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Mustafa, Ayyaz (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) | Tariq, Zeeshan (King Fahd University of Petroleum and Minerals) | Al-Nakhli, Ayman (Saudi Aramco)
The enormous unconventional gas resources can make an effective contribution to the world’s economy as conventional hydrocarbon reserves are depleting rapidly. Oil-producing countries can produce from unconventional gas resources in order to fulfill their local demands and export most of the oil produced. However, for production at economical flow rates, the reservoir volume needs to be increased after stimulation treatment, which is a big challenge for the industry.
This study aims to provide a new stimulation technique after the hydraulic fracturing stimulation to increase the stimulated reservoir volume by creating localized fractures and connecting the existing fractures using thermochemical reagents. Thermochemical reagents generate heat and pressure that cause microscale fractures in tight reservoir formations, thus increasing the fracture complexity. Microscale fractures, in turn, increase the porosity and permeability, causing sweet spot creation near the fracture zone.
A set of thermochemical reagents was used for flooding the samples under pressure and heat until an exothermic reaction took place. An integrated methodology was implemented to investigate the role of thermochemical stimulation on different rock types, including Scioto sandstone, Indiana limestone, and Berea sandstone, through routine core analysis and advanced spectroscopic analytical techniques, such as porosity, permeability, microcomputed tomographic (MCT) imaging, capillary pressures, and nuclear magnetic resonance (NMR). In addition, ultrasonic velocity (compressional- and shearwave) measurements were performed, and dynamic elastic parameters (Poisson’s ratio and Young’s modulus) were determined.
The monitoring techniques exhibited significant changes in petrophysical, strength, and other mechanical properties in rock samples as a result of the exothermic reaction inside the core samples. The MCT images revealed microfractures in the core sample generated as a result of the thermochemical treatment. Post-treatment measurements exhibited a substantial increase in porosity and permeability. A reduction in capillary pressure was observed after the treatment.
The consequence of this study is to introduce a new, economical, and practical approach to increase the stimulated reservoir volume. This new stimulation technique will assist in meeting the difficult challenges related to producing unconventional reservoirs. Thermochemical stimulation may create multidirectional microfractures within the reservoir that better enhance the effective permeability around the wellbore as compared to hydraulic fracturing stimulation. This new thermochemical stimulation is a promising technique for the Middle East, where water scarcity is a big problem. A huge amount of water currently being used for hydraulic fracturing treatment could be redirected for other purposes. The outcome will result in a tremendous enhancement to unconventional gas production.
Tariq, Zeeshan (King Fahd University of Petroleum and Minerals) | Kamal, Muhammad Shahzad (King Fahd University of Petroleum and Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum and Minerals) | Hussain, Syed Muhammad Shakil (King Fahd University of Petroleum and Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum and Minerals) | Zhou, Xianmin (King Fahd University of Petroleum and Minerals)
During well completion operations, the wells are killed with specific fluids to control the well. These fluids can invade and damage the formation because of fluid/rock interactions. Fluids such as fresh water, brines, and weighted fluids (e.g., barite weighted, calcite weighted, and bentonite weighted) are used to control the formations during completion operations. These fluids can invade and interact with clays and damage the formation. In addition, these fluids may alter the near-wellbore wettability and make them more oil-wet, thereby affecting the production from these formations. In this work, polyoxyethylene quaternary ammonium gemini surfactants with different types of spacers are proposed as clay swelling additives in completion fluids to mitigate the formation damage in unconventional reservoirs. Adding the new surfactants will maintain the in-situ permeability and avoid the formation damage. The novel gemini surfactants are tested on unconventional tight sandstone formation enriched with high clay content to mitigate the formation damage during well completion. The process involved a complete stabilization of clays using gemini surfactants added in deionized water (DW). Coreflooding experiments were carried out on Scioto sandstone rock samples with an average porosity of 15.6% and average absolute permeability of 0.25 md. Several coreflooding experiments were carried out with different fluids, such as potassium chloride (KCl), sodium chloride (NaCl), and different classes of gemini surfactants. Coreflooding experiments were designed in a way that the cores were preflushed with the subjected fluid and then post-flooded with DW. Results showed that the cores saturated with KCl and NaCl solutions lost permeability significantly when flooded with water while gemini surfactant solutions maintained the same permeability even after being treated with DW. Conditioning with the KCl solution resulted in a 38% reduction of permeability and that with NaCl solution resulted in an 80% reduction of permeability when treated with DW. No significant change of permeability was found for the case of gemini surfactants. This indicates that the synthesized surfactants can be used for well completion operation without any side effects.
Hassan, Amjed (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals) | Al-Majed, Abdulaziz (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Nonconventional wells (NCWs) are applied to increase the well deliverability and access the difficult formations. The nonconventional wells have been used to refer for the advanced wells such as highly deviated, horizontal, fishbones or multilateral wells. These wells offer a great potential to maximize the hydrocarbon recovery, however, it is difficult to predict their performance. In the literature, numerous mathematical models were developed to predict the well-productivity. However, the available models have been developed by employing one or more simplifying assumption(s), which may lead to over or under estimate the hydrocarbon production. This paper presents an effective technique to estimate the productivity for nonconventional wells.
In this work, artificial intelligence (AI) technique was utilized to determine the well-productivity for wide range of conditions. The developed models can determine the well performance without introducing the complexity associated with the numerical approaches. Artificial neural network was utilized to estimate the hydrocarbon production for two types of nonconventional wells; fishbone multilateral and hydraulically fractured horizontal wells. Reliable models are presented to quantify the performance of nonconventional wells producing from heterogeneous and anisotropic formations. The developed models evaluate the importance of reservoir properties and well configuration on the well deliverability. Total of 850 data sets were utilized to construct the intelligence models and to validate the prediction performance. Moreover, mathematical equations were extracted utilizing the optimized ANN models. The extracted correlations showed acceptable prediction errors, the absolute error around 7.4% in average.
The novelty of this work is that effective models are proposed to quantify the productivity of nonconventional wells. The proposed models can be utilized to refine commercial software to narrow down the deviations between the actual measurements and simulation outputs. Also, this work can contribute to enhance our understanding of the oil-field management by improving the prediction of well deliverability. Consequently, this study can help in optimizing the well planning for complex wells such as hydraulically fractured horizontal and fishbone multilateral wells.
Gowida, Ahmed (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals) | Shehri, Dhafer Al (King Fahd University of Petroleum & Minerals)
Synthetic well-log generation using artificial intelligence tools is presented as a robust solution when the logging data are not available or partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. It is measured in the field using density log tool either while drilling by logging while drilling technique (LWD) or mostly by wireline logging after the formations are drilled because of the operational limitations during the drilling process.
Therefore the objective of this study is to develop a predictive tool for estimating RHOB while drilling using artificial neural networks (ANN) and Adaptive network-based fuzzy interference systems (ANFIS). The proposed models used the drilling mechanical parameters as feeding inputs and the conventional RHOB log-data as an output. These drilling mechanical parameters including the rate of penetration (ROP), weight on bit (WOB), torque (T), stand-pipe pressure (SPP) and rotating speed (RPM), are usually measured while drilling and their responses vary with different formations.
A dataset of 2400 actual data points obtained from horizontal well in the Middle East is used for building the proposed models. The obtained dataset is divided into 70/30 ratios for training and testing the model respectively. The optimized ANN-based model outperformed the ANFIS-based model with correlation coefficient (R) of 0.95 and average absolute percentage error (AAPE) of 0.72 % between the predicted and the measured RHOB compared to R of 0.93 and AAPE of 0.81 % for the ANFIS-based model. These results demonstrated the reliability of the developed ANN model to predict the RHOB while drilling based on the drilling mechanical parameters. Afterwards, the ANN-based model is validated using unseen data from another well within the same field. The validation process yielded AAPE of 0.5 % between the predicted and the actual RHOB values which confirmed the robustness of the developed model as an effective predictive tool.
Tariq, Zeeshan (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals) | Al-Shehri, Dhafer (King Fahd University of Petroleum & Minerals) | Al-Nakhli, Ayman (Saudi Aramco) | Murtaza, Mobeen (King Fahd University of Petroleum & Minerals) | Mustafa, Ayyaz (King Fahd University of Petroleum & Minerals)
Current global energy demand and supply gap needs the best engineering methods to recover hydrocarbons from the unconventional hydrocarbon formations. Unconventional hydrocarbons normally present in deep formations, where the overburden stresses and formation integrity are very high. When fracturing these types of formations, the hydraulic fracturing job becomes much more challenging, and in some scenarios, pumping reached to the maximum capacity limits without generating any fracture. This reduces the operational gap to optimally placed hydraulic fractures. In the present research study, a novel thermochemical fracturing approach is presented to reduce the breakdown pressure of the high-strength formations. The hydraulic fracturing experiments presented in this study are carried out on ultra-tight cement block samples. The composition of cement blocks is synthesized in this way that it simulates the real rocks. The results showed that the newly proposed thermochemical fracturing approach reduced the breakdown pressure in ultra-tight cement from 1095 psia (reference breakdown pressure recorded from conventional hydraulic fracturing technique) to 705 psia. The post treatment experimental analysis showed that the thermochemical fracturing approach resulted in a deep and long fracture while conventional hydraulic fracturing resulted in a thin fracture. In addition to that, a Finite element analysis using ABAQUS is also presented. The main purpose of the numerical investigation is to confirm the sufficiency of the experimental data for reproducing the same breakdown pressure that included depiction of injection pressure versus time plots, failure loads and cracking patterns.
Hassan, Amjed (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals) | Mohamed, Abdelmjeed (King Fahd University of Petroleum & Minerals)
In petroleum industry, PVT properties are very important in predicting the performance of oil and gas reservoirs. Several laboratory measurements are used to determine these properties at reservoir condition. The PVT measurements are costly and time-consuming; therefore, numerous correlations were developed to predict the PVT properties based on primary inputs such as reservoir pressure, temperature and hydrocarbon gravities. However, significant deviations are reported between the actual values and the predicted results. The aim of this paper is to present reliable and rigorous models to determine the PVT properties using artificial intelligence technique.
Artificial neural network was utilized to develop the pressure-volume-temperature models. The proposed models estimate the PVT properties based on the pressure, temperature, oil and gas densities, and the solution gas-oil ratio. The developed models determine the bubble point pressure, formation volume factor and solution gas-oil ratio. Total of 250 data sets were used to develop and evaluate the model reliability. Average absolute percentage error (AAPE) and coefficient of determination (R-value) were used to evaluate the model reliability.
The new models are simple, accurate and easy to use when compared with the existing empirical equations. The obtained results showed that the developed models are able to determine the PVT properties with an average absolute error of around 7.7%. The intelligence models developed in this study outperform the popular PVT equations such as Standing and Al-Marhoun correlations. Average errors of 6.8% and 11.7% were obtained for the developed model and the popular PVT correlations, respectively. Therefore, the developed models can highly increase the quality of production management by providing an accurate estimation for the PVT properties. Also, the presented models can significantly reduce the time and cost required for conducting the PVT measurements.
Ahmed, Abdulmalek (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals) | Abughaban, Mahmoud (Saudi Aramco)
Drilling deep and high-pressure high-temperature wells have many challenges and problems. One of the most severe, costly and time-consuming problem in the drilling operation is the loss of circulation. The drilling fluid accounts for 25-40% of the total cost of the drilling operation. Any loss of the drilling fluid will increase the total cost of the drilling operation. Uncontrolled lost circulation of the drilling fluid can result in dangerous well control problems and in some cases the loss of the well. In order to avoid loss circulation, many methods were introduced to identify the zones of losses. However, some of these methods are difficult to be applied due to financial issues and lack of technology and the other methods are not accurate in the prediction of the thief zones.
The objective of this paper is to predict the lost circulation zones using two different techniques of artificial intelligence (AI). More than 5000 real field data from two wells that contain the real-time surface drilling parameters was used to predict the zones of circulation loss using radial basis function (RBF) and support vector machine (SVM). Six surface drilling parameters were used as an input. The data of the well (A) was divided into training and testing to build the two AI models and then unseen well (B) was used to validate the ability of AI models to predict the zones of lost circulation.
The obtained results showed that the two models of AI were able to predict the zones of circulation loss in well (A) with high accuracy in terms of correlation coefficient (R), root mean squared error (RMSE) and confusion matrix. RBF predicted the losses zones with excellent precision of R = 0.981 and RMSE = 0.088. SVM also achieved higher accuracy with a correlation coefficient of 0.997 and root mean squared error of 0.038. Moreover, the RBF model was able to predict the losses zones in the unseen well (B) that was used as a validation for the ability of the AI models with a correlation coefficient of 0.909 and root mean squared error of 1.686.
Mahmoud, Ahmed Abdulhamid (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Ali, Abdulwahab (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals) | Abouelresh, Mohamed (King Fahd University of Petroleum & Minerals)
Total organic carbon (TOC) is an important factor for the characterization of unconventional shale resources; which is currently evaluated by either conducting extensive laboratory work, using empirical correlations developed based on linear regression analysis, or applying artificial intelligence (AI) techniques. The AI models approved their efficiency for TOC estimation compared to the use or empirical correlation and they have the advantage of providing a continuous TOC profile compared to the laboratory-based evaluation.
This study is aimed to evaluate the predictability of the TOC using two AI models namely functional neural networks (FNN) and support vector machine (SVM). The AI models were trained to estimate the TOC based on well log data of gamma ray, deep resistivity, sonic transit time, and bulk formation density, more than 500 datasets of the well logs and their corresponding core-derived TOC collected from Barnett shale were used to train and optimize the AI models. The predictability of the optimized AI models was then tested on other data from Barnett shale and validated on unseen data from Devonian shale. The ability of the optimized AI models to estimation the TOC for Devonian shale was compared with Wang's density-based correlation (WDC) which was developed recently to estimate the TOC for Devonian formation.
The results showed that the AI models predicted the TOC with high accuracy, and they overperformed WDC in estimating the TOC for Devonian formation. For the validation data, FNN model overperformed SVM in estimating TOC with average absolute percentage error (AAPE) of 12.0% and correlation coefficient (R) of 0.88, while SVM model predicted the TOC with AAPE and R of 14.5% and 0.86, respectively, and WDC estimated the TOC with high AAPE of 34.6% and low R of 0.61.
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) | Al-Nakhli, Ayman (Saudi Aramco) | Asad, Abdul (King Fahd University of Petroleum and Minerals)
Current global energy demand and supply gap needs the best engineering methods to recover hydrocarbons from the unconventional hydrocarbon resources. Unconventional resources mostly found in highly stressed, over pressured, and deep formations, where the rock strength and integrity both are very high. The pressure at which the rock fractures or simply the breakdown pressure is directly correlated with the rock tensile strength and the stresses acting on them from the surrounding formations. When fracturing these kinds of rocks, the hydraulic fracturing operation becomes much more challenging and difficult, and in some scenarios reached to the maximum pumping capacity limits. This reduces the operational gap to optimally placed hydraulic fractures.
In the present research study, a novel thermochemical fracturing approach is presented to reduce the breakdown pressure of the high-strength layered formations. The new approach not only reduces the breakdown pressure of the layered rocks but also generate highly conductive fractures which can penetrate in most of the layers being subjected to fracturing. The hydraulic fracturing experiments presented in this study are carried out on four layered cement block samples. The composition of cement blocks is synthesized in this way that it simulates the real rocks.
The results showed that the newly proposed thermochemical fracturing approach reduced the breakdown pressure in layered rocks from 1495 psia (reference breakdown pressure recorded from conventional hydraulic fracturing technique) to 1107 psia. The post treatment experimental analysis showed that the thermochemical fracturing approach resulted in deep and long fractures, passing through majority of the layers while conventional hydraulic fracturing resulted in a thin fracture affected only the top layer. Thermochemical fluids injection caused the creation of microfractures, improved the porosity and permeability, and reduces the Young's modulus of the rocks. The new technique is cost effective, non-toxic, and sustainable in terms of no environmental hazards.
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) | Siddique, Faisal Anwar (Pakistan Petroleum Limited) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals) | Khan, Rizwan Ahmed (King Fahd University of Petroleum & Minerals)
Reservoir and production engineers rely heavily on well production rates to optimize well activities such as ensuring optimum reservoir monitoring. Individual gas well rates are not readily available, rather, they can be estimated thru multi-phase flow meter (MPFM) and well test analysis. These methods are associated with certain limitations such as high cost, high uncertainty, and technically elaborate calculations. Consequently, empirical and numerical calculations are employed with well test data to calculate daily rates. These practices lead to inaccurate gas rate estimations.
A model with an ability to provide accurate estimates of gas rates for a gas reservoir can serve as a handy tool for the subsurface engineers in addressing well and reservoir optimization strategies. This work presents artificial intelligence models to estimate gas rates in a gas field containing ten wells. The aim is to develop a correlation that is simple and easy to incorporate yet providing robust answers on a global scale. Multiple machine learning tools are employed. These include; Artificial Neural Network (ANN), Functional Network (FN), and Adaptive Neuro Fuzzy Inference System (ANFIS).
Production data from a dry gas field X was used for the model development. Data cleaning and data reduction steps were carried out to ensure the input parameters for the proposed model are physically relevant and accurate. Missing these steps would result in the development of an erroneous correlation, i.e., garbage -in garbage-out (GIGO). This led to finalization of certain basic well-head parameters which are available at any typical well and had direct impact on the output production rate. The target parameter for model training is the gas rate. A rigorous comparison between the investigated artificial intelligence models was conducted by calculating average absolute percentage error (AAPE) and coefficient of determination. The comparative analysis shows that the intelligent model is able to predict the gas rate in condensate wells with accuracy in excess of 90%. Examples of such large accuracy has not been reported previously.
ANN performs a step ahead as compared to the various intelligent algorithms used in this study. This paper sheds light on the potential of the Industrial Revolution 4.0 for the Pakistani Oil and Gas Sector. Data-driven artificial intelligent models are capable of validating the well test and multiphase flow meter results. In addition, it can prove to be a vital tool in an engineer's tool-kit to reduce uncertainties in gas rate measurements.