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This paper provides a comprehensive overview of Deep Transient Testing (DTT), a cutting-edge technique for reservoir characterization that has revolutionized the oil and gas industry. The main aim of DTT is to characterize the reservoir with a deeper radius of investigation. The optimization of the radius of investigation with the DTT approach is studied in detail. Reveal is a commercial numerical simulation application used to simulate the DTT process and evaluate the pressure wave analysis in the porous media. The main aim of the simulation is to understand the impact of the reservoir quality on the pressure response and use it to address the noise-to-pule ratio, which is a determinantal parameter in testing duration. The tested wells with the DTT tool show that measured well productivity can deliver the minimum commercial rate. The has been delivered within 2 days compared to the potential test time of 21 days which saved the 19 rig days and contributed to CO2 emission reduction of (gas flaring 1340 + rig emission 600) 1940 Metric tons equivalent to 421 cars emission in a year. However, DTT also presents certain limitations, such as the requirement for specialized equipment and expertise, as well as the potential for formation damage during testing. This study provides a detailed description of the DTT technique, encompassing its history, theory, and practical applications. Furthermore, it discusses the benefits and limitations of DTT and presents case studies to illustrate its effectiveness across various reservoir types. Overall, this study serves as a valuable resource for reservoir engineers, geologists, and other professionals involved in the exploration and production of oil and gas.

This study presents a correlation-based approach for predicting the compressibility of sandstone reservoir rocks. The study proposes a matrix of new empirical equations that significantly improve the precision of measuring the pore volume compressibility, with the most optimal fit of results based on a cubic polynomial model. The accuracy of the calculations was validated through comparison with actual data using root mean square method, and the suggested correlations significantly enhance the precise prediction of rock compressibility in sandstone reservoirs. In this study, the source of data collection is consolidated and unconsolidated sandstone from East Asia offshore oilfields. Accordingly, variations in compressibility with net overburden pressure over the course of the field's lifespan have been examined. The results demonstrate the application of regression analysis in establishing a network of linkages between independent and dependent variables. The proposed correlations for consolidated and unconsolidated sandstones offer a remarkable improvement in the accurate calculation of rock compressibility compared to traditional laboratory procedures, with an average error of 2.5% compared to 5–10% for laboratory measurements. The approach of this study offers a cost-effective and time-efficient alternative to remarkedly enhance the overall performance of sandstone reservoirs in the oil and gas industry.

Effective flow line and piping network design depends on the accurate prediction of pressure drop in multiphase flow for horizontal and near horizontal pipes. Since early 1950, several empirical correlations and mechanistic models have been developed to predict pressure drop. All correlations used by the industry, in addition to their applicability limitations, fall short of providing the necessary precision of pressure drop predictions. However, compared to empirical correlations, the recently developed mechanistic models improved pressure drop prediction. To design and construct more dependable and economical surface piping networks and wells, it is still necessary to improve prediction accuracy. This study uses the Adaptive Neuro-Fuzzy Inference System (ANFIS) to create a model that predicts pressure drop in horizontal and near-horizontal pipelines with greater accuracy and simplicity. Using the ANFIS method, the fuzzy modelling procedure can gather knowledge about a set of data to determine the membership function parameters that will enable the associated fuzzy inference system to track input/output data most effectively. The model was created and tested using field data encompassing various variables. The model was developed using 450 different data sets that were collected from the Asian continent. 113 data sets were used for testing, and a total of 337 data sets were used for training. Trend analysis was carried out during the model development phase prior to the model’s completion. This is performed to make sure the model is stable and to make sure the created model is physically sound and accurately simulates the real physical process. To determine the percentage of error between the predicted value and the actual measured data, statistical analysis was carried out. To compare the performance of the new ANFIS model to earlier empirical correlations and mechanistic models, graphical and statistical techniques were also used. The new model outperformed known correlations and the most recent mechanistic models by a significant margin in producing incredibly accurate pressure drop predictions. The Dukler et al. empirical correlation, Beggs and Brill empirical correlation, Xiao mechanistic model, and Gomez mechanistic model had values of 25.284, 20.940, 30.122, and 20.817, respectively, while the ANFIS model had a value of 13.256 for the lowest average absolute percentage error. Additionally, the Duckler and Beggs & Brill models came in second and third, with values of 0.908 and 0.906, respectively, and the ANFIS model had the highest coefficient of determination at 0.955.

Abstract Experimental work has been conducted to study the influence of gas injection on the phase inversion between oil and water flowing in a vertical pipe. A vertical transparent pipe test section line of 40 mm ID and 50 cm length was used. The test fluids used were synthetic oil and filtered tap water. Measurements were taken for mixture velocity, superficial water velocity, superficial gas velocity, and input superficial oil velocity ranging from 0.4 to 3 m/s, 0.18 to 2 m/s, 0 to 0.9 m/s, and 0 to 1.1 m/s. Most of the experiments were conducted more than two times, and the reproducibility of the experiments was quite good. Special attention was given to the effect of oil and water concentration where phase inversion took place with and without gas injection. The results showed that the phase inversion point was close to water fraction of ~ 30%, for both water friction direction changes (from water to oil or from oil to water) and that the effective viscosity increases once the mixture velocity increases. On the other hand, the results with gas injection showed that gas injection had no effect on the oil or water concentration where phase inversion occurred. Furthermore, the study investigated the effect of gas–oil–water superficial velocity on the total pressure gradient in the vertical pipe. It was found that the total pressure gradient was fast and increased at high superficial gas velocity but was slow at low superficial gas velocity. When the superficial oil velocity increased, the total pressure gradient approached the pressure gradient of an oil–water two-phase flow. The obtained results were compared with few correlations found in the literature, and the comparison showed that the uncertainty of the flow pattern transition peak in this study is very low.

Abstract Flow experiments have been conducted for two-phase flow in a vertical pipe. The experiments were made for highly viscous oil–water in a stainless pipe at 250 psig pressure through the laboratory-scale flow test equipment. The test section used is a vertical transparent tube of 50 cm length and 40 mm ID. The test fluid utilized in this experiments is synthetic oil (viscosity = 35 mPas, density = 860 kg/m3) and filtered tap water (interfacial tension 31 mN/m at 20 °C, viscosity 0.95 mPas at 25 °C). The measurements of superficial velocities of oil and water were varied between 0.01 to 3 m/s. According to the experimental observations using audiovisual recordings, a flow pattern map was identified at different condition. Measuring the variations in pressure gradient and flow patterns at different superficial velocities of two-phase flow, six typical flow patterns were categorized and mapped under two groups, oil-dominant region and water-dominant region, and categorized based on the variations of oil and water superficial velocities, and mixture fluid velocity, at different amount of the water holdup in the vertical tubing. The measurements of the total pressure gradient at five different mixture fluid velocities were conducted verses water holdup in vertical tubing. The results show that all the upward flows show an identical flow pattern, where the pressure gradients increase with increasing mixture fluid velocity and water cut with similar trend. The experiments show a clear peak in the pressure gradient as a result of the frictional factor, specifically at the point of flow patterns occurs (i.e., water holdup ~ 30%). The results concluded that the pressure gradient is significantly influenced by flow patterns and flow rates. Besides, the oil viscosity has a high effect on the pressure gradients; however, it is observed that at similar water and oil superficial velocities, there is a subsequent increase in the pressure gradient due to the increase in oil viscosity.

Application of carbon nanomaterials in oil well drilling fluid has been previously studied and was found to enhance its filtration properties. There is a general consensus that addition of colloids in suspension will alter its rheology, i.e., carbon nanomaterials, in this research work; graphene nanoplatelets are hydrophobic materials, which require functionalisation to improve its dispersion in aqueous solution. However, different degrees of dispersion may vary the rheological properties behaviour of drilling fluid. The objective of this study was to characterize the colloidal dispersion of graphene nanoplatelets (GNP) in aqueous solution and its impact on the rheological properties behaviour of water-based drilling fluid. Dispersion of graphene nanoplatelets was achieved through noncovalent functionalisation by means of surfactant attachment. UV-visible spectroscopy was employed to analyze the dispersion of GNP in aqueous solution. The rheological test was carried out using a simple direct-indicating viscometer at six different speeds. Results revealed that the degree of dispersion of GNP using Triton X-100 was generally higher than both SDS and DTAB. Comparison between the rheological properties behaviour of drilling fluid with GNP dispersed using different surfactants shows little to no difference at low shear rates. At high shear rates, however, greater dispersion of GNP shows higher thinning properties while fluid with a low dispersion of GNP exhibited linear behaviour to thickening properties.

In the original publication, third author name was incorrectly published as Meftah Hairir.