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) | Al-Nakhli, Ayman (Saudi Aramco) | BaTaweel, Mohammed (Saudi Aramco) | Elktatany, Salaheldin (King Fahd University of Petroleum & Minerals)
Condensate banking is a common problem in tight gas reservoirs because it diminishes the gas relative permeability and reduces the gas production rate significantly. CO2 injection is a common and very effective solution for condensate removal in tight gas reservoirs. The problem with CO2 injection is that it is a temporary solution and has to be repeated frequently in the field in addition to the supply limitations of CO2 in some areas. Also, the infrastructure required at the surface to handle CO2 injection makes it expensive to apply CO2 injection for condensate removal.
In this paper, a new permanent technique is introduced to remove the condensate by using a thermochemical technique. Two chemicals will be used to generate in-situ CO2, nitrogen, steam, heat, and pressure. The reaction of the two chemicals downhole can be triggered either by the reservoir temperature, or a chemical activator. Two chemicals will start reacting and produce all the mentioned reaction products after 24 hrs. of mixing and injection. Also, the reaction can be triggered by a chemical activator and this will shorten the time of reaction. Coreflooding experiments were carried out using actual condensate samples from one of the gas fields. Tight sandstone cores of 0.9 mD permeability were used.
The results of this study showed that, the thermochemical reaction products removed the condensate and reduced its viscosity due to the high temperature and the generated gases. The novelty in this paper is the creation of micro-fractures in the tight rock sample due to the in-situ generation of heat and pressure from the thermochemical reaction. These micro-fractures reduced the capillary forces that hold the condensate and enhanced its relative permeability. The creation of micro-fractures and in turn the reduction of the capillary forces can be considered as permanent condensate removal.
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) | Alade, Olalekan (King Fahd University of Petroleum & Minerals) | Al-Nakhli, Ayman (Saudi Aramco) | BaTaweel, Mohammed (Saudi Aramco) | Elktatany, Salaheldin (King Fahd University of Petroleum & Minerals)
In petroleum industry, great challenges are associated with producing hydrocarbon from unconventional reservoirs. Tight reservoirs are characterized with low permeability which reduces the hydrocarbon flow into the wellbore. Water blockage is considered as a potential damage issue in tight reservoirs due to increasing the water saturation around wellbore region and eventually decreasing the relative permeability of hydrocarbons. Acid fracturing or hydraulic fracturing are required to remove the damage and enhance the formation conductivity. The objective of this paper is to propose a new technique to remove the water blockage from tight formations using thermochemical treatment. Chemicals that generate pressure and heat at reservoir conditions are used to remove the water bank from tight core samples.
Coreflooding experiments, capillary pressure and NMR measurements were conducted as well as routine core analysis. The impact of thermochemical treatment on improving the formation productivity was quantified. The effect of thermochemical injection on rock integrity was analyzed by evaluating the pore geometry before and after the chemical treatment. Thermochemical treatment resulted in a significant improvement in the core conductivity. NMR indicated that, tiny fractures were created in the core samples due the thermochemical flooding. Capillary pressure measurements showed that, the capillary pressure was reduced by 55.6% after the chemical treatment.
The results of this study highlight that water blockage is great challenge in tight gas reservoirs. Injecting thermochemical fluids into tight samples reduces the capillary forces significantly, which leads to remove the water accumulation. Therefore, considerable enhancement was observed in the rock conductivity. This study provides a novel approach for removing the water blockage from tight formations using environmentally friendly chemicals. Chemicals that generate heat and pressure at downhole conditions were used to create tiny fractures. This treatment was able to remove the water blockage from tight sandstone cores and improve the productivity index by reducing the capillary forces.
Elhaj, Murtada (Memorial University of Newfoundland) | Abdullatif, Osman (King Fahd University of Petroleum and Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum and Minerals) | Hassan, Amjed (King Fahd University of Petroleum and Minerals) | Sultan, Abdullah (King Fahd University of Petroleum and Minerals)
The science of Acoustics deals with the propagation of mechanical waves in the three phases of materials, solids, liquids, and gases. In exploration and reservoir engineering, acoustic wave velocities play an essential role in reservoir description. The primary challenge in the initial evaluation and characterization of reservoirs is related to the understanding of its geology, petrophysics, and geomechanics. Therefore, an accurate estimation of acoustic wave velocities and rock porosity is essential for better reservoir description and performance as well as a better forecast of seismic properties. In this reseach, the primary objective is to analyze the texture, mineralogy, porosity and permeability data of outcrop carbonate rock samples to study the impact of confining pressure on wave velocities. Furthermore, an empirical correlation is proposed for relating porosity with acoustic properties.
Ninety outcrops samples are collected from Dam Formation in Al-Lidam area in Eastern Province, Saudi Arabia to develop a correlation. The carbonate samples varies from mudstone to grainstone facies. The samples are collected, prepared, and tested for this experimental study based on API standards. Compressional and shear wave velocities of carbonate rocks are measured under dry and fully brine-saturated conditions for 5 to 25 MPa effective confining pressures at room temperature. Moreover, porosity and permeability are measured using three different techniques, viz., AP-608 Automated Porosimeter-Permeameter, Helium Porosimeter, and thin section technique. Finally, the results are compared with those from other studies related to the same area.
A state-of-the-art review is presented on seismic properties, relationship with porosity and acoustics in addition to the current trend and the future challenges in the area. The laboratory investigations for this study reveals that Al-Lidam area has different types of facies. The results also show that both compressional and shear wave velocities increase as the confining pressure on the dry samples increase. However, the compressional wave velocities increased and the shear wave velocities decreased with confining pressure under fully saturated conditions. A new correlation is presented for carbonate rocks to predict porosity from acoustic wave velocities.
This study will help in improving the exploration efforts as well as give a better explanation for reservoir characterization, facies recognition, geophysical interpretation, and engineering calculations. This attempt will open a new research area for engineers and scientists to study the effect of variation in different properties on wave velocities.
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.
Hassan, Amjed (KFUPM) | Al-Majed, Abdulaziz (KFUPM) | Elkatatny, Salaheldin (KFUPM) | Mahmoud, Mohamed (KFUPM) | Abdulraheem, Abdulazeez (KFUPM) | Nader, Mahmoud (KFUPM) | Abughaban, Mahmoud (Saudi Aramco) | Khamis, Mohammed (king Saud university)
Understanding the formation behavior and the drilling operation is essential to optimize the performance of drilling systems. Several studies were conducted to improve the drilling operation in real time basis utilizing different approaches. Numerous mathematical models (analytical or empirical) were developed to relate the drilling parameters with the rate of penetrations and to predict the drilling efficiency. However, these models are arrived at by ignoring some parameters or employing simplifying assumption(s), which may lead to over or under optimistic drilling performance.
The main objective of this research is to investigate the analytical and numerical approaches to calculate the torque and drag in drilling operations and produce a simple and robust model using artificial intelligent techniques. More than 22,000 data point from several wells for depth up to 18,000 ft. was used to develop and validate the model reliability. The full profiles of torque and rate of penetrations was determined, also the required energy for drilling each section has been estimated. The developed model could be utilized to define the optimum range for torque, which leads consequently to generate an efficient drilling system and reduce the drilling cost.
In this study, rate of penetration (ROP) was determined using the torque profile and mechanical specific energy (MSE) based on real time and rig-site data. Statistical analysis was conducted to understand the importance of drilling parameters on the variations of torque and rate of penetration. Drilling parameters such as weight on bit (WOB), revolution per minute (RPM), fluid circulation rate (Q), and bit hydraulic horse power (HPb) has been studied. Thereafter, artificial neural network (ANN) model was developed to predict the torque and ROP profiles. The suggested models enable the drilling engineers to optimize the drilling parameters in a real-time manner, by changing the surface and controllable parameters in such a way that maintains the drilling operations within the optimum conditions. This research will assist in improving the operations efficiency through optimizing the drilling parameters. A strong robust model was developed which yields high accuracy results when compared with actual field measurements, average absolute percentage error of less than 6.5% was achieved.
Tariq, Zeeshan (King Fahd University of Petroleum & Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum & Minerals) | Hassan, Amjed (King Fahd University of Petroleum & Minerals) | Khan, Mohammad Rasheed (King Fahd University of Petroleum & Minerals) | Sadeed, Ahmed (King Fahd University of Petroleum & Minerals)
The applicability of Archie's relation depends on the link between pore structure and resistivity of the rock, and because of this relationship, the rock maybe classified as Non-Archie. Keeping this factor in view, for carbonates, the intricate pore systems, the application of Archie's law becomes invalid and any estimate of water saturation using Archie's equation will most likely yield erroneous results. Many scholars and researchers have attempted to come up with models linking well logs and laboratory data to estimate water saturation in complex pore systems. To appropriately capture the conduction behavior of a rock is aimed at and the conduction mechanism is then related to not only the pore geometry but also other petrophysical factors including wettability and the existence of conductive exterior minerals. For complex lithologies, resistivity index curves cannot be accurately defined by Archie's law, and investigations at lower water saturation values are required to describe the non-Archie behavior.
The shape of the RI curves varies quite considerably and an attempt to describe the variation qualitatively as well as quantitatively requires the use of either dual or triple conductivity models. In these models, the pore systems are linked with each other electrically in series or parallel, in addition, a combination of the two is also possible. This enables the use of simply two of the three adjustable parameters to adequately model the experimental outcomes.
This study, presents a saturation model for dual porosity systems. Having NMR data will enable the use of this model to determine water saturation by utilizing the volume fraction of each contributing pore system (Macro and Micro) derived from NMR logs. Furthermore, this work also examined the effects of various influences on the electrical constraints which included cementation factor, the shape factor for the lithology and saturation exponent.
Exploration in oil and gas industry is considered as an area full of uncertainties. The uncertainties are mainly due to the heterogeneous nature of the reservoir rock, rock-fluid interaction, fluid-fluid interaction, and since the reservoirs are usually located at about several thousand feet below the surface.
Thermal operations are the most effective enhanced oil recovery (EOR) techniques used to increase hydrocarbon production, especially in heavy oil reservoirs. Thermal EOR involves injecting of steam or hot fluids into underground reservoirs to alter the physical properties, specifically, the fluid viscosity and effective mobility. Numbers of mathematical (numerical or analytical) models were developed to estimate the temperature variations during such operations, however, those models assume constant fluid velocity throughout the reservoir, then, severe estimations errors could be generated. The objective of this paper is to develop a new approach for determining the temperature distribution during thermal- EOR processes, and the heat propagations with time and distance from the wellbore with acceptable tolerance.
The main aim of this work is to develop a reliable model to predict the temperature distributions in porous rocks during thermal EOR operations. Artificial intelligence (AI) methods were utilized to compute the temperature profiles, those models would minimize the complexity and uncertainties of numerical approaches. The impact of formation permeability, injection time and distance from wellbore were considered to develop effective models. To ensure a high level of model reliability, more than 220 data set was used for training and testing the proposed models.
Temperature distribution was determined using the neural network, fuzzy logic system, and generalized intelligent networks. Different model's parameters were used to optimize the intelligent networks, average absolute error and correlation coefficient were utilized to measure the model performance. ANN model showed the best prediction performance, an average absolute error of 6.2 % and a correlation coefficient of 0.98 was obtained using unseen data set.
Thermal operations are considered as the most common techniques used in the world to enhance the oil production during the tertiary stage. These operations are conducted by injecting steam or hot water into hydrocarbon reservoirs to reduce the oil viscosity and improve displacement efficiency (Marx and Langenheim, 1959). A significant increase in the oil recovery was reported, up to 30% of the original oil in place can be recovered using steam injection (Zerkalov,2015). Figure 1 shows a typical steam injection operation, where the injection well is utilized for steam injecting and production well is used for producing the hydrocarbon. Usually, the production rate is kept below a critical flow rate to avoid water fingering and fast breakthrough (Siemek and Stopa, 2002).
The use of carbon dioxide in miscible flooding has been considered as one of the most effective techniques for enhancing oil production. The flooding efficiency is an extreme function of the minimum miscibility pressure (MMP), therefore, searching for a quick and rigorous method to determine MMP is highly needed. Slim tube experiments are normally used to measure the minimum miscibility pressure. However, such experiments are time-consuming and very costly. Different correlations have been developed to determine the MMP during CO2 injection process. These empirical equations are not widely applicable and might produce severe estimation errors, because they are developed based on limited experimental results.
This paper proposes a new technique to evaluate the CO2 flooding and minimize the uncertainties of using numerical approaches. The objective of this work is developing a reliable model to predict the MMP during CO2 flooding. Actual case studies for flooding heterogeneous and anisotropic reservoir were utilized to generate the MMP model, more than 140 data points were used to construct and evaluate the proposed model. Several artificial intelligence techniques were studied to estimate the CO2-MMP for a wider range of conditions. The developed models investigate the effect of API gravity, fluid composition, and injected gas composition on the performance of CO2 flooding operation.
The CO2-MMP was estimated using different artificial intelligence techniques including; radial basis function network, artificial neural network, generalized neural network and adaptive neuro-fuzzy inference system. The wellbore condition and reservoir parameters were used to provide an accurate and quick prediction for the flooding performance. Sensitivity study was conducted to optimize the model parameters. Then, the optimized artificial neural network model was utilized to extract an empirical equation.
The developed equation was verified using actual field data an acceptable average absolute percentage error (AAPE) of 6.6% was obtained. In addition, the developed CO2-MMP model was compared with different determination approaches. It is found that, the proposed technique outperforms the current CO2-MMP models. This work would afford an effective approach to characterize the CO2-flooding for complex reservoirs, also improve the prediction performance of commercial software, which leads to a better production management in the particular CO2-operations.
The horizontal wells are applied to enhance the production rate by increasing the contact area between the wellbore and reservoir, it has been also used to access the highly heterogeneous and unconventional formations. One horizontal well can produce the same amount of 5 vertical wells with a very competitive cost and operational time. Further improvement for the productivity of horizontal well can be achieved by conducting hydraulic fracture operations, especially for low permeable or unconventional formations. This paper shows a new technique to estimate the performance of hydraulically fractured horizontal wells, without a need for using downhole valves or smart completion.
In the literature, few empirical models have been proposed to evaluate the inflow performance of such wells. However, most of these models assume constant pressure drop in the horizontal section, therefore, significant errors were reported from those models. In this work, a reliable model will be presented to predict the well deliverability for hydraulically fractured horizontal well producing from heterogeneous and anisotropic formation. Different artificial intelligence (AI) methods were investigated to evaluate the well performance using a wide range of reservoir/wellbore conditions. The significant of several parameters on the well productivity were investigated including; permeability ratio (kh/kv), number of fracture stages and the length of horizontal section.
The AI model was developed and validated using more than 300 data sets. Artificial neural network (ANN) model is built to determine the production rate with an acceptable error of 8.4%. The model requires the wellbore configurations and reservoir parameters to quantify the flow rate. No numerical approaches or downhole well completions were involved in this ANN model, which reduce the running time by avoiding such complexity. Moreover, a mathematical relation was extracted from the optimized artificial neural network model. In conclusion, this work would afford an effective tool to determine the performance of complex wells, and reduce the differences between the actual production data and the outputs of commercial well performance software.
The fishbone wells are new production technology applied to increase well productivity and access the difficult geological formations and unconventional reservoirs. The main advantages of this technology over hydraulic fracturing are the competitive price and reduced operation time. Fishbone shaped multilateral wells proved better productivity than multi-fractured horizontal wells in relatively low permeable reservoirs. In this paper, a new approach is proposed to predict the fishbone performance without using smart well completion or any down-hole valves in the horizontal laterals.
Very limited work has been done for multilateral fishbone drilled in dry gas reservoir, and few empirical models were developed to estimate the inflow performance of fishbone wells producing from two-phase reservoirs, however, those models ignore the number of rib holes and assume constant pressure drop across horizontal laterals, therefore, using such models can produce severe estimations errors. The main objective of this research is to present a reliable model to estimate the productivity of fishbone multilateral wells producing from anisotropic and heterogeneous gas reservoirs. Several artificial intelligence techniques were studied to quantify the productivity of fishbone multilateral well for a wider range of conditions without introducing uncertainties/ complexity associated with other numerical methods.
The proposed models investigate the significance of reservoir parameters, number of laterals, permeability ratio (Kh/Kv), length of laterals and lateral spacing on the productivity of the fishbone well. More than 250 data sets were utilized to develop and validate the model reliability. The production rate is estimated using artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), generalized neural network (GRNN) and radial basis function network (RBF). The models require the reservoir parameters and the wellbore configurations to determine the flow rate without a need for using down-hole well completion. Furthermore, mathematical equation was extracted by utilizing the artificial neural network model, this equation was verified using two rate tests from actual gas field, an acceptable error of 7% was obtained. The finding of this work would afford an effective tool for quantifying the productivity of complex fishbone wells and refine the commercial well performance software to narrow down the differences between the simulation outputs and actual field data, then lead to a better determination of the optimum production rate.