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Ahmed Alsaihati

Bio: Ahmed Alsaihati is an academic researcher from King Fahd University of Petroleum and Minerals. The author has contributed to research in topics: Drilling & Support vector machine. The author has an hindex of 3, co-authored 13 publications receiving 36 citations.

Papers
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Journal ArticleDOI
TL;DR: The development and evaluation of an intelligent system that could help the drilling crew to detect downhole abnormalities in real-time, react, and take corrective action to mitigate the problem promptly is detailed.
Abstract: The standard torque and drag (T&D) modeling programs have been extensively used in the oil and gas industry to predict and monitor the T&D forces. In the majority of cases, there has been variability in the accuracy between the pre-calculated (based on a T&D model) and actual T&D values, because of the dependence of the model’s predictability on guessed inputs (matching parameters) which may not be correctly predicted. Therefore, to have a reliable model, program users must alter the model inputs and mainly the friction coefficient to match the actual T&D. This, however, can conceal downhole conditions such as cutting beds, tight holes, and sticking tendencies. The objective of this study is to develop an intelligent machine to predict the continuous profile of the surface drilling torque to enable the detection of operational problems ahead of time. This paper details the development and evaluation of an intelligent system that could promote safer operation and extend the response time limit to prevent undesired events. Actual field data of Well-1, starting from the time of drilling a 5-7/8-in. horizontal section until 1 day prior to the stuck pipe incident, were used to train and test three models: random forest, artificial neural network, and functional network, with an 80/20 training-to-testing data ratio, to predict the surface drilling torque. The independent variables for the model are the drilling surface parameters, namely: flow rate (Q), hook load (HL), rate of penetration (ROP), rotary speed (RS), standpipe pressure (SPP), and weight-on-bit (WOB). The prediction capability of the models was evaluated in terms of correlation of coefficient (R) and average absolute error percentage (AAPE). The model with the highest R and lowest AAPE was selected to continue with the analysis to detect downhole abnormalities. The best-developed model was used to predict the surface drilling torque on the last day leading up to the incident in Well-1, which represents the normal and healthy trend. Then, the model was coupled with a multivariate metric distance called “Mahalanobis” to be used as a classification tool to measure how close an actual observation is to the predictive normal and healthy trend. Based on a pre-determined threshold, each actual observation was labeled “NORMAL” or “ANOMAL.” Well-2 with a stuck pipe incident was used to assess the capability of the developed system in detecting downhole abnormalities. The results showed that in Well-1, where a stuck pipe incident was reported, a continuous alarm was detected by the developed system 9 h before the drilling crew observed any abnormality, while the alarm was detected 7 h prior to any observation by the crew in Well-2. The developed intelligent system could help the drilling crew to detect downhole abnormalities in real-time, react, and take corrective action to mitigate the problem promptly.

32 citations

Journal ArticleDOI
TL;DR: The developed PCA-based RF and FN models provide an accurate UCS estimation in real-time from the drilling data, saving time and cost, and enhancing the well stability by generating UCS log from the rig drilling data.
Abstract: The rock unconfined compressive strength (UCS) is one of the key parameters for geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by conventional methods such as experimental work or empirical correlation from logging data are time consuming and highly cost. To overcome these drawbacks, this paper utilized the help of artificial intelligence (AI) to predict (in a real-time) the rock strength from the drilling parameters using two AI tools. Random forest (RF) based on principal component analysis (PCA), and functional network (FN) techniques were employed to build two UCS prediction models based on the drilling data such as weight on bit (WOB), drill string rotating speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q), and the rate of penetration (ROP). The models were built using 2333 data points from well (A) with 70:30 training to testing ratio. The models were validated using unseen dataset (1300 data points) of well (B) which is located in the same field and drilled across the same complex lithology. The results of the PCA-based RF model outperformed the FN in terms of correlation coefficient (R) and average absolute percentage error (AAPE). The overall accuracy for PCA-based RF was R of 0.99 and AAPE of 4.3%, and for FN yielded R of 0.97 and AAPE of 8.5%. The validation results showed that R was 0.99 for RF and 0.96 for FN, while the AAPE was 4% and 7.9% for RF and FN models, respectively. The developed PCA-based RF and FN models provide an accurate UCS estimation in real-time from the drilling data, saving time and cost, and enhancing the well stability by generating UCS log from the rig drilling data.

27 citations

Journal ArticleDOI
TL;DR: The results showed that the RF-meta model outperformed the base learners and Maurer's and Bingham's empirical models in predicting the ROP in Well-2 with a low absolute average percentage error (AAPE) and a high coefficient of determination (R2) of 0.94.

18 citations

Journal ArticleDOI
TL;DR: In this paper, the field practices were combined with the literature to study the most practiced water-based drilling fluid recipes used for onshore gas applications in the Middle East (i.e., spud mud, high-bentonite spud, salt/polymer mud, and high-overbalanced mud).
Abstract: The proper selection of drilling fluids formulations and its treatment has always been a challenge and requires a great effort to ensure optimum drilling performance. The objective of this paper is to assist the mud engineer in selecting the water-based drilling fluid formulations that are best suited for a certain application. To achieve this target, the field practices were combined with the literature to study the most practiced water-based drilling fluid recipes used for onshore gas applications in the Middle East (i.e., spud mud, high-bentonite spud mud, salt/polymer mud, and high-overbalanced mud). From both field practices and deep literature review, it is recommended that both spud mud and high-bentonite spud mud be prepared and pre-hydrated for 4–6 h before a well spud. Also, it is important to add detergents with high-viscosity sweeps to avoid the bit balling and maintain the gel strength. While for salt/polymer mud, the regular addition of sodium sulfite is necessary for polymers stabilization, and the efficient solids control equipment performance is essential. To avoid the solids sagging issues associated with drilling high-pressure high-temperature deep gas reservoirs, it is recommended to either uses sag resistance materials, micronized weighting materials, or a combination of different weighting materials. The high-overbalanced mud is the most effective and efficient type when drilling a combination of natural fractured depleted gas reservoirs with high-pressure gas reservoirs.

15 citations

Journal ArticleDOI
TL;DR: In this paper, two intelligent models were developed utilizing the random forest (RF) and decision tree (DT) techniques to predict the rock porosity in real time while drilling complex lithology using machine learning.
Abstract: Rock porosity is an important parameter for the formation evaluation, reservoir modeling, and petroleum reserve estimation. The conventional methods for determining the rock porosity are considered costly and time-consuming operations during the well drilling. This paper aims to predict the rock porosity in real time while drilling complex lithology using machine learning. In this paper, two intelligent models were developed utilizing the random forest (RF) and decision tree (DT) techniques. The drilling parameters include weight on bit, torque, standpipe pressure, drill string rotation speed, rate of penetration, and pump rate. Two datasets were employed for building the models (3767 data points) and for validating the developed models (1676 data points). Both collected datasets have complex lithology of carbonate, sandstone, and shale. Sensitivity and optimization on different parameters for each technique were conducted to ensure optimum prediction. The models' performance was checked by four performance indices which are coefficient of determination (R2), average absolute percentage error (AAPE), variance account for (VAF), and a20 index. The results indicated the strong porosity prediction capability for the two models. DT model showed R2 of 0.94 and 0.87 between the predicted and actual porosity values with AAPE of 6.07 and 9% for training and testing, respectively. Generally, RF provided a higher level of strong prediction than DT as RF achieved R2 of 0.99 and 0.90 with AAPE of 1.5 and 7% for training and testing, respectively. The models' validation proved a high prediction performance as DT achieved R2 of 0.88 and AAPE of 8.58%, while RF has R2 of 0.92 and AAPE of 6.5%.

14 citations


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Book
01 Jan 2011
TL;DR: In this article, an online statement fundamentals of drilling engineering can be one of the options to accompany you subsequently having further time, which is an easy means to specifically acquire lead by on-line.
Abstract: Getting the books fundamentals of drilling engineering now is not type of challenging means. You could not deserted going taking into account ebook stock or library or borrowing from your contacts to log on them. This is an very easy means to specifically acquire lead by on-line. This online statement fundamentals of drilling engineering can be one of the options to accompany you subsequently having further time.

112 citations

Journal ArticleDOI
TL;DR: The developed PCA-based RF and FN models provide an accurate UCS estimation in real-time from the drilling data, saving time and cost, and enhancing the well stability by generating UCS log from the rig drilling data.
Abstract: The rock unconfined compressive strength (UCS) is one of the key parameters for geomechanical and reservoir modeling in the petroleum industry. Obtaining the UCS by conventional methods such as experimental work or empirical correlation from logging data are time consuming and highly cost. To overcome these drawbacks, this paper utilized the help of artificial intelligence (AI) to predict (in a real-time) the rock strength from the drilling parameters using two AI tools. Random forest (RF) based on principal component analysis (PCA), and functional network (FN) techniques were employed to build two UCS prediction models based on the drilling data such as weight on bit (WOB), drill string rotating speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q), and the rate of penetration (ROP). The models were built using 2333 data points from well (A) with 70:30 training to testing ratio. The models were validated using unseen dataset (1300 data points) of well (B) which is located in the same field and drilled across the same complex lithology. The results of the PCA-based RF model outperformed the FN in terms of correlation coefficient (R) and average absolute percentage error (AAPE). The overall accuracy for PCA-based RF was R of 0.99 and AAPE of 4.3%, and for FN yielded R of 0.97 and AAPE of 8.5%. The validation results showed that R was 0.99 for RF and 0.96 for FN, while the AAPE was 4% and 7.9% for RF and FN models, respectively. The developed PCA-based RF and FN models provide an accurate UCS estimation in real-time from the drilling data, saving time and cost, and enhancing the well stability by generating UCS log from the rig drilling data.

27 citations

Journal ArticleDOI
TL;DR: In this article, three machine learning models, namely, ANN, adaptive neuro-fuzzy inference system (ANFIS), and functional neural networks (FNN), were used to predict the lithology changes and formation top in real-time while drilling.

25 citations

Journal ArticleDOI
19 May 2021
TL;DR: Support vector machines, functional networks, and random forest are used to develop three models for real-time pore pressure gradient prediction using both mechanical and hydraulic drilling parameters.
Abstract: Real-time prediction of the formation pressure gradient is critical mainly for drilling operations. It can enhance the quality of decisions taken and the economics of drilling operations. The pressure while drilling tool can be used to provide pressure data while drilling, but the tool cost and its availability limit its usage in many wells. The available models in the literature for pressure gradient prediction are based on well logging or a combination of some drilling parameters and well logging. The well-logging data are not available for all wells in all sections in most wells. The objective of this paper is to use support vector machines, functional networks, and random forest (RF) to develop three models for real-time pore pressure gradient prediction using both mechanical and hydraulic drilling parameters. The used parameters are mud flow rate (Q), standpipe pressure, rate of penetration, and rotary speed (RS). A data set of 3239 field data points was used to develop the predictive models. A different data set unseen by the model was utilized for the validation of the proposed models. The three models predicted the pore pressure gradient with a correlation coefficient (R) of 0.99 and 0.97 for training and testing, respectively. The root-mean-squared error (RMSE) ranged from 0.008 to 0.021 psi/ft for training and testing, respectively, between the predicted and the actual pore pressure data. Moreover, the average absolute percentage error (AAPE) ranged from 0.97% to 3.07% for training and testing, respectively. The RF model outperformed the other models by an R of 0.99 and RMSE of 0.01. The developed models were validated using another data set. The models predicted the pore pressure gradient for the validation data set with high accuracy (R of 0.99, RMSE around 0.01, and AAPE around 1.8%). This work shows the reliability of the developed models to predict the pressure gradient from both mechanical and hydraulic drilling parameters while drilling.

21 citations

Journal ArticleDOI
TL;DR: The results showed that the RF-meta model outperformed the base learners and Maurer's and Bingham's empirical models in predicting the ROP in Well-2 with a low absolute average percentage error (AAPE) and a high coefficient of determination (R2) of 0.94.

18 citations