Al-Nakhli, Ayman (Saudi Aramco) | Tariq, Zeeshan (King Fahd University of Petroleum and Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum and Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum and Minerals) | Al-Shehri, Dhafer (King Fahd University of Petroleum and Minerals) | Murtaza, Mobeen (King Fahd University of Petroleum and Minerals)
Recent rise in global warming and fluctuations in world economy needs the best engineering designs to extract hydrocarbons from unconventional resources. Unconventional resources mostly found in over-pressured and deep formations, where the host rock has very high strength and integrity. Fracturing techniques becomes very challenging when implemented in these types of rocks, and in many cases approached to the maximum operational limits without generating any fracture. This leaves a small operational window to initiate and place the hydraulic fractures. Current stimulation methods to fracture these formations involve with adverse environmental effects and high costs due to the entailment of water mixed with huge volumes of chemicals such as biocides, scale inhibitors, polymers, friction reducers, rheology modifiers, corrosion inhibitors, and many more.
In this study, a novel environmentally friendly approach to reduce the breakdown pressure of the unconventional rock is presented. The new approach makes it possible to fracture the high strength rocks more economically and in more environmentally friendly way. The new method incorporates the injection of chemical free fracturing fluid in a series of cycles with a progressive increase of pressure in every cycle. This will allow stress relaxation at the fracture tip and correspondingly enough time for fracturing fluid to infiltrate deep inside the rock sample and weaken the rock matrix. As a result of which the tensile strength-ultimately the breakdown pressure of the rock gets reduced. The present study is carried out on different cement blocks.
The post treatment experimental analysis confirmed the success of cyclic fracturing treatment. The results of this study showed that the newly formulated method of cyclic injection can reduce the breakdown pressure by up to 24% of the original value. This reduction in breakdown pressure helped to overcome the operational limits in the field and makes the fracturing operation greener.
Kalam, Shams (King Fahd University of Petroleum & Minerals) | Khan, Mohammad Rasheed (King Fahd University of Petroleum & Minerals) | Tariq, Zeeshan (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Dew point is an important thermodynamic parameter for a gas condensate reservoir and has a very complicated nature due to its reliance on the composition of the mixture. For accurate prediction of this property, it is imperative to develop accurate models that are not computationally expensive. Currently, there exists various methodologies to estimate dew point pressure at various temperatures and hydrocarbon compositions. These methods include equation of states (EOS), analytical methods, and empirical correlations. These methods, however, have limitations in terms of accuracy or computational expense. This paper proposes a new empirical correlation to predict the dew point pressure for gas condensate reservoirs utilizing computational intelligence algorithms, namely Artificial Neural Networks (ANN), Functional Networks (FN), and Support Vector Machines (SVM).
The available data set comprises of dew-point pressure, temperature, component mole fractions (C7+, CH4, N2, CO2, and H2S). This data is divided into two parts to allow for the training and development of the new model and testing/validation phase. For ANN model the weights and bias as well as the neurons in the hidden layer are tuned to result in an optimized model. In the FN model, a number of learning algorithms were tested to reach to the optimum model to get accurate results. For SVM, three main parameters were explored to develop the intelligent model which includes epsilon, kernel parameters and ‘C’.
This study has resulted in the development of an empirical equation that is able to predict the dew point pressure accurately consuming the least amount of computation time. The proposed equation can be applied for a variable range of composition and temperature/pressure conditions. This is done by incorporating the effect of composition through two equations for normal-boiling point condition as well as the critical-temperature of the mixture. Model accuracy has been validated through a comparative analysis incorporating actual experimental data from various gas-condensate reservoir samples. These data-set includes various published sources and the results of Wilson, Whitson, and EOS.
This work showcases the effectiveness of intelligent models in providing answers with the least amount of error. A comparative analysis for the various computational models is done to come up with a correlation proving accurate results for the dew point pressure. The proposed correlation can predict the output with relatively small errors.
Al-Nakhli, Ayman (Saudi Aramco) | Tariq, Zeeshan (King Fahd University of Petroleum and Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum and Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum and Minerals) | Al-Shehri, Dhafer (King Fahd University of Petroleum and Minerals)
Current global energy needs require best engineering methods to extract hydrocarbon from unconventional resources. Unconventional resources mostly found in highly stressed and deep formations, where the rock strength and integrity both are very high. The pressure at which rock fractures or simply breakdown pressure is directly correlated with the rock tensile strength and the stresses acting on them from surrounding formation. When fracturing these rocks, the hydraulic fracturing operation becomes much challenging and difficult, and in some scenarios reached to the maximum pumping capacity limits. This reduces the operational gap to create hydraulic fractures.
In the present research, a novel thermochemical fracturing approach is proposed to reduce the breakdown pressure of the high-strength rocks. The new approach not only reduces the breakdown pressure but also reduces the breakdown time and makes it possible to fracture the high strength rocks with more conductive fractures. Thermochemical fluids used can create microfractures, improves permeability, porosity, and reduces the elastic strength of the tight rocks. By creating microfractures and improving the injectivity, the required breakdown pressure can be reduced, and fractures width can be enhanced. The fracturing experiments presented in this study were conducted on different cement specimen with different cement and sand ratio mixes, corresponds to the different minerology of the rock. Similar experiments were also conducted on different rocks such as Scioto sandstone, Eagle Ford shale, and calcareous shale. Moreover, the sensitivity of the bore hole diameter in cement block samples is also presented to see the effect of thermochemical on breakdown pressure reduction.
The experiments showed the presence of micro-fractures originated from the pressure pulses raised in the thermochemical fracturing. The proposed thermochemical fracturing method resulted in the reduction of breakdown pressure to 38.5 % in small hole diameter blocks and 60.5 % in large hole diameter blocks. Other minerology rocks also shown the significant reduction in breakdown pressure due to thermochemical treatments.
Khan, Mohammad Rasheed (King Fahd University of Petroleum & Minerals) | Kalam, Shams (King Fahd University of Petroleum & Minerals) | Khan, Rizwan Ahmed (King Fahd University of Petroleum & Minerals) | Tariq, Zeeshan (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Over the past two decades, enhanced oil recovery (EOR) has become one of the prominent techniques to increase the recovery of reservoir. EOR consist of four different techniques i.e. thermal, chemical, microbial and gas injection. Gas injection/flooding is one of the most robust applied technique for light oil EOR. Gas injection/flooding comprise of two processes called miscible gas flooding and immiscible gas flooding. Miscible gas flooding is the process in which both injection fluid and reservoir fluid are miscible. The minimum pressure at which both injecting, and reservoir fluids are miscible–is commonly known as Minimum Miscibility Pressure (MMP). The estimation of MMP is the challenging and crucial task in the designing of miscible gas flooding. In this study, we used experimental data along with the machine learning algorithms to find out the relation for MMP. Moreover, the comparison between three different algorithms (Support Vector Machine (SVM), Functional Network (FN) and Artificial Neural Networks (ANN)) was performed based on the results of statistical analysis.
A new empirical correlation was established to estimate MMP as a function of reservoir temperature, reservoir oil composition, and injected gas composition. Since the data set contains reservoir composition data, the developed correlation incorporates the condensing/vaporizing mechanism during the miscible gas flooding process.
The data set used to establish the new empirical correlations was based on experimental data obtained from literature. The data set was separated into two parts called development data and testing and validation data. To establish the new correlations, development data comprising of 70% of the data was used. Whereas, the rest 30% was kept solely to perform testing and validation of the developed correlations. Three different machine learning algorithms called Artificial Neural Networks (ANN), Support Vector Machine (SVM), Functional Network (FN) were used to develop the new correlation. The parameters of each algorithm were optimized to find out the best correlation. For ANN, the number of neurons, weights, and bias were optimized. Whereas for SVM, the epsilon and kernel parameters were tweaked to yield an accurate model. Likewise, for FN model a backward elimination method was found to be the best learning algorithm.
To assess the performance of the developed correlations, statistical analysis was performed. Moreover, to avoid the occurrence of local minimum, multiple realizations (total 5000) with different algorithm parameters were run. The results indicated a minimal and acceptable average absolute error. Based on error, ANN was found to give the best correlation for prediction of MMP.
Murtaza, Mobeen (King Fahd University of Petroleum & Minerals) | Rahman, Mohammad Kalimur (King Fahd University of Petroleum & Minerals) | Al Majed, Abdulaziz Abdulla (King Fahd University of Petroleum & Minerals) | Tariq, Zeeshan (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals)
The mechanical properties are determined to measure the sustainability and long-lasting behavior of cement slurry under wellbore conditions. Different measurement methods were adopted in the past to study the mechanical behavior of a cement slurry. The most commonly used methods applied in oil and gas sector are cement crushing and acoustic velocities measurements. Both techniques have some limitations and additional techniques are warranted. Scratch test technique is commonly used for characterization of mechanical properties of metals, coatings and other materials. Advances in scratch testing of materials has resulted in its application to cohesive material such as rocks and cement. Recently, scratch test has been successfully applied for the strength evaluation of oil well cement. In this paper, we present the results of scratch tests carried out on oil well cement using type G cement and the specimens modified using nanoclay as an additive. The compressive strength test results from scratch test was compared to the macro level testing of cement cores loaded in compression up to failure. The dynamic elastic parameters of cement mix, elastic modulus and Poisson's ratio, were also determined using the scratch test. The scratch test based strength measurement technique will serve as a very handy tool for drilling and geomechanics engineers to study the mechanical properties of the cement slurry aged under different wellbore conditions with high level of certainty.
Tariq, Zeeshan (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Carbonate rocks have a very complex pore system due to the presence of interparticle and intra-particle porosities. This makes the acquisition and analysis of the petrophysical data, and the characterization of carbonate rocks a big challenge. In this study, functional network tool is used to develop a model to predict water saturation using petrophysical well logs as input data and the dean-stark measured water saturation as an output parameter. The data comprised of more than 200 well log points corresponding to available core data. The developed FN model was optimized by using several optimization algorithms such as differential evolution (DE), particle swarm optimization (PSO), and covariance matrix adaptation evolution strategy (CMAES). FN model optimized with PSO found to be the most robust artificial intelligence (AI) model to predict water saturation in carbonate rocks. The results showed that the proposed model predicted the water saturation with an accuracy of 97% when related to the experimental core values. In this study in addition to the development of optimized FN model, an explicit empirical correlation is also extracted from the optimized FN model. To validate the proposed correlation, three most commonly applied water saturation models (Simandoux, Bardon and Pied model, Fertl and Hammack Model, Waxman-Smits, and Indonesian) from literature were selected and subjected to same well log data as the AI model to estimate water saturation. The estimated water saturation values for AI and other saturation models were then compared with experimental values of testing data and the results showed that AI model was able to predict water saturation with an error of less than 5% while the saturation models did the same with lesser accuracy of error up to 50%. This work clearly shows that computer-based machine learning techniques can determine water saturation with a high precision and the developed correlation works extremely well in prediction mode.
Up to 2010, 44.55% of 312 EOR's project for light oil implemented around the world in sandstone reservoirs were come from continuous miscible gas CO2 injection which contributed to an incremental Recovery Factor (RF) of about 34.5% for less than 10 years of production period. This fact has triggered many oil industries to apply this potential and proven technogy for their assets. This potential comes with the needs of having a robust tool to forecast additional recovery due to CO2 injection. This work focuses to development of predictive model using artificial neural network (ANN). More than 6000 series of input-output parameters for ANN training and validation/testing data are extracted from numerical reservoir simulator of 1/8 of five-spot pattern models. The models are set as combination of reservoir geometry, rock, fluid and well operating condition parameters within the range of CO2 EOR screening criteria. The main objective of this work is to find the best ANN architecture/model which accurately matches reservoir simulation results, especially the relationship of RF, total volume of injected CO2 (GI) and the reservoir characteristics and well operating conditions. Trial and error of ANN architectures and parameters are done on number of hidden layers, number of neurons for each hidden layer, learning rate (LR) value, and momentum constant (MC) with minimization algorithm (Lavenberg-Marquardt) in Feed-Forward Back Propagation (FFBP) schemes under log-sigmoid transfer function. An optimum result of ANN model is achieved with an architecture of 18-26-11-2. The relative error of RF and GI of the ANN model are within range of 3 to 10% respectively. A better average relative error of RF and GI of 2.8% and 4.15% respectively are obtained after removing the outliers (unrealistic combinations of input data) from training process of the ANN model. Furthermore, it is clearly found that oil viscosity plays the most the important factor in CO2 EOR method.
The gas deviation factor (Z-factor) is an effective thermodynamic property required to address the deviation of the real gas behavior from that of an ideal gas. Empirical models and correlations to compute Z-factor based on the equation of states (EOS) are often implicit, because they needed huge number of iterations and thus computationally very expensive. Many explicit empirical correlations are also reported in the literature to improve the simplicity; yet, no individual explicit correlation has been formulated for the complete full range of pseudoreduced temperatures and pseudo-reduced pressures, which demonstrates a significant research gap.
Tariq, Zeeshan (King Fahd University of Petroleum and Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum and Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum and Minerals) | Al-Nakhli, Ayman (Saudi Aramco) | Bataweel, Mohammed (Saudi Aramco)
The enormous resources of hydrocarbons hold by unconventional reservoirs across the world along with the growing oil demand make their contributions to be most imperative to the world economy. However, one of the major challenges faced by oil companies to produce from the unconventional reservoirs is to ensure economical production of oil. Unconventional reservoirs need extensive fracturing treatments to produce commercially viable hydrocarbons. One way to produce from these reservoirs is by drilling horizontal well and conduct multistage fracturing to increase stimulated reservoir volume (SRV), but this method of increasing SRV is involved with higher equipment, material, and operating costs.
To overcome operational and technical challenges involved in horizontal wells multistage fracturing, the alternative way to increase SRV is by creating multiple radial fractures by performing pulse fracturing. Pulse fracturing is a relatively new technique, can serve as an alternative to conventional hydraulic fracturing in many cases such as to stimulate naturally fractured reservoirs to connect with pre-existing fractures, to stimulate heavy oil with cold heavy oil production technique, to remove condensate banking nearby wellbore region, and when to avoid formation damage near the vicinity of the wellbore originated due to perforation. Pulse fracturing is not involved with injecting pressurized fluids into the reservoir, so it is also a relatively cheaper technique.
The purpose of this paper is to present a general overview of the pulse fracturing treatment. This paper will give general idea of the different techniques and mechanisms involved in the application of pulse fracturing technique. The focus of this review will be on the comparison of different fracturing techniques implemented normally in the industry. This study also covers the models developed and applied to the simulation of complex fractures originated due to pulse fracturing.
Khan, Mohammad Rasheed (King Fahd University of Petroleum & Minerals) | Alnuaim, Sami (ARAMCO) | Tariq, Zeeshan (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Well production rate is one of the most critical parameters for reservoir/production engineers to evaluate performance of the system. Given this importance, however, monitoring of production rates is not usually carried out in real time. Some cases flowmeters are used which are known to carry their own inherent uncertainties. The industry, thus, relies on the use of correlations to allocate production to wells. Over time, it has been realized that the generally used correlations are not effective enough due to multiple technical and economic issues.
The focus of this work is to utilize machine learning (ML) algorithms to develop a correlation that can accurately predict oil rate in artificial gas lift wells. The reason for using these algorithms is to provide a solution that is simple, easy to use and universally applicable. Various intelligent algorithms are employed, namely; Artificial Neuro Fuzzy Inference Systems (ANFIS), and Support Vector Machines (SVM), along with the development of Artificial Neural Network providing a usable equation to be applied on any field, hence demystifying the black-box reputation of artificial intelligence. In addition, non-linear regression is also performed to compare the results with ML methods.
Data cleansing and data-reduction were carried out on the dataset comprising of 1500 separator test points. This practice yielded in only the common wellhead parameters to be used as input for the model. All ML models were compared with the non-linear regression model and with previously derived empirical models to gauge the effectiveness of the work. The newly developed model using ANN shows that it can predict the flow-rate with 99% accuracy. This is an interesting outcome, as such accuracy has not been reported in literature usually.
The results of this study show that the correlation developed using ANN outperforms all the current empirical correlations, moreover, it also performs multiple times better in comparison to previously developed AI models. In addition, this work provides a functional equation that can be used by anyone on their field data, thereby removing any ambiguities or confusion related to the concept of artificial intelligence expertise and software. This effort puts forth an industrial insight into the role of data-driven computational models for the production reconnaissance scheme, not only to validate the well tests but also as an effective tool to reduce qualms in production provisions.