Gowida, Ahmed (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Formation density plays a central role to identify the types of downhole formations. It is measured in the field using density logging tool either via logging while drilling (LWD) or more commonly by wireline logging, after the formations have been drilled, because of operational limitations during the drilling process that prevent the immediate acquisition of formation density.
The objective of this study is to develop a predictive tool for estimating the formation bulk density (RHOB) while drilling using artificial neural networks (ANN). The ANN model uses the drilling mechanical parameters as inputs and petrophysical well-log data for RHOB as outputs. These drilling mechanical parameters including the rate of penetration (ROP), weight on bit (WOB), torque (T), standpipe pressure (SPP) and rotating speed (RPM), are measured in real time during drilling operation and significantly affected by the formation types. A dataset of 2,400 data points obtained from horizontal wells was used for training the ANN model. The obtained dataset has been divided into a 70:30 ratio for training and testing the model, respectively.
The results showed a high match with a correlation coefficient (R) between the predicted and the measured RHOB of 0.95 and an average absolute percentage error (AAPE) of 0.71%. These results demonstrated the ability of the developed ANN model to predict RHOB while drilling based on the drilling mechanical parameters using an accurate and low-cost tool. The black-box mode of the developed ANN model was converted into white-box mode by extracting a new ANN-based correlation to calculate RHOB directly without the need to run the ANN model. The new model can help geologists to identify the formations while drilling. Also, by tracking the RHOB trends obtained from the model it helps drilling engineers avoid many interrupting problems by detecting hazardous formations, such as overpressured zones, and identifying the well path, especially while drilling horizontal sections. In addition, the continuous profile of RHOB obtained from the developed ANN model can be used as a reference to solve the problem of missing and false logging data.
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.
Development of stress sensitive reservoirs, especially in challenging environment, is raising awareness that geomechanics is a vital aspect of reservoir management. Understanding reservoir geomechanical behavior becomes more and more important for petroleum industry. A significant changes in formation pressure caused by depletion will induce deformation and stress/strain changes in the reservoir and the surrounding formation, understanding in-situ stresses and how stress changes in and around the reservoir due to depletion is important in a multidisciplinary approach to reservoir characterization and management. These changes in stresses/strain affect the reservoir as well as the overburden and underburden formation, which directly affect drilling and stimulation operations strategies. Reservoir compaction, shear casing and well damage, cap-rock integrity, fault reactivation and sand production can occur during reservoir depletion. To address these issues, 3D geomechanical models have been developed (which describe the state of stresses in the reservoir and overburden).
Murtaza, Mobeen (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Majed, Abdulaziz Al (King Fahd University of Petroleum & Minerals) | Chen, Weiqing (King Fahd University of Petroleum & Minerals) | Jamaluddin, Abul (King Fahd University of Petroleum & Minerals)
In cementing operations of deep oil and gas wells, long term integrity of the well is highly dependent on the cement sheath. Obtaining success rate in cementing operations has been subjected to a myriad of challenges, as drilling into deeper, high pressure/high temperature horizons is done. To gain long term integrity of cement sheath, a successful placement of cement slurry plays a pivotal role. So, the design of suitable rheological properties helps characterize the cement pumpability, mixability, and displacement rates for adequate removal of mud. So, the design of cement slurry for HPHT and deviated wells has become a complex task. Recently employing nano-materials in improved oil recovery, designing of drilling fluids as well as hydrocarbon well cementing has been the focus of many studies. The intrinsic characteristics of being smaller in size, while at the same time providing a larger surface area, nanomaterials can prove to be a game-changer for the challenges faced in HPHT cementing. This paper reproduces the outcomes of an investigational study conducted to determine the effect of nanoclay as an additive on rheological properties of Type-G cement slurry under various temperature conditions. Nano-clay with Class G cement in two different concentrations 1% and 2% by weight of cement, mixed and tested under different temperature conditions (37°C, 50°C, 60°C & 80 °C). Additionally, nano-clay based cement mixtures were prepared by substituting cement with 1%, 2% and 3% of nano-clay by weight of cement(BWOC), and admixed with silica flour, along with various chemical admixtures. American Petroleum Institute (API) standard-10B was followed to condition the slurry at predetermined temperature, while the slurry was under atmospheric pressure. This conditioning was followed by the measurement of rheological properties. Results of this investigation demonstrate that incorporation of nano-clay advances the rheology of prepared cement slurry that could aid in mud-displacement and anti-settling as per the requirements.
Ahmed S, Abdulmalek (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Ali, Abdulwahab Z (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Rate of Penetration (ROP) means how fast the drilling bit is drilling through the formations. It is known that in the oil and gas industry, most of the well cost is taken by the drilling operations. So, it is very crucial to drill carefully and improve the drilling processes. Nevertheless, it is hard to know the influence of every single parameter because most of the drilling parameters depend on each other, and altering an individual parameter will have an impact on the other. Due to the difficulty of the drilling operations, up to the present time, there is no dependable model that can estimate the ROP correctly. Consequently, using the artificial intelligence (AI) in the drilling is becoming more and more applicable because it can consider all the unknown parameters in building the model. In this work, a real filed data that contain the real time surface drilling parameters and the drilling fluid properties were utilized by fuzzy logic (FL) to estimate the rate of penetration. The achieved results proved that fuzzy logic technique can be applied effectively to estimate the rate of penetration accurately with R 0.97 and AAPE 7.3%, which outperformed the other ROP models. The developed AI models also have the advantage of using less input parameters than the previous ROP models.
Ahmed S., Abdulmalek (King Fahd University of Petroleum & Minerals) | Mahmoud, Ahmed Abdulhamid (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Pore and fracture pressures are a critical formation condition that affects efficiency and economy of drilling operations. The knowledge of the pore and fracture pressures is significant to control the well. It will assist in avoiding problems associated with drilling operation and decreasing the cost of drilling operation. It is essential to predict pore and fracture pressures accurately prior to drilling process to prevent various issues for example fluid loss, kicks, fracture the formation, differential pipe sticking, heaving shale and blowouts. Many models are used to estimate the pore and fracture pressures either from log information, drilling parameters or formation strengths. However, these models have some limitations such as some of the models can only be used in clean shales, applicable only for the pressure generated by under-compaction mechanism and some of them are not applicable in unloading formations. Few papers used artificial intelligence (AI) to estimate the pore and fracture pressures. In this work, a real filed data that contain the log data and real time surface drilling parameters were utilized by support vector machine (SVM) to predict the pore and fracture pressures.
Drilling Fluids rheological properties play a vital role in controlling the success of the drilling operation. Rheological parameters such as effective (or apparent) viscosity, yield point (YP), gel strength, and plastic viscosity (PV) are very important for rig hydraulic calculations and hole cleaning efficiency. The water-based drilling fluid (WBDF) consists of a mixture of different solids and polymers which are used to optimize the rheological properties. Starch is a resistance-solid additive which is used mainly to control the filtration properties and at the same time to increase the viscosity of the drilling fluid.
The main goal of this research is to evaluate the effect of using micronized starch (1 μm) on the rheological and filtration properties of water-based drilling fluid. Field emission scanning electron microscope (FESEM) was used to evaluate the starch at different particle size. Rheological properties for the drilling fluid with different starch sizes were measured at ambient condition using Fan VG rheometer while the high-pressure high-temperature (HPHT) filter press was used to conduct the filtration experiment at 200°F.
It was noted that micronized starch (1 μm) had a vital effect on the rheological and filtration properties of WBDF. The PV of the WBDF with micronized starch was increased by 158% while the YP was increased by 125% as compared with the starch of conventional size (60 μm). The apparent viscosity (AV) was increased by 137% after reducing the starch sized to 1 μm. Adding the micronized starch for the WBDF resulted in flat rheology behavior where there is no increase in the gel strength between 10 seconds and 10 minutes. The filter cake thickness was reduced by 63% while the cumulative filtrate volume was decreased by 52% when 1 μm starch is used.
This study introduced a new drilling fluid formulation that contains a micronized starch as an additive, which will help the drilling engineers to avoid many drilling issues especially the formation damage by forming an ideal filter cake.
Ahmed S, Abdulmalek (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Ali, Abdulwahab Z (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals)
Fracture pressure is a critical formation condition that affects efficiency and economy of drilling operations. The knowledge of the fracture pressure is significant to control the well. It will assist in avoiding problems associated with drilling operation and decreasing the cost of drilling operation. It is essential to predict fracture pressure accurately prior to drilling process to prevent various issues for example fluid loss, kicks, fracture the formation, differential pipe sticking, heaving shale and blowouts.
Many models are used to estimate the fracture pressure either from log information or formation strengths. However, these models have some limitations such as some of the models can only be used in clean shales, applicable only for the pressure generated by under-compaction mechanism and some of them are not applicable in unloading formations. Few papers used artificial intelligence (AI) to estimate the fracture pressure. In this work, a real filed data that contain only the real time surface drilling parameters were utilized by artificial neural network (ANN) to predict the fracture pressure.
The results indicated that artificial neural network (ANN) predicted the fracture pressures with an excellent precision where the coefficient of determination (R2) is greater than 0.99. In addition, the artificial neural network (ANN) was compared with other fracture pressure models such as Matthews and Kelly model, which is one of the most used models in the prediction of the fracture pressure in the field. Artificial neural network (ANN) model outperformed the fracture models by a high margin and by its simple prediction of fracture pressure where it can predict the fracture pressure from only the real time surface drilling parameters, which are easily available.
Hassan, Amjed (King Fahd University of Petroleum & Minerals) | Al-Majed, Abdulaziz (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Oil is considered one of the main drivers that affects the world economy and a key factor in its continuous development. Several operations are used to ensure continues oil production, these operations include; exploration, drilling, production, and reservoir management. Numerous uncertainties and complexities are involved in those operations, which reduce the production performance and increase the operational cost.
Several attempts were reported to predict the performance of oil production systems using different approaches, including analytical and numerical methods. However, severe estimation errors and significant deviations were observed between the predicted results and actual field data. This could be due to the different assumptions used to simplify the problems. Therefore, searching for quick and rigorous models to evaluate the oil-production system and anticipate production problems is highly needed.
This paper presents a new application of artificial intelligent (AI) techniques to determine the efficiency of several operations including; drilling, production and reservoir performance. For each operation, the most common conditions were applied to develop and evaluate the model reliability. The developed models investigate the significance of different well and reservoir configurations on the system performance. Parameters such as, reservoir permeability, drainage size, wellbore completions, hydrocarbon production rate and choke performance were studied. The primary oil production and enhanced oil recovery (EOR) operations were considered as well as the stimulation processes. Actual data from several oil-fields were used to develop and validate the intelligent models.
The novelty of this paper is that the proposed models are reliable and outperform the current methods. This work introduces an effective approach for estimating the performance of oil production system and refine the current numerical or analytical models to improve the reservoir managements.
Barite is one of the most common weighting materials used in drilling fluids for deep oil and gas wells. Consequently, the main source of solids forming the filter cake is "barite particles," the weighting material used in drilling fluids. Barite is insoluble in water and acids such as hydrochloric acid (HCl) and formic, citric, and acetic acids, and barite is moderately soluble in chelating agents such as ethylenediaminetetraacetic acid (EDTA).
The present study introduces a new formulation to dissolve barite scale and barite filter cake using converters and catalysts. Barite can be converted to barium carbonate (BaCO3) in a high-pH medium (pH = 12) using a combination of potassium hydroxide (KOH) and potassium carbonate (K2CO3) solutions. Subsequently, HCl or low-pH chelating agents can be used to dissolve the BaCO3. Another solution is to use the EDTA chelating agent at pH of 12 and K2CO3 or KOH as a catalyst/converter in a single step. The removal formulation also contains a polymer breaker (oxidizers or enzymes). The three components of the new formulation are compatible with each other and stable up to 300°F. Solubility experiments were conducted using industrial-grade barite (particle size of 30 to 60 µm). The solubility experiments were conducted at 300°F for 24 hours. Varying concentrations of the catalyst were added to determine the optimal concentration. The developed formulation was tested for the removal of filter cake formed by barite drilling fluid using a high-pressure/high-temperature (HP/HT) cell. Filter-cake removal was conducted for filter cakes formed by both water- and oil-based drilling fluids.
The results of this study show that the barite-removal efficiency of the new formulation is 87% for water-based mud (WBM) and 83% for oil-based mud (OBM). The test results show that the solubility of barite particles in 0.6 M EDTA is 62 wt% in 24 hours at 300°F. Adding K2CO3 or KOH catalyst to the 0.6-M-EDTA solution increases the solubility of barite to 90 wt% in 24 hours at 300°F. Thus, barite scale can be removed efficiently using high-pH formulations (pH = 12) to avoid the safety issues associated with HCl. Because the EDTA chelating agent is compatible with the polymer breaker (oxidizer), the filter cake can be removed in a single stage. The concentration of the components of the formulations used in this study is as follows: 10 wt% oxidizer, 10 wt% K2CO3 or KOH concentration (catalyst/converter), and 0.6 M EDTA. The developed formulations achieved more than 80% filter-cake removal in both oil-based and water-based drilling fluids. For OBM, a water-wetting surfactant, a mutual solvent, and an emulsifier were added tot he formulation to remove the oil and to make the surface of the filter cake water-wet. In this study, two solutions are proposed to remove the barite filter cake and barite scale from oil and gas wells at different conditions. The first one is using HCl after converting the barium sulfate (BaSO4) to BaSO3 by use of a high-pH medium such as KOH and K2CO3. Although HCl can easily remove the resulting BaSO3, the generated barium chloride (BaCl2) is a safety and health concern. The second method is to create a high-pH medium (pH = 12) using the removal fluid itself, which uses the EDTA chelating agent in addition to using K2CO3 or KOH as converters.