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Dr. Sulaiman Alarifi
King Fahd University of Petroleum & Minerals

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Journal article
Published: 12 August 2021 in Molecules
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Drilling issues such as shale hydration, high-temperature tolerance, torque and drag are often resolved by applying an appropriate drilling fluid formulation. Oil-based drilling fluid (OBDF) formulations are usually composed of emulsifiers, lime, brine, viscosifier, fluid loss controller and weighting agent. These additives sometimes outperform in extended exposure to high pressure high temperature (HPHT) conditions encountered in deep wells, resulting in weighting material segregation, high fluid loss, poor rheology and poor emulsion stability. In this study, two additives, oil wetter and rheology modifier were incorporated into the OBDF and their performance was investigated by conducting rheology, fluid loss, zeta potential and emulsion stability tests before and after hot rolling at 16 h and 32 h. Extending the hot rolling period beyond what is commonly used in this type of experiment is necessary to ensure the fluid’s stability. It was found that HPHT hot rolling affected the properties of drilling fluids by decreasing the rheology parameters and emulsion stability with the increase in the hot rolling time to 32 h. Also, the fluid loss additive’s performance degraded as rolling temperature and time increased. Adding oil wetter and rheology modifier additives resulted in a slight loss of rheological profile after 32 h and maintained flat rheology profile. The emulsion stability was slightly decreased and stayed close to the recommended value (400 V). The fluid loss was controlled by optimizing the concentration of fluid loss additive and oil wetter. The presence of oil wetter improved the carrying capacity of drilling fluids and prevented the barite sag problem. The zeta potential test confirmed that the oil wetter converted the surface of barite from water to oil and improved its dispersion in the oil.

ACS Style

Mobeen Murtaza; Sulaiman A. Alarifi; Muhammad Shahzad Kamal; Sagheer A. Onaizi; Mohammed Al-Ajmi; Mohamed Mahmoud. Experimental Investigation of the Rheological Behavior of an Oil-Based Drilling Fluid with Rheology Modifier and Oil Wetter Additives. Molecules 2021, 26, 4877 .

AMA Style

Mobeen Murtaza, Sulaiman A. Alarifi, Muhammad Shahzad Kamal, Sagheer A. Onaizi, Mohammed Al-Ajmi, Mohamed Mahmoud. Experimental Investigation of the Rheological Behavior of an Oil-Based Drilling Fluid with Rheology Modifier and Oil Wetter Additives. Molecules. 2021; 26 (16):4877.

Chicago/Turabian Style

Mobeen Murtaza; Sulaiman A. Alarifi; Muhammad Shahzad Kamal; Sagheer A. Onaizi; Mohammed Al-Ajmi; Mohamed Mahmoud. 2021. "Experimental Investigation of the Rheological Behavior of an Oil-Based Drilling Fluid with Rheology Modifier and Oil Wetter Additives." Molecules 26, no. 16: 4877.

Journal article
Published: 01 March 2021 in Applied Sciences
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A comprehensive overview and analysis of the productivity of 1216 recently abandoned multi-stage hydraulically fractured horizontal wells from five shale formations in the United States (US) is presented in this study. In this study, two decline curve analysis (DCA) methods were used to match actual production history data using least-squares fitting to find the best fit production parameters to reliably forecast production. The production history matching conducted resulted in very accurate matches (correlation coefficient of 0.99) between actual production data and the two DCA methods (Arps hyperbolic decline and stretched exponential production decline (SEPD) models). Using the outcomes from production history matching, universal averages of decline parameters for Arps hyperbolic decline and SEPD models were developed for each of the five formations. Furthermore, hindcasting was performed by matching a portion of the known production history and comparing the remaining portion of the known production history to the forecast. The Arps hyperbolic decline and SEPD methods were used to match production using only limited early production data (three months, six months, one year and two years). The main goals for fitting the DCA model to early production data was to estimate the optimum decline parameters that are then used to forecast production and estimate ultimate recovery. Production history matching using limited early production periods produced accurate production forecasts using as few as six months of production history (correlation coefficients between 0.85 and 0.94 using Arps hyperbolic decline). The main outcome of this study was a production analysis conducted on the production data of more than 1000 wells from five different shale formations to present the expected production behaviors of similar wells. Different production key performance indicators (KPIs) such as average well life, cumulative production volumes at different periods, average drop in production rate within the first year of production, average time to reach maximum flow rate, and the maximum flow rate were measured on all the wells from the five formations to provide an overview of the production performance of each formation.

ACS Style

Sulaiman Alarifi. Production Data Analysis of Hydraulically Fractured Horizontal Wells from Different Shale Formations. Applied Sciences 2021, 11, 2165 .

AMA Style

Sulaiman Alarifi. Production Data Analysis of Hydraulically Fractured Horizontal Wells from Different Shale Formations. Applied Sciences. 2021; 11 (5):2165.

Chicago/Turabian Style

Sulaiman Alarifi. 2021. "Production Data Analysis of Hydraulically Fractured Horizontal Wells from Different Shale Formations." Applied Sciences 11, no. 5: 2165.

Review
Published: 18 December 2020 in Sustainability
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A continuous growth in the global economy and population requires a sustainable energy supply. Maximizing recovery factor out of the naturally occurring hydrocarbons resources has been an active area of continuous development to meet the globally increasing demand for energy. Coalbed methane (CBM), which is one of the primary resources of natural gas, associates complex storage mechanisms and requires some advanced recovery techniques, rendering conventional reserve assessment methods insufficient. This work presents a literature review on CBM in different aspects. This includes rock characteristics such as porosity, permeability, adsorption capacity, adsorption isotherm, and coal classification. In addition, CBM reservoirs are compared to conventional reservoirs in terms of reservoir quality, reservoir properties, accumulation, and water/gas saturation and production. Different topics that contribute to the production of CBM reservoirs are also discussed. This includes production mechanisms, well spacing, well completion, and petrophysical interpretations. The main part of this work sheds a light on the available techniques to determine initial-gas-in-place in CBM reservoirs such as volumetric, decline curve, and material balance. It also presents the pros and cons of each technique. Lastly, common development and economic challenges in CBM fields are listed in addition to environmental concerns.

ACS Style

Ali Altowilib; Ahmed AlSaihati; Hussain Alhamood; Saad Alafnan; Sulaiman Alarifi. Reserves Estimation for Coalbed Methane Reservoirs: A Review. Sustainability 2020, 12, 10621 .

AMA Style

Ali Altowilib, Ahmed AlSaihati, Hussain Alhamood, Saad Alafnan, Sulaiman Alarifi. Reserves Estimation for Coalbed Methane Reservoirs: A Review. Sustainability. 2020; 12 (24):10621.

Chicago/Turabian Style

Ali Altowilib; Ahmed AlSaihati; Hussain Alhamood; Saad Alafnan; Sulaiman Alarifi. 2020. "Reserves Estimation for Coalbed Methane Reservoirs: A Review." Sustainability 12, no. 24: 10621.

Conference paper
Published: 08 March 2015 in All Days
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In the production aspect of petroleum engineering, Productivity Index (PI) is considered as a key parameter to develop the inflow performance relationships (IPR). It estimates and forecasts the well productivity and production efficiency. The current practice to obtain PI is to conduct a rate or well test in a producing well after which the PI can be calculated. Many correlations have been developed to predict PI for horizontal oil wells using reservoir and well properties before drilling wells for planning purposes to effectively build and estimate the production system of the well. Artificial intelligence (AI) techniques have been used in the industry to enhance the engineer's ability to forecast and predict the many different and high uncertain outcomes of many of the petroleum industry aspects. AI methods have proven its accuracy for many cases and have been used as successful tools by many oil and gas companies. Prediction/forecasting using AI is a well-known practice and very essential requirement toward a better and more productive industry. This paper discusses and shows the ability of three artificial intelligence methods (Neural Networks, Fuzzy Logic and Functional Networks) to predict productivity index of horizontal oil wells with very good accuracy as compared to several well-known correlations in the industry (Borisov, Giger-Reiss-Jourdan, Renard-Dupuy, Joshi and Economides, Butler and Furui). It discusses, for the first time in the oil industry, the application of the Functional Networks AI techniques in prediction PI of the horizontal oil wells. The models are built using several real field rate tests collected from more than 100 different horizontal oil wells from a field in the Middle East. Also, models showed to overcome the limitations of existing horizontal wells' correlations with high validity of the prediction due the presence of actual field data, not just assumed/simulated data.

ACS Style

Sulaiman Alarifi; Sami AlNuaim; Abdulazeez Abdulraheem. Productivity Index Prediction for Oil Horizontal Wells Using different Artificial Intelligence Techniques. All Days 2015, 1 .

AMA Style

Sulaiman Alarifi, Sami AlNuaim, Abdulazeez Abdulraheem. Productivity Index Prediction for Oil Horizontal Wells Using different Artificial Intelligence Techniques. All Days. 2015; ():1.

Chicago/Turabian Style

Sulaiman Alarifi; Sami AlNuaim; Abdulazeez Abdulraheem. 2015. "Productivity Index Prediction for Oil Horizontal Wells Using different Artificial Intelligence Techniques." All Days , no. : 1.