Upstream Oil and Gas Technology最新文献

Stuck-pipe phenomena can have disastrous effects on drilling performance, with outcomes that can range from time delays to loss of expensive machinery. In this work, we develop three indicators based on mudlog data, which aim to detect three different physical phenomena associated with the insurgence of a sticking. In particular, two indices target respectively the detection of translational and rotational motion issues, while the third index concerns the wellbore pressure. A statistical model that relates these features to documented stuck-pipe events is then developed using advanced machine learning tools. The resulting model takes the form of a depth-based map of the risk of incurring into a stuck-pipe, updated in real-time. Preliminary experimental results on the available dataset indicate that the use of the proposed model and indicators can help mitigate the stuck-pipe issue.

The main objective of this paper is to use experimental measurements of downhole pressure, temperature and initial mud density to predict downhole density using multigene genetic programming. From the results, the mean square error for the WBM density model was 0.0012, with a mean absolute error of 0.0246 and the square of correlation coefficient (R²) was 0.9998; while for the OBM, the MSE was 0.000359 with MAE of 0.01436 and R² of 0.99995. In assessing the OBM model's generalization capability, the model had an MSE of 0.031, MAE of 0.138 and mean absolute percentage error (MAPE) of 0.95%.

Pyridine, 2-hydroxy-6-methylpyridine, 3-hydroxypyridine, and 4-methoxypyridine are evaluated as potential phase-partitioning oil field tracers. Their stability is tested in a brine for 12 weeks at temperatures between 25 °C–150 °C, and at initial pH values of 5,5; 7,1; 8,0. Interactions with kaolinite clay, Berea sandstone and limestone are also evaluated. The main results are as follows: pyridine is stable up to 12 weeks at 150 °C, and not influenced by the rock substrates or pH. 2-hydroxy-6-methylpyridine becomes unstable at T ≥ 50 °C, is not affected by the rock substrates, and exihibts slower degradation kinetics at higher pH values. 3-hydroxypyridine is unstable at T ≥ 75 °C, sensitive to the presence of kaolinite in a combined effect with pH, and exihibts slower degradation kinetics at higher pH. 4-methoxypyridine degrades at T ≥ 75 °C, is characterised by a strong interaction with kaolinite, and is insensitive to pH.

The degradation of 4-methoxypyridine in the absence of kaolinite clay follows pseudo first-order kinetics. This compound could indicate the temperature in the swept volumes, and in conjunction with a fully conservative tracer indicate the presence of clays. Pyridine exhibits the required stability and lack of interaction with rock materials to be used as PITT tracer in oil reservoirs. However, it is present in oils and its concentration levels in production waters should be evaluated prior to its use.

Stuck pipe incidents are considered a very common challenge in the drilling phase, which can result in increasing non-productive time. Common recommended practices are used to prevent or reduce the severity of these incidents. The ability to predict these incidents based on some measured parameters has been applied in the industry by using different non-physical techniques such as Artificial Neural Networks. In this work, recursive partition analysis was used to develop classification trees. The data was collected from 385 wells drilled in Southern Iraq in different fields. A total of 1015 data points were collected and divided into three data sets: training, validation, and testing. The main objective of this work is to develop a model that consists of easily adoptable logical conditions that predict stuck pipe events and suggest an appropriate remedy to free the stuck pipe. The developed method was able to predict stuck pipe events with an accuracy of 90% using simple and limited input parameters. For the stuck pipe remedy model, the accuracy of the prediction for freeing the stuck pipe reached 84%. The proposed models for stuck pipe events and remedy predictions provide logical criteria based on simple quantities that can be easily applied in the field.

Three decades has passed since the introduction of smart water injection in carbonate rocks; however, use of diluted seawater (dSW) and its associated mechanisms are not yet well understood. Several mechanisms have been introduced in the literature for increased productivity of low salinity water injection. In this study, coreflooding tests were conducted to analyze the importance of one of the contribution mechanisms, the so-called rock dissolution mechanism. We used seawater as the baseline injecting phase, along with two dSW solutions, 5- and 20-folds dilution ratios as the low salinity solutions. Several pore volumes of the displacing phase were injected into real reservoir core plugs to recover the oil content. The impact of rock dissolution on oil recovery was evaluated by measuring core plug permeabilities before and after the flood as well as the recovery factor (RF) as a function of time, along with monitoring pH of the displacing phase at the inlet and effluent. The interaction of rock and fluid was closely monitored and analyzed by studying the injection pressure profiles. It was obtained that diluting the seawater intensified the rock dissolution. This mechanism was absent when unprocessed seawater was used to recover the oil.

Rock physics enhanced seismic strati-structural interpretation of the "Wuzozo Field" revealed two genetic units with the reservoir sands interpreted as channel fill deposits belonging to the Low Stand Systems (LST) and Trangressive Systems Tracts (TST) respectively. Estimated effective bulk moduli of the field revealed values between 23.55-36.74GPa for sand bodies while the results of the rock physics modeling revealed that the elastic moduli increases as porosity values decrease. Evaluation of the reservoir properties indicated that the pay zones are high-quality sands owing to their high effective porosities and net-to-gross ratio. In conclusion, the integration of rock physics with seismic stratigraphy has proved to be an invaluable tool for identifying prospective pay zones, especially at deeper subsurface intervals.

The electrochemical techniques included electrochemical impedance spectroscopy (EIS) and Tafel polarization. Changing GABA concentrations greatly impacted the rate of both the corrosion reaction and the evolution of hydrogen. The findings of polarization suggested that GABA is a mixed inhibitor of form. Rising the temperature (298–338 K) resulted in an intensification in the rate of hydrogen progression and a diminution in their steel's full superficial confrontation measure (RT) or comparative coverage width (1/CT). The inhibition capacity of GABA was demonstrated by the quantity control including E_HOMO, E_LUMO, the energy gap (∆E) and the segment of relocated electron (∆E).

Modelling lithofacies and petrophysical properties are the challenging processes at the beginning of exploration and production of hydrocarbon reservoirs. However, the limited amount of well data and core data are the main issues facing conventional modelling processes. In this study, Artificial Neural Network (ANN), Sequential Gaussian Simulation (SGS), Co-kriging and object-based modelling (OBM) were integrated as the enhancement framework for lithofacies and petrophysical properties modelling in the fluvial channel sandstone reservoir.

In the OBM, multiple fluvial channels were generated in the lithofacies model. The result of this model represented all the characteristic of the fluvial channel reservoir. The model was then distributed with channels, crevasse, and leeves depositional facies with background shale. Multiple geological realizations were made and cross-validation to select the most suitable lithofacies distribution. This model was cross-validated by modelling the porosity and permeability properties using Sequential Gaussian Simulation.

Thereafter, the modelling process continued with Artificial Neural Network. Petrophysical properties (mainly porosity and permeability) were predicted by training various seismic attributes and well log data using the ANN. Applying the co-kriging algorithm, the predicted ANN model was integrated with OBM simulated lithofacies model to preserve the fluvial features of the geological system. To achieve full field history matching, the final geological model was upscaled to serve as input data in dynamic history matching.

An excellent and nearly perfect history matching with a least mismatch was obtained between the measurement and simulated bottom hole pressure from well test and production history. The results indicated that an efficient integrated workflow of ANN and other geostatistical approaches are imperative to attaining an excellent history matching.

Despite the known practical applications of remote sensing in a wide range of industries and situations, it has not been used extensively in petroleum exploration, which has relied mostly on geological and / or geophysical surveys. With the advances made in sensing equipment since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) became operational, extraction of potential indicators such as: surface emissivity, surface kinetic temperature, brightness temperature and surface radiance need to be re-evaluated in the context of petroleum exploration.

Land Surface Temperature (LST) anomalies associated with geothermal activity was used, to determine if petroleum in place, can be the source of thermal anomalies detected at surface, by ASTER. A study area, in onshore Iran, consisting of 53 existing petroleum-rich areas was selected. Twenty (20) ASTER scenes (Granules) covering the study area were provided by National American Space Agency (NASA).

In order to highlight subsurface contributors to geothermal anomalies detected at surface, assumed to be due to underlying hydrocarbons, ASTER images were processed to minimize temperature variations caused by any sources other than the underlying hydrocarbons. In order to assign weight to the role-playing variables according to their degree of influence over the final LST, fuzzy logic was employed, as the main processing approach. In the resulting processed maps, following the removal of all the contributors to LST, the remaining thermal anomalies, which were reduced up to 80% in some areas of the study area, could then be linked to the underlying hydrocarbons. The final results indicate that, even after the influence of all the contributors to LST has been removed, still 67.9% (36 out of 53 petroleum containing areas) of the pixels within the buffer zones of petroleum reservoirs of the study area are considered to be thermally-anomalous.

Continuity of thermal anomalies, in different directions, was also investigated within the buffer zones of each of the petroleum reservoirs, by Variograms. The results of the Variogram analysis indicate that even if the detected thermal anomalies within the buffer zones of some of the reservoir of the study area were not high, the direction of thermal anomalies strongly follow the NW-SE direction, which is the direction along the trend line of elongation axes of petroleum reservoirs. Details of the spatial analysis post LST processing will be reported.

By employing GIS and fuzzy logic, a dynamic model was developed, with variable input data from geology and ASTER, which could be customised and changed according to target area's thermal specifications and characteristics.