The primary physical mechanisms that occur as a result of gas injection are (1) partial or complete maintenance of reservoir pressure, (2) displacement of oil by gas both horizontally and vertically, (3) vaporization of the liquid hydrocarbon components from the oil column and possibly from the gas cap if retrograde condensation has occurred or if the original gas cap contains a relict oil saturation, and (4) swelling of the oil if the oil at original reservoir conditions was very undersaturated with gas. Gas injection is particularly effective in high-relief reservoirs where the process is called "gravity drainage" because the vertical/gravity aspects increase the efficiency of the process and enhance recovery of updip oil residing above the uppermost oil-zone perforations. The decision to apply immiscible gas injection is based on a combination of technical and economic factors. Deferral of gas sales is a significant economic deterrent for many potential gas injection projects if an outlet for immediate gas sales is available. Nevertheless, a variety of opportunities still exist. First are those reservoirs with characteristics and conditions particularly conducive to gas/oil gravity drainage and where attendant high oil recoveries are possible. Second are those reservoirs where decreased depletion time resulting from lower reservoir oil viscosity and gas saturation in the vicinity of producing wells is more attractive economically than alternative recovery methods that have higher ultimate recovery potential but at higher costs. And third are reservoirs where recovery considerations are augmented by gas storage considerations and hence gas sales may be delayed for several years. Nonhydrocarbon gases such as CO2 and nitrogen can and have been used. In general, calculation techniques developed for hydrocarbon-gas injection and displacement can be used for the design and application of nonhydrocarbon, immiscible gas projects. Valuing the use of such gases must include any additional costs related to these gases, such as corrosion control, separating the nonhydrocarbon components to meet gas marketing specifications, and using the produced gas as fuel in field operations. The conceptual aspects of the displacement of oil by gas in reservoir rocks are discussed in this section. There are three aspects to this displacement: gas and oil viscosities, gas/oil capillary pressure (Pc) and relative permeability (kr) data, and the compositional interaction, or component mass transfer, between the oil and gas phases.
A variety of gases can and have been used for immiscible gas displacement, with lean hydrocarbon gas used for most applications to date. Historically, immiscible gas injection was first used for reservoir pressure maintenance. The first such projects were initiated in the 1930s and used lean hydrocarbon gas (e.g., Oklahoma City field and Cunningham pool in the US and Bahrain field in Bahrain). Over the decades, a considerable number of immiscible gas injection projects have been undertaken, some with excellent results and others with poor performance. This page discusses gas injection into oil reservoirs to increase oil recovery by immiscible displacement.
The Haft Kel field is located in Iran. Its Asmari reservoir structure is a strongly folded anticline that is 20 miles long by 1.5 to 3 miles wide with an oil column thickness of approximately 2,000 ft. The matrix block size determined from cores and flowmeter surveys varied from 8 to 14 ft. The numerical simulation model considered matrix permeabilities from 0.05 to 0.8 md. The overall horizontal and vertical permeabilities are approximately equal.
Many aspects of reservoir geology interplay with the immiscible gas/oil displacement process to determine overall recovery efficiency. Because there is always a considerable density difference between gas and oil, the extent to which vertical segregation of the fluids occurs and can be taken advantage of or controlled is critical to the success of gas displacing oil. As with any oil recovery process involving the injection of one fluid to displace oil in the reservoir, the internal geometries of the reservoir interval have a controlling effect on how efficiently the injected fluid displaces the oil from the whole of the reservoir. The stratigraphy of a reservoir is determined primarily by its depositional environment. First and foremost is how layered the reservoir is in terms of both how heterogeneous the various sand intervals are and the scale at which shales or other barriers to vertical flow are interbedded with the sands.
Gas lift is one of the most widely used artificial lift methods, and the use of nodal analysis to generate the gas lift performance curve is well established. However, the optimal gas injection rate is often selected as the point with maximum liquid production, which neglects the cost of incremental injection gas volume. This paper investigates the determination of the optimal operational point using a multiobjective optimization technique by considering the trade-off between gas consumption and oil production. The indicator-based evolutionary algorithm transforms the multiobjective problem into a single objective one using the hypervolume metric computed in the objective space. For the gas lift problem, which is a bi-objective problem aimed at maximizing oil production while minimizing gas injection rate, the hypervolume metrics are identically equivalent to geometric hyperareas under the trade-off curve. The optimization is only applied to the monotonically increasing portion of the gas lift performance curve; thus, all trivial sub-optimal conditions are excluded. The optimal operational point of gas injection rate is determined by finding the maximum rectangular hyperarea under the performance curve. The proper determination of the optimal injection gas rate could not only improve the efficiency of the gas lift itself, but also reduce the burden on the maintenance of surface facilities. The method is also applied to the multi-well scenario where a novel multi-well gas lift performance curve is generated using multiobjective Genetic Algorithm, which could help determine the optimal gas allocation/distribution scenario. The described process is incorporated in an integrated workflow which further leads to fast delivery of analysis/results that enable production engineers to make smarter decisions faster in a repeatable way.
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) | 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.
Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.
In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation pore pressure, and in-situ stresses of the studied area were included as inputs. The second step was by optimizing the process using a genetic algorithm (GA), as a class of optimizing methods for complex functions, to obtain the maximum ROP along with the related wellbore trajectory (AZI and INC). Finally, the suggested azimuth (AZI) and inclination (INC) are premeditated by considering the results of wellbore stability analysis using wireline logging measurements, core and drilling data from the offset wells.
The results showed that the optimized wellbore trajectory based on wellbore stability analysis was compatible with the results of the genetic algorithm (GA) that used to reach higher ROP. The recommended orientation that leads to maximum ROP and maintains the stability of drilling deviated wells (i.e., inclination ranged between 40°—50°) is parallel to (140°—150°) direction. The present study emphasizes that the proposed methodology can be applied as a cost-effective tool to optimize the wellbore trajectory and to calculate approximately the drilling time for future highly deviated wells.
The advanced technology has made directional drilling widely used to enhance the production of mature fields. The rate of penetration (ROP) contributes strongly towards the cost of drilling operations, where achieving higher ROP leads to substantial cost saving. The main objective of this study is to develop a model that predicts the ROP for deviated wells using artificial neural networks (ANNs).
The model was developed based on the most critical variables affecting ROP using ANNs. In addition to the azimuth and inclination of the well trajectory, the controllable drilling parameters, unconfined compressive strength (UCS), pore pressure, and in-situ stresses of the studied area were included as inputs. 1D Mechanical earth modeling (1D-MEM) data, geophysical logs, daily drilling reports, and mud logs (master logs) of deviated wells drilled in Zubair field located in Southern Iraq were used to develop the ANN model.
The results displayed that the ANN’s outputs are close to the measured field data. The correlation coefficient (R) and average absolute percentage error (AAPE) were over 0.91 and 8.3%, respectively, for the training dataset. For testing data, the developed model achieved a reasonable correlation coefficient (R) of 0.89 and average absolute percentage error (AAPE) of 9.6%. Unlike previous studies, this paper investigates the effect of well trajectory’s (azimuth and inclination) and their influence on the ROP for deviated wells. The major advantage of the present study is calculating approximately the drilling time of the deviated well and eventually reducing the drilling cost for future neighboring wells.
The fluid rates and bottom-hole flowing pressure of the wells are essential parameters in the petroleum industry. The need of accurate readings of these measurements are necessary for many calculations such as gas-lift optimization, field monitoring and depletion plans. Predicting these parameters without running in hole has a good impact on reducing the intervention risk and on organization financials by saving time and money. Huge number of correlations are used to estimate these parameters. These correlations need the values of different parameters that are not accurately found. Therefore, an artificial neural network (ANN) model was built from exported data set of PROSPER