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Iron sulfide scale is considered a big issue in the oil and gas industry and a very common problem in daily operation bases. The iron scale severely affects the production rates and field operation, as the iron scale precipitation on the well completion tools or the pipelines` inner surfaces will decrease the diameter size and cause flow restriction. Failure of the downhole completions tools will decrease the well production and it might require workover operations to cure the problem. Such workover operations add an extra cost. In addition, the iron scale is a big problem for the petroleum reservoirs as it changes the rock wettability from water-wet to oil-wet. Many studies and researches were performed to find an economical and efficient removal for the iron sulfide scale. The main objective of this study is to find a new environmentally chemical solution to be used for iron sulfide scale removal. Additionally, to study the new fluid performance in terms of solubility for an iron scale (field sample), corrosion effect, and biodegradability for environmentally safe use.
Lab solubility tests had been done at different times and concentrations to get the optimum time and concentration that provides the optimum iron scale solubility rate. Corrosion tests were performed to determine the corrosion rates for the new chemical and the results were compared with other commercial chemicals for iron scale removal.
The results from the study showed excellent performance for the scale solubility and for the corrosion rates. The iron scale solubility showed 82 % after 6 hours at 125°C for the static condition. In addition, the solubility rates ranged from 77% after 2 hours to 81% after 24 hours along the 24 hours of different time steps. The corrosion rate is 0.04 lb/ft2 which is considered a small rate compared to other used commercial fluids. The research outcomes provide an efficient chemical for iron scale removal that is environmentally safe to be used in the oil and gas fields. The new environmentally friendly acid system NEFAS provides very good performance for iron scale removal, with a lower cost than commercial chemicals. Furthermore, the new system is a biodegradable and environmentally safe system.
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.
Basfar, Salem (King Fahd University of Petroleum and Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum and Minerals) | Al-Majed, Abdulaziz (King Fahd University of Petroleum and Minerals) | Al Sheri, Dhafer (King Fahd University of Petroleum and Minerals)
High-pressure high-temperature (HPHT) reservoirs required special drilling fluid with high density and steady rheology to sustain the challenging conditions. Barite is one of the most weighting agents to increase the mud density at a low cost. But drilling HPHT wells causes settle the barite particle down in the wellbore known as barite sagging. Barite sagging may lead to several issues, for instance, stuck pipe, density variation, loss of circulation and well control problems.
To prevent the barite sagging, a new copolymer was used in this study. Static sag experiments were conducted to evaluate the mud in drilling break for both cases vertical and 45° inclined wells, dynamic sag tests also were performed to simulate the sagging while drilling. Static and dynamic sag tests were conducted to evaluate the ability of copolymer to suspend the weighting materials in the invert emulsion drilling fluids (IEF). Then the effect of adding copolymer in rheology, electrical stability, and HPHT filtration was achieved.
The objective of this study is to assess the effect of adding the new copolymer to IEF (polymer based on styrene and acrylic monomers) on the suspension efficiency of the weighting agent and rheological stability in HPHT condition
The results obtained showed that 1 lb/bbl of the new copolymer didn't affect the electrical stability of the invert emulsion mud. The sag issue was prevented after adding 1 lb/bbl of the copolymer for both vertical and inclined situation and in the dynamic case at a temperature greater than 300 °F. For the rheology test, the copolymer enhanced the plastic viscosity, yield point and gel strength of the mud to increase the mud stability during drilling HPHT wells. Adding 1 lb/bbl of copolymer had no effect on HPHT filtration loss and filter cake thickness.
The novelty of this study is to provide the oil and gas industry with newly designed mud to solve the barite sagging for the invert emulsion drilling fluid at high density to sustain the HPHT.
The mechanical behavior of the rocks can greatly assist in optimizing the drilling operation and well completion design. This behavior can be expressed in terms of Young’s modulus and Poisson’s ratio. Reliable Poisson’s ratio values can be estimated experimentally from core measurements however this method consumes time and economically ineffective.
This study involved the development of two models using neural networks (ANN) and fuzzy logic to estimate static Poisson’s ratio (PRstatic) of sandstone rocks based on the conventional well-log data including bulk density and sonic log data. The models are developed using 692 of actual data core data and the corresponding logging data. The models are optimized after several runs of the different combinations of the available tuning parameters.
The results showed that the neural network model outperformed the model developed using the fuzzy logic tool and yielded a great match with correlation coefficient (R) of 0.98 and AAPE of 1.5% between the predicted and measured PRstatic values. The developed ANN-based model is then validated using unseen data from another well within the field to estimate PRstatic over a certain interval. The validation process results showed a significant agreement with correlation coefficient (R) of 0.95 between the predicted PRstatic values and the actual measured ones. The results demonstrated the ability of the developed model to provide a continuous profile of static Poisson’s ratio (PRstatic) whenever the petrophysical logging data are available.
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.
Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals and Cairo University) | Al-AbdulJabbar, Ahmed (King Fahd University of Petroleum & Minerals ) | Mahmoud, Ahmed Abdulhamid (King Fahd University of Petroleum & Minerals )
Determination of the formation tops is an important and critical parameter while drilling a hydrocarbon well since it is one of the main factors affecting selection of the casing setting depths and drilling fluid design. During the field exploration and delineation phase and based on the geological data, the formation tops are estimated with low accuracy because of data limitations.
In this study, a potential alternative technique for predicting formation tops is introduced. This technique involves application of artificial neural networks (ANN) and the use of a combination of the drilling mechanical parameters and the rate of penetration (ROP) to provide an accurate prediction of the formation tops. Incorporating the drilling mechanical parameters in this technique is suggested to help in predicting the true increase or decrease in the ROP regardless of the fluctuation on the other drilling parameters.
Field data from two vertical oil wells (Well-A and Well-B) from the Middle East were used in this study. Seventy percent of the data from Well-A (4,436 data points) was used to train the ANN model, which was then tested on the remaining 30% of the data for Well-A (1,900 data points) and validated using the data from Well-B (6,569 data points).
The sensitivity analysis confirmed that using a ANN model that consists of 25 neurons, one hidden layer, and with the Levenberg-Marquardt backpropagation function as the training function, is the optimum for predicting the formation tops with correlation coefficients (R) of 0.94 and 0.98 for the testing and validation data of Well-A and Well-B, respectively. The developed ANN model showed high accuracy in estimating the formation tops for both the testing and validation datasets of Well-A and Well-B, respectively.
ABSTRACT: Cement strength retrogression under high-temperature restricts the use of the oil well cement. Previous studies evaluated the effect of the silica (SiO2) particles on mitigating the effect of temperature on the mechanical properties of the cement at specific time periods. In this work, the changes in the mechanical properties of the SiO2 based cement will be evaluated continuously with time from a slurry to set. Two samples were prepared, one without SiO2 while the other one containing 35% BWOC of SiO2 particles. The changes in the mechanical properties of the samples when exposed to 140°C were then studied. The results of this study confirmed that incorporating 35% BWOC of SiO2 particles accelerated the hydration process, and thus, improved the cement strength retrogression resistance. The final stabilized compressive strength of the base sample (i.e. sample without SiO2 particles) was 743 psi compared to 6200 psi for the sample incorporating SiO2 particles. Poisson's ratio of the sample incorporating SiO2 stabilized at 0.19 which is equivalent to 52.8% of the Poisons ratio for the base cement. The final stabilized bulk, Young's, shear, and uniaxial compaction moduli of the sample including 35% BWOC of SiO2 are 1.87, 3.86, 2.71, and 2.46 of those for the base sample, respectively.
Oil well cement is used to provide oil and gas wells with the desired mechanical stability needed to prevent formation of micro-cracks which will present conduits for the formation fluids to flow between different drilled formations and enable the corrosive formation fluids to contact the well casing. To meet the requirement of the needed isolation efficiency throughout the life of the well, the cement sheath filling the gap between the casing and the formations must be hard enough and durable (Rabia, 2001; Mitchell and Miska, 2011).
The downhole operating temperature or the reservoir temperature to which the cement will be subjected is one of the main parameters to be considered while designing the cement slurry and cementing operations, this is because the temperature change affects both the liquid slurry and solid cement sheath (Luke, 2004; Vu et al., 2012; Shahab et al., 2015; Maharidge et al., 2016; Costa et al., 2017; Wang, 2017).
ABSTRACT: This study evaluates the use of the synthetic polypropylene fibers (SPF) to mitigate Class G cement strength retrogression under high-temperature conditions. Cement samples with different SPF concentrations were prepared, half of the samples were cured at 38°C for 28 days to represent a base of comparison, while the other half was cured for 25 days at 38°C and for additional 3 days at 300°C. The changes in the compressive strength, tensile strength, and permeability of the cement samples were evaluated under both conditions. At low-temperature conditions, incorporating the SPF improved the compressive and tensile strengths of the cement, and the rate of improvement increases with the increase in the SPF concentration. Adding 0.125% BWOC of SPF into the cement decreased the cement permeability to 0.0013 mD compared with 0.0036 mD for the base cement. Subjecting the cement samples to 300°C during the last 3 days of curing considerably affected the compressive and tensile strengths of the base sample which decreased by 11.9% and 13.7%, respectively. Incorporating 0.125% of the SPF decreased the base cement strength retrogression at 300°C. The cement sample containing 0.125% of SPF has compressive and tensile strengths greater than those of the base cement by 19.3% and 18.7%, respectively.
One of the main functions of the oil well cement is to provide oil and gas wells with the desired mechanical stability and to prevent formation of micro-cracks which could present conduits for the formation fluids to flow between different drilled formations and could enable the corrosive formation fluids to contact and damage the well casing. To satisfy the required isolation efficiency throughout the life of the well, the cement sheath must be hard enough and durable (Rabia, 2001; Mitchell and Miska, 2011).
The reservoir temperature expected to act on the cement is one of the main factors to be considered while designing the cement slurry and cementing operations (Luke, 2004; Vu et al., 2012; Shahab et al., 2015; Maharidge et al., 2016; Wang, 2017).
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.