Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters.

Total organic carbon (TOC) content is one of the crucial parameters that determine the value of the source rock. The TOC content gives important indications about the source rocks and hydrocarbon volume. Various techniques have been utilized for TOC quantification, either by geochemical analysis of source rocks in laboratories or using well logs to develop mathematical correlations and advanced machine learning models. Laboratory methods require intense sampling intervals to have an accurate understanding of the reservoir, and depending on the thickness of the interested formation, it can be time-consuming and costly. Empirical correlations based on well logs (e.g., density, sonic, gamma ray, and resistivity) showed fast predictions and very reasonable accuracies. However, other important parameters such as thermal neutron logs have not been studied yet as a potential input for providing reliable TOC predictions. Also, different studies estimate the TOC based on the well-logging data for various formations; however, limited studies were reported to predict the TOC for the Horn River Formation. Therefore, the objective of this study is to estimate the TOC variations based on the thermal neutron logs using one of the largest source rocks in Canada: The Horn River Formation. More than 150 data sets were collected and used in this work. The parameters of the artificial neural network (ANN) model were fine-tuned in order to improve the model's prediction performance. Furthermore, an empirical correlation was developed utilizing the optimized ANN model to allow fast and direct application for the developed model. The developed correlation can predict the TOC with an average absolute error of 0.52 wt %. The proposed TOC model was able to outperform the previous models, and the coefficient of determination was increased from 0.28 to 0.73. Overall, the proposed TOC model can provide high accuracy for TOC ranges from 0.3 to 6.44 wt %. The developed model can provide a real-time quantification for the organic matter maturity, helping to allocate the zones of mature organic matter within the drilled formations.

Carbonate Stimulation Using Chelating Agents: Improving the Treatment Performance by Optimizing the Fluid Properties.

Chelating agents' solutions were introduced as effective alternatives to strong acids to be used in acid-sensitive situations such as high temperature and salinity conditions. However, limited studies have been conducted to examine the optimum conditions for improving the chelating agent performance. In this study, a comprehensive study of solubility and physical properties of different chelating agents' fluids that are commonly used in the oil upstream applications was performed under different conditions. The optimum concentration ranges at which chelating agents are soluble and effective to provide the best acidizing efficiency are determined. Also, more than 340 data sets were used to develop new empirical models that can help in estimating the chelating agents' properties at wide ranges of concentrations and treatment temperatures. In this work, different experimental measurements were conducted using a pressure of 2000 psi (13.7 MPa) and a temperature of 120 °C (393.15 K). The conducted experiments are density and viscosity measurements, solubility experiments, interfacial tension measurements, computed tomography scan, and coreflooding tests. The used chelating agents are diethylenetriaminepentaacetic acid (DTPA), hydroxyethylenediaminetriacetic acid (HEDTA), and ethylenediaminetetraacetic acid (EDTA). Results revealed that HEDTA and DTPA chelating agents have good solubility at different pH and concentration ranges. However, EDTA showed a limited solubility performance, especially at a concentration greater than 15 wt %. Moreover, the developed correlations provided fast and reliable estimations for the chelating agent density and viscosity, and estimation errors of around 1% were achieved. Also, treating the tight carbonate rocks with the optimized chelating agent solutions showed effective wormholes with a minimum acid volume. Finally, a good match between the actual and predicted pressure drops is achieved, confirming the high reliability of the developed models. Overall, this work can help in designing the stimulation treatment by suggesting the optimum ranges for fluid concentration and solution pH for wide ranges of temperature. Also, the newly developed correlations can be used to provide quick and reliable estimations for the pressure drop and the chelating agent properties at reservoir conditions.

Enhancing the Oil Recovery from Naturally Fractured Reservoirs Using Viscoelastic Surfactant (VES) Flooding: A Field-Scale Simulation.

A viscoelastic surfactant (VES) has the combined properties of a surfactant and a polymer. Injection of VES fluids into naturally fractured reservoirs (NFRs) can control the mobility of the injected fluid and enhance the total oil recovery. This paper presents a field-scale simulation to evaluate the performance of a noble VES fluid in enhancing the oil recovery from a naturally fractured reservoir. In this work, the results of coreflooding, computerized tomography (CT)-scan, rheology, interfacial tension (IFT), and adsorption measurements were used to build and calibrate a lab-scale model. Thereafter, a chemical enhanced oil recovery (EOR) modeling simulator developed by a computer modeling group (CMG-STARS) was used to build a field-scale simulation. Real seismic data, permeability and porosity distributions, and operating conditions were utilized to develop and evaluate the simulation model. The results show that VES can outperform the surfactant-polymer (SP) flooding and waterflooding in NFRs; VES improved the oil recovery by 10% and reduced the water cut by 47%, at the same conditions. VES reduced the IFT by two orders of magnitude (100 times) compared to waterflooding. Also, VES altered the rock wettability to a more water-wet status, leading to reduce the relative permeability to water (K rw) by a factor of 10, on average. Finally, the simulation study indicated that applying waterflooding after VES flooding leads to a minor increase in the oil recovery. Overall, this study provides a detailed comparison between VES flooding, SP flooding, and conventional waterflooding in NFRs. Sensitivity analysis was performed to study the impact of treatment parameters on the oil recovery from naturally fractured reservoirs. Using actual NFR data, the optimum VES flooding was determined, which will help in conducting VES flooding for real EOR operations.

Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination.

The accurate evaluation of groundwater contamination vulnerability is essential for the management and prevention of groundwater contamination in the watershed. In this study, advanced multiple machine learning (ML) models of Radial Basis Neural Networks (RBNN), Support Vector Regression (SVR), and ensemble Random Forest Regression (RFR) were applied to determine the most accurate performance for the evaluation of groundwater contamination vulnerability. Eight vulnerability factors of DRASTIC-L were rated based on the modified DRASTIC model (MDM) and were used as input data. The adjusted vulnerability index (AVI) with nitrate values was used as output data for the modeling process. The performance of three models was verified using the statistical performance criteria of MAE, RMSE, r2, and ROC/AUC values. The ensemble RFR model showed the highest performance in comparison with standalone SVR and RBNN models. Specifically, ensemble RFR kept all promising solutions during the model performance due to its flexibility and robustness, and the vulnerability map obtained by the RFR model was more accurate for predicting the most vulnerable areas to contamination. It was concluded that ensemble RFR was a robust tool to enhance the evaluation of groundwater contamination vulnerability, and that it could contribute to environmental safety against groundwater contamination.

An Artificial Intelligence-Based Model for Performance Prediction of Acid Fracturing in Naturally Fractured Reservoirs.

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.

Novel Treatment for Mitigating Condensate Bank Using a Newly Synthesized Gemini Surfactant.

Condensate accumulation in the vicinity of the gas well is known to curtail hydrocarbon production by up to 80%. Numerous approaches are being employed to mitigate condensate damage and improve gas productivity. Chemical treatment, gas recycling, and hydraulic fracturing are the most effective techniques for combatting the condensate bank. However, the gas injection technique showed temporary condensate recovery and limited improvement in gas productivity. Hydraulic fracturing is considered to be an expensive approach for treating condensate banking problems. In this study, a newly synthesized gemini surfactant (GS) was developed to prevent the formation of condensate blockage in the gas condensate reservoirs. Flushing the near-wellbore area with GS will change the rock wettability and thereby reduce the capillary forces holding the condensate due to the strong adsorption capacity of GS on the rock surface. In this study, several measurements were conducted to assess the performance of GS in mitigating the condensate bank including coreflood, relative permeability, phase behavior, and nuclear magnetic resonance (NMR) measurements. The results show that GS can reduce the capillary pressure by as much as 40%, increase the condensate mobility by more than 80%, and thereby mitigate the condensate bank by up to 84%. Phase behavior measurements indicate that adding GS to the oil-brine system could not induce any emulsions at different salinity levels. Moreover, NMR and permeability measurements reveal that the gemini surfactant has no effect on the pore system and no changes were observed in the T2 relaxation profiles with and without the GS injection. Ultimately, this work introduces a novel and effective treatment for mitigating the condensate bank. The new treatment showed an attractive performance in reducing liquid saturation and increasing the condensate relative permeability.

A Novel Approach to Improve Acid Diversion in Carbonate Rocks Using Thermochemical Fluids: Experimental and Numerical Study.

The distribution of acid over all layers of interest is a critical measure of matrix acidizing efficiency. Chemical and mechanical techniques have been widely adapted for enhancing acid diversion. However, it was demonstrated that these often impact the formation with damage after the acid job is completed. This study introduces, for the first time, a novel solution to improve acid diversion using thermochemical fluids. This method involves generating nitrogen gas at the downhole condition, where the generated gas will contribute in diverting the injected acids into low-permeability formations. In this work, both lab-scale numerical and field-scale analytical models were developed to evaluate the performance of the proposed technique. In addition, experimental measurements were carried out in order to demonstrate the application of thermochemical in improving the acid diversion. The results showed that a thermochemical approach has an effective performance in diverting the injected acids into low-permeability rocks. After treatment, continuous wormholes were generated in the high-permeability rocks as well as in low-permeability rocks. The lab-scale model was able to replicate the wormholing impact observed in the lab. In addition, alternating injection of thermochemical and acid fluids reduced the acid volume 3.6 times compared to the single stage of thermochemical injection. Finally, sensitivity analysis indicates that the formation porosity and permeability have major impacts on the acidizing treatment, while the formations pressures have minor effect on the diversion performance.

Novel Approach for Improving the Flow of Waxy Crude Oil Using Thermochemical Fluids: Experimental and Simulation Study.

This article focuses on the flow assurance of waxy crude oil using an environmentally benign and cost-effective approach involving thermochemical reaction. The study incorporates experimental and simulation works to evaluate heat and pressure generation potentials and heat transfer efficiency of the thermochemical fluids. Experimental results reveal that at the concentration (1 M) of thermochemical fluid (TCF) ranging between 14 and 33% v/wt of the waxy oil, sufficient heat could be generated to raise the temperature of the oil significantly above the pour point (48 °C). In addition, from the bench-top treatment of a damaged tubing, it was observed that more than 95% of the deposited wax could be removed using the thermochemical solutions. Subsequently, a large-scale application of the technology in a long-distance flow assurance of waxy crude oil was confirmed through process simulation. Ultimately, the simulation results revealed the capacity of the method to improve the temperature and pressure profiles of the pipe flow system, and most significantly, to remove wax deposition up to 98%.

New Chemical Treatment for Permanent Removal of Condensate Banking from Different Gas Reservoirs.

Condensate banking represents a challenging problem in producing the hydrocarbon from tight gas reservoirs. The accumulation of liquid condensates around the production well can significantly impair the gas flow rate. Gas injection and hydraulic fracturing are the common techniques used to avoid the condensate development by maintaining the reservoir pressure above the dew point curve. However, these treatments are associated with high operational costs and large initial investment. This study presents a new chemical treatment for removing the condensate banking using thermochemical solutions. The presented treatment can cause a permanent impact on the treated formations. Chemicals are injected to react downhole and generate in situ pressure and heat in certain conditions. The generated pressure can raise the gas pressure above the dew point, and the generated heat can change the phase of the liquid condensate to gas. Kinetic analysis indicates that thermochemical fluids can increase the temperature and heat by 85 °C and 369 kJ/mol, respectively. In addition, the impact of clay content on the efficiency of thermochemical treatment was studied using coreflooding experiments. A condensate removal of more than 60% was achieved using the huff and puff injection mode. A good correlation between the rock permeability and the condensate removal efficiency was observed. Higher condensate removal was obtained for the rock samples with high permeability values. Moreover, the presence of clay minerals in the treated rock showed a minor impact on the condensate removal, indicating that the injected chemicals are able to stabilize the clay minerals and avoid clay damage. This research shows that thermochemical treatment can remove more than 60% of the condensate damage for different types of tight sandstones. Huff and puff treatment was found to be a very practical approach to diminish the condensate banking from different sandstone rocks. Also, this work confirms that thermochemical treatment can be applied in the clayey formation for removing the condensate blockage without affecting the clay stability or inducing clay damage. Ultimately, this study introduces a new chemical treatment in the gas industry, and the used chemicals are effective, environmentally friendly, and not expensive.