He, Youwei (China University of Petroleum, Beijing) | Cheng, Shiqing (China University of Petroleum, Beijing) | Chai, Zhi (Texas A&M University) | Patil, Shirish (King Fahd University of Petroleum and Minerals) | Rui, Ray (Massachusetts Institute of Technology) | Yu, Haiyang (China University of Petroleum, Beijing)
Applications of cluster wells and hydraulic fracturing enable commercial productivity from unconventional reservoirs. However, well productivity decrease rapidly for this type of reservoirs, and in many cases, it is difficult to maintain a productivity that is economical. Enhanced oil recovery (EOR) is therefore needed to improve well performance. Traditional fluid injection from other wells are not feasible due to the ultra-low permeability, and fluid Huff-n-Puff also fails to meet the expected recovery. This work investigates the feasibility of the inter-fracture injection and production (IFIP) approach to increase oil production of multiple multi-fractured horizontal wells (MFHW).
Three MFHWs are considered in a cluster well. Each MFHW includes injection fractures (IFs) and recovery fractures (RFs). The fractures with even and odd indexes are assigned to be IFs or RFs, respectively. The injection/production schedule falls into two categories: synchronous inter-fracture injection and production (s-IFIP) and asynchronous inter-fracture injection and production (a-IFIP). To analyze the well performance of multiple MFHWs using the IFIP method, this work performs numerical simulation based on the compartmental embedded discrete fracture model (cEDFM) and compares the production performance of three MFHWs using four different producing methods (i.e., primary depletion, CO2 Huff-n-Puff, s-IFIP, and a-IFIP). Although the number of producing fractures is reduced by about 50% for s-IFIP and a-IFIP, they achieve much higher oil rates than primary depletion and CO2 Huff-n-Puff. Sensitivity analysis is performed to investigate the impact of parameters on the IFIP. The fracture spacing between IFs and RFs, CO2 injection rates, and connectivity of fracture networks affect the oil production significantly, followed by length of RFs, well spacing among MFHWs and length of IFs. The suggested well completion scheme is presented for the a-IFIP and s-IFIP methods. This work demonstrates the ability of the IFIP method in enhancing oil production of multiple MFHWs in unconventional reservoirs.
Abdelgawad, Khaled (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Patil, Shirish (King Fahd University of Petroleum & Minerals)
Barium Sulfate (Barite) is one of the common oil and gas field scales formed inside the production equipment and in the reservoir. Barite is also a common weighting material used during drilling oil and gas wells. Barium sulfate scale may exist as well in carbonate formations. The removal of barium sulfate from calcium carbonate formation is a challenging problem because of the solubility of calcium carbonate is higher compared to that of barium sulfate in different acids. In addition, barium sulfate is not soluble in the regular acids such as hydrochloric (HCl) acid and other organic acids.
In this paper, the effect of calcium carbonate on barium sulfate solubility in a chelating agent and converter catalyst was investigated using solubility experiments at 80°C as a function of time. 20 wt.% DTPA with 6 wt.% potassium carbonate (converter) were used at pH of 12. The effect of calcium chelation on the barium sulfate solubility was studied in two scenarios. The first scenario when Barium sulfate is dissolved first then the solution reacts with calcium carbonate. The second scenario when both calcium carbonate and barium sulfate are exposed to the DTPA solution at the same time. In addition, the effect of calcium carbonate loading on the barium sulfate solubility was determined using 25, 50, 75, and 100 wt.% of the scale as calcium carbonate. As an evaluation criterion, inductively coupled plasma (ICP) was used to analyze the cation concentration and determine the solubility of each scale type.
For the two scenarios of barium sulfate dissolution, the presence of calcium carbonate had a significant effect on the solubility of barium sulfate. When DTPA solution got saturated first with barium cations after 24 hours, and the addition of calcium carbonate to the solution will cause immediate barium drop of solution (concentration drop from 2140 to 1984 ppm in 30 min in 50 ml solution) which cause precipitation of barium sulfate. In addition, simultaneous chelation of both calcium carbonate and barium sulfate showed a low barium sulfate solubility compared to calcium carbonate. This can be explained by the high affinity of DTPA to calcium compared to barium.
It is highly recommended to account for the presence of any calcium source during the design of the chemical formulation for barium sulfate scale removal using DTPA. Therefore, DTPA treatment formulation is recommended in sandstone formations. Field results can be completely different from laboratory results if Ca2+ chelation from carbonate rocks is ignored.
Al-Amoudi, Luai Ali (Hadhramout University) | Ba geri, Badr Salem (King Fahd University of Petroleum & Minerals, Hadhramout University) | Patil, Shirish (King Fahd University of Petroleum & Minerals) | Baarimah, Salem Obaid (Hadhramout University)
Crude oil viscosity is a significant parameter for the fluid flow in both porous media and pipe lines. Therefore, it has to be determined using highly accurate methods. Oil viscosity is usually predicted with the correlations obtained from the laboratory measured data. However, some of the presented correlations have very complicated assumptions which make them very difficult to apply in most of the case studies reported. On the other hand, simplified correlations companies the accuracy.
The present work in this paper studies predictive capabilities of Artificial Intelligence (AI) to estimate the oil viscosity. Artificial Neural Network (ANN) models are proposed to predict the undersaturated, saturated and dead oil viscosity in Yemeni fields. A data set consisting 545 of laboratory measurements on oil samples was gathered from different oil fields in Yemen. 70% of the data points were used to train the proposed ANN models while the remaining data set was tested the model performance. The performance of the ANN methods was compared with some of the conventional correlations such as (Beal's correlation, Khan's correlation, Kartoatmodjo and Schmidt correlation, Vasquez-Begg's correlation, Chew and Connaly correlation, Beggs and Robinson correlation, Elsharqawy correlation and Glaso's correlation).
The result of this study shows the superiority of the Artificial Neural Network (ANN) models over the current models for predicting oil viscosity from PVT data. The comparative results displayed that the proposed ANN models performed better with higher accuracy than those obtained with published correlations.
During waterflooding, pore-throat structure of the porous media in the reservoir changes continually, which causes the great challenge in reservoir modeling and simulation. However, through the evolution mechanism of pore-throat characteristics for the reservoir during waterflooding has been intensively investigated in the past several decades, the essential controls on pore-throat structure evolution of reservoir rocks are not studied much. It is of theoretical and practical significance to use analytical methods to study the evolution of pore-throat characteristics of porous media during waterflooding. However, because of the disordered and extremely complicated microstructures of porous media, the theoretical model for stress sensitivity is scarce. The objective of this work is to establish a novel and reasonable quantitative model to determine the essential controls on pore-throat structure evolution of reservoir rocks. The theoretical model is derived from the fractal geometry. The predictions from the proposed model agree well with the available experimental data presented in the literature, which verified the novel quantitative model. There is no empirical constant and every parameter in the model has specific physical significance. In addition, the evolution rule for the pore-throat structure parameters has been obtained. The results show that the pore-throat structure of porous media becomes more complex and more heterogeneous after waterflooding. The pore-throat parameters (e.g. porosity, permeability, the maximum pore-throat radius, average pore-throat radius and sorting coefficient, etc.) will change during waterflooding. This work presents accurate and fast analytical models to perform the evolution rule of pore-throat characteristics of porous media during waterflooding. The proposed models can reveal more mechanisms that affect the coupled flow deformation behavior in porous media.
Moussa, Tamer (King Fahd University of Petroleum and Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum and Minerals) | Patil, Shirish (King Fahd University of Petroleum and Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum and Minerals) | Abdelgawad, Khaled (King Fahd University of Petroleum and Minerals)
Thermal recovery methods are viable and commonly used to recover heavy oil reservoirs by reducing oil viscosity and improving oil displacement. However, there are many challenges associated with conventional steam injection methods. These challenges include the significant heat energy losses before steam reaches the reservoir, the high cost of steam generation and injection, as well as the emission of greenhouse gases. Therefore, it is essential to introduce a heavy oil recovery approach in which steam can be generated downhole to overcome these challenges associated with conventional steam injection methods. However, this novel heavy-oil recovery method has several designs and operational parameters that must be efficiently optimized, to achieve maximum recovery from heavy-oil reservoirs with less cost and minimum environmental impact.
The objective of this work is to introduce a novel heavy-oil recovery technique using in-situ steam generated by downhole thermochemical reactions and investigate the key design and operational parameters of this complex recovery process. Modified self-adaptive differential evolution (MSaDE) and particle swarm optimization (PSO) methods are used in this work as global optimizer to find the optimum design and operation parameters to achieve the maximum net present value (NPV) and highest oil recovery (RF) of a heavy-oil reservoir after ten years of development. Comparison of the two proposed optimization methods is introduced as well.
The results show that downhole thermochemical reactions can be used to generate in-situ steam, to efficiently reduce the heavy-oil viscosity and improve oil mobility. It has also shown that utilizing MSaDE and PSO methods to optimize the key components of this novel recovery process, significantly enhanced the recovery performance, in terms of higher NPV and RF.
This study provides the first known in-depth optimization and uncertainty analysis to outline the significance of each design and operation parameter of the proposed novel thermochemical recovery process. This work showed and verified the concept of using downhole thermochemical reactions as an environmental-friendly solution to recover oil from heavy-oil reservoirs and is considered as a step forward to eliminate the greenhouse gases emission related to thermal recovery methods.
Moussa, Tamer (King Fahd University of Petroleum and Minerals) | Patil, Shirish (King Fahd University of Petroleum and Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum and Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum and Minerals)
Determination of optimal well locations plays an important role in the efficient recovery of hydrocarbon resources. However, it is a challenging and complex task because it relies on reservoir, and fluid and economic variables that are often nonlinearly correlated. Traditionally, well placement optimization (WPO) has been done through experience and use of quality maps. However, reservoir management teams are beginning to appreciate the use of automatic optimization tools for well placement that will yield the largest financial returns or highest net present value (NPV). In addition, the performance of a reservoir is time and process dependent, therefore well placement decisions cannot be based on static properties alone. On the other hand, well placement optimization requires a large number of simulator runs in an iterative process, and thus several runs to reach the maximum achievable NPV. Therefore, there is a real need for automatic well placement approach that uses highly efficient optimization method, which can improve the result quality, speed of the convergence process to optimal result and thus decrease the time required for computation.
The objective of this work is to determine the optimal well locations in a heavy oil reservoir under production using a novel recovery process, in which steam is generated, in-situ, using thermochemical reactions. Self-adaptive differential evolution (SaDE) and particle swarm optimization (PSO) methods are used as the global optimizer to find the optimal configuration of wells that will yield the highest NPV. Comparison analysis between the two proposed optimization techniques is introduced. The CMG STARS Simulator is utilized in this research to simulate reservoir models with different well configurations.
Comparison of results is made between the NPV achieved by the well configuration proposed by the SaDE and PSO methods. The results show that SaDE performed better than PSO in terms of higher NPV after ten years of production while under in-situ steam injection process using thermochemical reactions. This is the first known application where SaDE and PSO methods are used to optimize well locations in a heavy oil reservoir that is recovered by injecting steam generated in-situ using thermo-chemical reactions. This research shows the importance of well placement optimization in a highly promising and novel heavy oil recovery process. This also is a step forward in the direction to eliminate the CO2 emissions related to thermal recovery processes.
Heavy oil has attracted global attention due to its huge volume of original oil in place. However, there are numerous operation and economic challenges to the recovery of heavy oil due to its high-viscosity and high-density. Thermal recovery methods such as steam injection is viable and commonly used to recover heavy oil and bitumen primarily by viscosity reduction of heavy oil and improving the displacement of the heavy oil. However, there are significant heat losses before the steam reaches the heavy-oil reservoir, in addition to the concerns of high cost and emission of greenhouse gases. One of the promising new heavy-oil recovery approaches is generating steam with nitrogen gas, in-situ, using thermochemical reaction to reduce oil viscosity, improve the mobility ratio and enhance the heavy-oil displacement. Steam and nitrogen are generated, in-situ, by injecting exothermic reactants downhole with the injected water to create heat and enhance reservoir pressure for mobilizing heavy oil. The exothermic reaction is triggered by either increasing downhole temperature or in the presence of a low pH weak acid.
In this research, a numerical study of the novel heavy oil recovery process using in-situ steam and nitrogen generated by thermochemical reactions in field scale is conducted. Various ratios of nitrogen- steam are studied to identify their effect on the recovery efficiency. In-situ Nitrogen-steam ratio generated by thermochemical is optimized to accomplish the maximum achievable net present value (NPV) after ten years of recovery. The CMG STARS simulator is used to simulate reservoir models with different operational parameters.
The results show that the generated heat from in-situ thermochemical reactions was sufficient to reduce the viscosity of heavy oil, while the generated nitrogen gas provided a good heat insulation effect and reduced steam-oil ratio. Thus, higher NPV was achieved than typical conventional steam-only injection method.
This is the first time NPV and all economic parameters are considered to analyze the performance of an in-situ steam and nitrogen generated by thermochemical reaction. This research shows that the recovery of the proposed method is more suitable and economical for the reservoirs which are not viable for conventional steam flooding methods and it is a step forward to eliminate CO2 emissions associated with thermal recovery process.
Abdelgawad, Khaled (King Fahd University of Petroleum and Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum and Minerals) | Mousa, Tamer (King Fahd University of Petroleum and Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum and Minerals) | Patil, Shirish (King Fahd University of Petroleum and Minerals)
Measuring the rheological properties during the drilling operation is time-consuming and usually, these properties are measured twice a day. The rheological properties play a key factor in controlling the drilling operation. The knowledge of these properties are very important for hydraulic calculations which are required for hole cleaning optimization, surge and swab pressure calculations and others. Wrong estimation of these properties may lead to big disaster during the drilling operation such as pipe sticking, loss of circulation, and/or well control issues. The aforementioned problems will increase the non-productive time and hence the overall cost of the drilling operations. Artificial intelligence techniques, once they are optimized, can be used to predict the rheological properties in a real time.
The main idea of this paper is to use the frequent measurements of the mud density, Marsh funnel viscosity and solid percent, which are measured every 15-20 minutes and build new artificial intelligent models for plastic viscosity, yield point, apparent viscosity, and flow behavior index. Different AI techniques were evaluated such as; artificial neural network, support vector machine (SVM) and adaptive-network-based fuzzy inference system (ANFIS). The model which yielded the highest accuracy (lower average absolute error (AAPE) and highest correlation coefficient (R)) was used to develop new empirical correlations for each rheological properties. For the first time, the artificial intelligence techniques were combined with the self-adaptive differential evolution algorithm to optimize the best combination of the AI parameters.
The results obtained showed that ANN is the best AI technique to predict the rheological properties from the mud density, Marsh funnel viscosity, and solid percent. It is very important to combine the self-adaptive differential evolution with the artificial neural network to predict the rheological properties with high accuracy (AAPE less than 5% and R greater than 95%). The ANN black box was converted to a white box by extracting the weights and biases of the optimized SaDe-ANN model for each rheological parameter and a new empirical correlation was developed. The developed technique will help the drilling engineer to predict the rheological properties every 15 to 20 minutes and this will help in hole cleaning optimization and avoid most of the drilling problems such as pipe sticking and loss of circulation. The developed correlation can be used without the need for the ANN model and can apply using any software. No additional equipment or especial software is required for applying the new method.
Zhang, Yin (University of Alaska Fairbanks) | Yang, Daoyong (Chenglin Hi-Tech Industry Co., Ltd. and University of Regina) | Li, Heng (Computer Modelling Group Ltd.) | Patil, Shirish (University of Alaska Fairbanks)
A damped iterative ensemble Kalman filter (IEnKF) algorithm has been proposed to estimate relative permeability and capillary pressure curves simultaneously for the PUNQ-S3 model, while its performance has been compared with that of the CMOST module. The power-law model is employed to represent the relative permeability and capillary pressure curves, while three-phase relative permeability for oil phase is determined by using the modified Stone II model. By assimilating the observed production data, the relative permeability and capillary pressure curves are inversely, automatically, and successively updated, achieving an excellent agreement with the reference cases. Not only are the associated uncertainties reduced significantly during the updating process, but also each of the updated reservoir models predicts the production profile that is in a good agreement with the reference cases. Although both the damped IEnKF and CMOST generate similar history matching results and prediction performance, the estimation accuracy of the damped IEnKF method developed in this study is generally much better than that of the CMOST. Besides, the variations in the ensemble of the updated reservoir models and production profiles of the damped IEnKF provide a robust and consistent framework for uncertainty analysis.
Oskui, Gh. Reza P. (Petroleum Research and Studies Center (PRSC), Kuwait Institute for Scientific Research (KISR)) | Jumaa, Mohammad A. (Petroleum Research and Studies Center (PRSC), Kuwait Institute for Scientific Research (KISR)) | Folad, Ebtisam G. (Petroleum Research and Studies Center (PRSC), Kuwait Institute for Scientific Research (KISR)) | Rashed, Abeer (Petroleum Research and Studies Center (PRSC), Kuwait Institute for Scientific Research (KISR)) | Patil, Shirish (Petroleum Development Laboratory, University of Alaska Fairbanks)