed models outperform the current CO₂-MMP methods. The presented models showed a very acceptable performance: the absolute error was 6.6% and the correlation coefficient was 0.98. The developed models can minimize the time and cost of determining the CO₂-MMP. Ultimately, this work will improve the design of CO₂ flooding operations by providing a reliable value for the CO₂-MMP.

Acid fracturing is one of the most effective techniques for improving the productivity of naturally fractured carbonate reservoirs. Natural fractures (NFs) significantly affect the design and performance of acid fracturing treatments. However, few models have considered the impact of NFs on acid fracturing treatments. This study presents a simple and computationally efficient model for evaluating acid fracturing efficiency in naturally fractured reservoirs using artificial intelligence-based techniques. In this work, the productivity enhancement due to acid fracturing is determined by considering the complex interactions between natural and hydraulic fractures. Several artificial intelligence (AI) techniques were examined to develop a reliable predictive model. An artificial neural network (ANN), a fuzzy logic (FL) system, and a support vector machine (SVM) were used. The developed model predicts the productivity improvement based on reservoir permeability and geomechanical properties (e.g., Young’s modulus and closure stress), natural fracture properties, and design conditions (i.e., acid injection rate, acid concentration, treatment volume, and acid types). Also, several evaluation indices were used to evaluate the model reliability including the correlation coefficient, average absolute percentage error, and average absolute deviation. The AI model was trained and tested using more than 3100 scenarios for different reservoir and treatment conditions. The developed ANN model can predict the productivity improvement with a 3.13% average absolute error and a 0.98 correlation coefficient, for the testing (unseen) data sets. Moreover, an empirical equation was extracted from the optimized ANN model to provide a direct estimation for productivity improvement based on the reservoir and treatment design parameters. The extracted equation was evaluated using validation data where a 4.54% average absolute error and a 0.99 correlation coefficient were achieved. The obtained results and degree of accuracy show the high reliability of the proposed model. Compared to the conventional simulators, the developed model reduces the time required for predicting the productivity improvement by more than 60-fold; therefore, it can be used on the fly to select the best design scenarios for naturally fractured formations.

Fishbone multilateral wells are applied to enhance well productivity by increasing the contact area between the bottomhole and reservoir region. Fishbone wells are characterized by reduced operational time and a competitive cost in comparison to hydraulic fracturing operations. However, limited models are reported to determine the productivity of fishbone wells. In this paper, several artificial intelligence methods were applied to estimate the performance of fishbone wells producing from a heterogeneous and anisotropic gas reservoir. The well productivity was determined using an artificial neural network, a fuzzy logic system and a radial basis network. The models were developed and validated utilizing 250 data sets, with the inputs being the permeability ratio (K_h/K_v), flowing bottomhole pressure and lateral length. The results showed that the artificial intelligence models were able to predict the fishbone well productivity with an acceptable absolute error of 7.23%. Moreover, a mathematical equation was extracted from the artificial neural network, which is able to provide a simple and direct estimation of fishbone well productivity. Actual flow tests were used to evaluate the reliability of the developed model, and a very acceptable match was obtained between the predicted and actual flow rates, wherein an absolute error of 6.92% was achieved. This paper presents effective models for determining the well performance of complex multilateral wells producing from heterogeneous reservoirs. The developed models will help to reduce the uncertainty associated with numerical methods, and the extracted equation can be inserted into commercial software, thereby significantly reducing deviation between the actual data and simulated results.

In this paper, chelating agents were introduced as standalone fluids for enhancing the oil recovery from carbonate and sandstone reservoirs. Chelating agents such as glutamic acid di-acetic acid (GLDA), ethylene-diamine-tetra acetic acid (EDTA), and hydroxyl-ethylethylene-diamine-tri-acetic acid (HEDTA) were used. Chelating agents can be found in different forms such as sodium, potassium, or calcium salts. There is a significant gap in the literature about the influence of salt type on the hydrocarbon recovery from carbonate and sandstone reservoirs. In this study, the impact of the salt type of GLDA chelating agent on the oil recovery was investigated. Potassium-, sodium-, and calcium-based high-pH GLDA solutions were used. Coreflooding experiments were conducted at high-pressure high-temperature (HPHT) conditions using carbonate and sandstone cores. The used samples had porosity values of 15–18%, and permeability values were between 10 and 75 mD. Seawater was injected as a secondary recovery process. Thereafter, a GLDA solution was injected in tertiary mode, until no more oil was recovered. In addition to the recovery experiments, the collected effluent was analyzed for cations concentrations such as calcium, magnesium, and iron. Moreover, dynamic adsorption, interfacial tension, and contact angle measurements were conducted for the different forms of GLDA chelating agent solutions. The results of this study showed that incremental oil recovery between 19% and 32% of the Original Oil in Place (OOIP) can be achieved, based on the salt type and the rock lithology. Flooding carbonate rocks with the calcium-based GLDA chelating agent yielded the highest oil recovery (32% of OOIP), followed by that with potassium-based GLDA chelating agent, and the sodium-based GLDA chelating agent yielded the lowest oil recovery. The reason behind that was the adsorption of the calcium-based GLDA on the rock surface was the highest without reducing the rock permeability, which was indicated by the contact angle, dynamic adsorption, and flooding experiments. The outcome of this study will help in maximizing the oil recovery from carbonate and sandstone reservoirs by suggesting the most suitable salt type of chelating agents.

Condensate-liquid accumulation in the vicinity of a well is known to curtail gas production up to 80%. Numerous approaches are employed to mitigate condensate banking and improve gas productivity. In this work, a field-scale simulation is presented for condensate damage removal in tight reservoirs using a thermochemical treatment strategy where heat and pressure are generated in situ. The impact of thermochemical injection on the gas recovery is also elucidated. A compositional simulator was utilized to assess the effectiveness of the suggested treatment on reducing the condensate damage and, thereby, improve the gas recovery. Compared to the base case, represented by an industry-standard gas injection strategy, simulation studies suggest a significantly improved hydrocarbon recovery performance upon thermochemical treatment of the near-wellbore zone. For the scenarios investigated, the application of thermochemicals allowed for an extension of the production plateau from 104 days, as determined for the reference gas injection case, to 683 days. This represents a 6.5-fold increase in production plateau time, boosting gas recovery from 25 to 89%. The improved recovery is attributed to the reduction of both capillary pressure and condensate viscosity. The presented work is crucial for designing and implementing thermochemical treatments in tight-gas reservoirs.