Accurate Pore Pressure Prediction Research Articles

Pore pressure prediction of hydrocarbon reservoirs with empirical models and artificial neural network: case study in the Doba basin, Chad

Accurate Pore pressure prediction is essential in drilling as it enables drilling hazards to be avoided. However, traditional methods of pore pressure prediction, especially empirical methods are field-specific. It is therefore important to evaluate the best empirical method which is to be applied in any basin or field according to its main characteristics. Besides, recently, artificial neural networks (ANNs) are the most used for pore pressure prediction because they can help to learn complex relationships between input and output parameters. They lead to accurate pore pressure prediction results in many cases. In light of this, the aim of this study carried out in the Doba basin is to analyse and compare the applicability of various sonic log-based empirical models of pore pressure prediction, including Eaton’s model, Raymer-Hunt’s model and Kozeny-Carman’s model and a ANN model of pore pressure prediction in order to find out the best predictive one to use in the studied area in order to achieve better prediction results. To achieve this objective, firstly, the mathematical models of pore pressure prediction from Eaton, Terzaghi and Raymer-Hunt equations and Kozeny-Carman’s model were used. The artificial neural network method uses 33 datasets of well log data including sonic transient time, vertical stress and porosity as input parameters to train the ANN model. The output parameters are the measured pore pressures produced by the Repeat Formation Tester. Using 70% of data set for training, 15% for test and 15% for validation, the trained model with the Bayesian Regularization Backpropagation function predicts pore pressure with an average correlation coefficient of 98%. The results show that pore pressure obtained from the Kozeny-Carman’s model, also has a 98% correlation coefficient, thus proving to be the most accurate of empirical methods. Indeed, pore pressures obtained from Raymer-Hunt’s model and Eaton’s model have 97% and 90% respectively as correlation coefficients. This study provides the best and most reliable empirical method of sonic log-based pore pressure prediction to use in the Doba basin, which can be used to achieve the best pore pressure prediction results and hence ensures drilling safety and wellbore stability in the studied area. The study also highlights a good application of the Bayesian function of the ANN for pore pressure prediction in the investigated area.

Advances in rock physics for pore pressure prediction: A comprehensive review and future directions

Advances in rock physics have significantly enhanced pore pressure prediction, a critical aspect of subsurface exploration and drilling operations. This comprehensive review delves into the latest developments in rock physics methodologies, integrating empirical, theoretical, and computational approaches to predict pore pressure more accurately. Traditional pore pressure prediction methods often rely on well log data and seismic attributes, but recent advancements have introduced innovative techniques that leverage the physical properties of rocks to provide more reliable predictions. Key advances include the development of improved rock physics models that better account for the complexities of subsurface environments, such as heterogeneity and anisotropy. These models integrate data from various sources, including well logs, core samples, and seismic surveys, to create a more comprehensive understanding of the subsurface. Additionally, the application of machine learning and artificial intelligence to rock physics has opened new avenues for analyzing large datasets, identifying patterns, and refining predictive models. This review also examines the role of laboratory experiments and field studies in validating and calibrating rock physics models. High-pressure and high-temperature experiments have provided valuable insights into the behavior of rocks under different conditions, which are essential for accurate pore pressure prediction. Field studies, on the other hand, offer real-world data that help in fine-tuning models and methodologies. Future directions in rock physics for pore pressure prediction include the integration of advanced geophysical techniques, such as full-waveform inversion and distributed acoustic sensing, which offer higher resolution data and more detailed subsurface imaging. The use of cloud computing and high-performance computing platforms is also expected to enhance the processing and analysis of large datasets, making predictive models more efficient and scalable. The comprehensive review concludes by highlighting the importance of interdisciplinary collaboration in advancing rock physics methodologies. By combining expertise from geophysics, petrophysics, geomechanics, and data science, the field can continue to innovate and improve the accuracy and reliability of pore pressure predictions, ultimately enhancing exploration and production efficiency in the oil and gas industry. Keywords: Advances, Rock Physics, Pore Pressure, Prediction, Future Directions.

ENHANCING PORE PRESSURE PREDICTION IN OIL WELL DRILLING: A COMPREHENSIVE STUDY OF WELL PLANNING AND COST-EFFECTIVE MODELING IN THE NIGER DELTA REGION

Accurate pore pressure prediction is pivotal in drilling operations, impacting safety, well design, and cost-effectiveness. This research paper aims to assess various pore pressure prediction methods, particularly in transition zones. It introduces a novel descriptive model for quick and reliable pore pressure estimation when essential data parameters are unavailable. The study underscores the importance of selecting appropriate prediction methods based on geological conditions. The research findings reveal that Eaton’s correlation utilizing transit compressive wave velocity offers superior pore pressure prediction when drilling through transition zones. Additionally, the developed descriptive model is a valuable tool for quick look pore pressure estimation, ensuring operational efficiency when data parameters for traditional methods like Eaton’s are lacking. However, a critical caveat emerges as the model’s suitability for pore pressure prediction in fractured or shaled-out reservoirs is questioned, necessitating caution in its application in such geological settings. This paper recommends the continued use of Eaton’s method as a reliable pore pressure prediction tool and advocates for the adoption of the proposed descriptive model in scenarios where time constraints or data limitations are prevalent. By amalgamating these approaches, drilling operations can achieve enhanced accuracy and efficiency in pore pressure assessment, ultimately contributing to safer and more cost-effective well-drilling processes.

Pore Pressure Prediction Using Velocity-Mean Effective Stress Relationship in Madura Sub-basin, East Java Basin - Indonesia

DOI:10.17014/ijog.9.2.247-262Pore pressure prediction using velocity-mean effective stress relationship is introduced in Madura Subbasin which is located in a compressional tectonic setting. The new workflow accommodates the three principal stresses, those are vertical stress, maximum horizontal stress, and minimum horizontal stress in a form of mean stress as the compaction main inducing agent. The application of this new workflow has resulted in a more accurate pore pressure prediction where the normally used pore pressure prediction method tends to underestimate the actual data. In Dukuh-1 well, as an example, the pore pressure prediction from the new work flow is able to explain the continuous hole problems such as well flows and well kicks that were recorded during drilling. In MDA-2 well, the new pore pressure prediction closely matches the well data in comparison to the normally used prediction. As a result, a more accurate regional pore pressure prediction map using mean stress is generated to aid both hydrocarbon exploration and development activities in the study area

The role of pore pressure and its prediction in deep geothermal energy drilling – examples from the North Alpine Foreland Basin, SE Germany

Pore pressure prediction is a well-developed key discipline for well planning in the hydrocarbon industry, suggesting a similar importance for deep geothermal wells, especially, since drilling cost is often the largest investment in deep geothermal energy projects. To address the role of pore pressure prediction in deep geothermal energy, we investigated pore pressure-related drilling problems in the overpressured North Alpine Foreland Basin in SE Germany – one of Europe's most extensively explored deep geothermal energy plays. In the past, pore pressure was mainly predicted via maximum drilling mud weights of offset hydrocarbon wells, but recently more data became available, which led to a re-evaluation of the pore pressure distribution in this area. To compare the impact of pore pressure and its prediction, 70% of all deep geothermal wells drilled have been investigated for pore pressure-related drilling problems and two deep geothermal projects are given as more detailed examples. Thereby, pore pressure-related drilling problems were encountered in one third of all wells drilled, resulting in several side-tracks and an estimated drilling rate decrease of up to 40%, highlighting the importance of accurate pore pressure prediction to significantly reduce the cost of deep geothermal drilling in overpressured environments. Thematic collection: This article is part of the Geopressure collection available at: https://www.lyellcollection.org/cc/geopressure

Combining porosity and resistivity logs for pore pressure prediction

Pore pressure prediction represents an important safety aspect of drilling engineering. Accurate pore pressure prediction is required for appropriate mud weight usage. Kick can occur when mud weight is lower than pore pressure gradient and this can result in disastrous events such as blowout when the kick is not properly controlled. Likewise, too high mud density can fracture the reservoir which can lead to several problems. Thus, the need to research on means of improving accurate pore pressure prediction during drilling is in order. In this article, two methodologies are presented. One of the methodologies is developed to utilize resistivity data for pore pressure prediction, and the other methodology is developed if resistivity and porosity data are available for pore pressure prediction. Several methodologies already exist for pore pressure prediction with resistivity data. Therefore, the methodology presented in this article is compared to other resistivity-based methodologies in order to observe their pore pressure prediction capabilities. Field data is used for testing prediction performance in terms of mean absolute percentage error, root mean square error and Pearson product moment correlation coefficient. Results of the test show that the methodology developed in this article performed best. Different logging/measurement parameters are used for pore pressure prediction e.g., resistivity log, sonic velocity, corrected d-exponent, etc. One way to improve accuracy of pore pressure prediction is utilizing pore pressure prediction from different logging/measurement parameters. For the other methodology which utilizes resistivity and porosity for pore pressure prediction, the methodology is proposed to utilize the change in Archie's cementation exponent. This is because the effect of cementation on pore pressure prediction could become significant at greater depths. Testing with field data showed that this methodology also performs better than simply averaging pore pressure prediction from resistivity and porosity logs using other conventional equations. In addition to the methodology developed for combining porosity and resistivity log for pore pressure prediction, machine learning can also be utilized. Results obtained using artificial neural network indicate better performance in comparison to simply averaging predictions from conventional means of using resistivity and porosity. • A novel approach that combines resistivity and porosity data for pore pressure prediction is presented. • The approach utilizes the change in Archie's cementation exponent to predict pore pressure. • The approach is tested using field data. • The testing confirms proposed approach performed better than the conventional method.

A machine learning methodology for multivariate pore-pressure prediction

Accurate pore-pressure prediction is of essential importance to hydrocarbon exploration and development. A multivariate prediction model of multiple petrophysical data is required to adequately reflect variations in pore pressure. However, the parametric multivariate models with assumptions on lithology, predominantly sand or shale, are theoretically inaccurate for mixed lithologies and require a tedious calibration process. Here, we propose a new method of utilizing machine learning (ML) techniques for pore-pressure prediction with a nonparametric multivariate model of petrophysical properties (sonic velocity, porosity, and shale volume). The training dataset for the ML models is constructed using petrophysical properties extracted from well log data and theoretical effective stress in the normally compacted interval. Bowers’ unloading relation is invoked herein to account for abnormal pressure generated by unloading. Four ML algorithms, including the multilayer perceptron neural network, support vector machine, random forest, and gradient boosting machine, are applied to well log data from a set of offshore exploration wells. The results suggest that the proposed method using ML makes pore-pressure predictions in good agreement with pore-pressure measurement, and random forest outperforms the other ML algorithms in terms of goodness-of-fit, generalizability, and prediction accuracy. Compared with methods based on parametric models, the proposed method based on ML produces more accurate pore-pressure prediction and better capture the onset of overpressure.

Pore pressure prediction of some selected wells from the Southern Pletmos Basin, offshore South Africa

An accurate pore pressure prediction is vital in any risk management plan during the exploration, exploitation and development of wells in the petroleum industry. It is therefore important to understand the factors controlling these pore pressures namely, sediment compaction, overburden, lithology characteristics, hydrocarbon generation, tectonic stress, thermodynamic effects, osmosis and clay mineral transformation, which are all influenced by physical, geological, geochemical and mechanical processes. In this study we present the results of data selected from three wells: Ga-N1, Ga-W1 and Ga-AA1, which were all drilled within the Southern Pletmos sub-basin, offshore South Africa. The pore pressure in the wells was predicted using the Ben Eaton method by creating a depth dependent Normal Compaction Trend (NCT), using resistivity and sonic wireline logs. 2D seismic data were used to develop depth imaging of the pre-drilling pore pressure predictions; this was done using seismic interval velocity data and pressure data to obtain a reflection tomography extraction grid map. Detailed depth plots of overburden gradient (OBG), the Effective Stress (ES), Fracture Gradient (FG), Fracture Pressure (FP), Pore Pressure Gradient (PPG), and the Predicted Pore Pressure (PPP) were thus derived for the three wells. The overburden density varies from 2.09 gm/cc to 2.24 gm/cc between the wells, while the PPP changes from 3,405 psi to 5,062 psi within the selected reservoir intervals. The results show that most of the reservoir intervals of the three wells experience normal pore pressures (between 3000 and 4000 psi), except the reservoir intervals between depths 1868.73 m to 1875.40 m and 1880.3 m to 1887.3 m, respectively, in well Ga-W1, where significant overpressure in the pores exceeding 4000 psi was demonstrated (normal pore pressures are between 3000 and 4000 psi). The tomography extraction grid map that was generated from 2D seismic data of the wells was used to delineate the depth imaging of the pore pressure, in both overpressure and normal pressure formations before drilling, yielded good results.

English

Knowledge of formation pore pressure ahead of the drill bit is not only critical for safe and cost-effective drilling of wells but also essential in studying the hydrocarbon trap seal and analyzing the trap configurations. A predrill estimate of the formation pore pressure can be obtained from seismic velocities and employing a velocity-to-effective stress transform. However, limitations abound in the use of seismic velocities for accurate pore pressure prediction. These limitations are traceable to some main factors such as the correctness of the seismic velocities themselves and the accuracy of the local parameters of the pore pressure prediction method used. Knowledge of the sources of overpressure in the formation is also an essential factor. This paper discusses these factors and attempts to put them into context for accurate pore pressure prediction using seismically-derived velocities.   Key words: Pore pressure prediction, overpressure, geopressure, seismic velocities, tomographic inversion, seismic inversion.

Pore-Pressure and Wellbore-Stability Prediction To Increase Drilling Efficiency

Technology Today Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering. Abstract Pore pressure and wellbore stability pose significant challenges to exploration drilling, particularly in regions featuring weaker rocks or subsalt formations. A lack of accurate pore-pressure prediction and wellbore-stability analysis brings many problems, such as blowouts, kicks, hole washouts, wellbore breakout, and stuck pipe. Wellbore instability also adds to drilling time and increased costs, and sometimes leads to abandoning the well before it reaches its objective. Estimates put the cost of these issues at approximately 10% of total drilling time on average. Ending wellbore-instability problems begins with the ability to predict conditions accurately and to reduce the chance of running into trouble. In this paper, commonly used methods for analyses of pore pressure, in-situ stress, and borehole shear failure are evaluated for their strengths and weaknesses. Examples are provided that demonstrate that the integration of predrill pore-pressure and geomechanics analyses with real-time monitoring consistently provides an effective way to mitigate predrill uncertainties and improve well-construction efficiency.

Origin of overpressure and pore-pressure prediction in the Baram province, Brunei

Accurate pore-pressure prediction is critical in hydrocarbon exploration and is especially important in the rapidly deposited Tertiary Baram Delta province where all economic fields exhibit overpressures, commonly of high magnitude and with narrow transition zones. A pore-pressure database was compiled using wireline formation interval tests, drillstem tests, and mud weights from 157 wells in 61 fields throughout Brunei. Overpressures are observed in 54 fields both in the inner-shelf deltaic sequences and in the underlying prodelta shales. Porosity vs. vertical effective stress plots from 31 fields reveal that overpressures are primarily generated by disequilibrium compaction in the prodelta shales but have been generated by fluid expansion in the inner-shelf deltaic sequences. However, the geology of Brunei precludes overpressures in the inner-shelf deltaics being generated by any conventional fluid expansion mechanism (e.g., kerogen-to-gas maturation), and we propose that these overpressures have been vertically transferred into reservoir units, via faults, from the prodelta shales. Sediments overpressured by disequilibrium compaction exhibit different physical properties to those overpressured by vertical transfer, and hence, different pore-pressure prediction strategies need to be applied in the prodelta shales and inner-shelf deltaic sequences. Sonic and density log data detect overpressures generated by disequilibrium compaction, and pore pressures are accurately predicted using an Eaton exponent of 3.0. Sonic log data detect vertically transferred overpressures even in the absence of a porosity anomaly, and pore pressures are reasonably predicted using an Eaton exponent of 6.5.

An introduction to velocity-based pore-pressure estimation

Abnormal pore pressures are encountered worldwide, often resulting in drilling problems such as kicks, blow-outs, borehole instability, stuck pipe, and lost circulation. Because of this, a quantitative predrill prediction of pore pressure is required for the safe and economic drilling of wells in overpressured formations. In this paper, the basic concepts used in pore-pressure prediction are defined, and the way in which pore pressure can be estimated using velocity measurements is explained. A predrill estimate of pore pressure can be obtained from seismic velocities using a velocity-to-pore-pressure transform calibrated with offset well data. However, velocities obtained from processing seismic reflection data often lack the spatial resolution needed for accurate pore-pressure prediction, due to assumptions such as layered media and hyperbolic moveout. In the following paper in this section, Chopra and Huffman review the available methods of velocity model building and discuss the advantages and disadva...

Pore pressure prediction using well-conditioned seismic velocities

Abnormal pore pressures are encountered worldwide, often resulting in drilling problems such as borehole instability, stuck pipe, lost circulation, kicks, and blow-outs (Dutta, 1997). To optimize the choice of casing and mud weight while drilling abnormally pressured formations, a pre-drill prediction of pore pressure is required. A pre-drill estimate of pore pressure can be obtained from seismic velocities using a velocity to pore pressure transform calibrated from offset well data. However, velocities obtained from processing seismic reflection data often lack the spatial resolution needed for accurate pore pressure prediction, due to assumptions such as layered media and hyperbolic moveout. In addition, the uncertainty in velocity is often not quantified. In this example from the Gulf of Mexico, seismic velocities obtained using reflection tomography are combined with well data to produce a refined velocity field that honours the available well information. The refined velocity field is then used to predict pore pressure.