Eaton Method Research Articles

Formation pressure prediction method based on machine learning

Abstract Formation pore pressure is one of the key parameters in petroleum engineering. The high-temperature and high-pressure working conditions formed under the complex mechanism of Yingqiong Basin make downhole complex events such as overflow and well leakage occur frequently. To address the problem of the large prediction error of the traditional Eaton method under high temperature and high-pressure conditions, three machine learning methods, such as GA-BP(Genetic Algorithm-Back Propagation Neural Network), PSO-LSTM(Particle Swarm Optimization-Long Short-Term Memory Neural Network), SVR(Support Vector Regression), are introduced to find the nonlinear relationship between logging parameters and formation pore pressure. Seven logging parameters such as formation depth, mechanical drilling speed, drilling pressure, and rotational speed were used as input variables, and formation pore pressure was used as an output variable through correlation analysis for constructing the machine learning model. By analyzing the experimental results of the above three machine learning models with the Eaton method, the GA-BP model with lower error and more satisfying the needs of drilling engineering is preferred, and the various regression evaluation indexes of the model are also more excellent compared with other machine learning models, which are Mean Squared Error (MSE)=0.001, Mean Absolute Error (MAE)=0.003, and R-squared (R2)=0.991, respectively. It is concluded that the machine learning method is not only more advantageous in a class of nonlinear regression problems such as formation pore pressure prediction, but also can help engineers to predict formation pore pressure more accurately under the conditions of deep high-temperature, high-pressure, and complex formations, which contributes to guaranteeing the safety and high efficiency of drilling projects.

Sand Production Prediction and Management Using a One-Dimensional Geomechanical Model: A Case Study in NahrUmr Formation, Subba Oil Field

Sand production in sandstone reservoirs is a complex problem for oil and gas companies. This problem can damage downhole equipment and surface production facilities. This paper presents a sand production case and quantifies sand risks for the NahrUmr formation located in Subbaoilfied.The risk of sanding or rock failure is commonly observed during production phases as well as drilling operations. A (1D) mechanical earth model was created using wireline log data and core data for estimating the formations mechanical properties.The extrapolation method is utilized for vertical stress prediction, whereas pore pressure and static Young’s modulus were calculated using Slowness Eaton method and John Fuller correlation respectively. The poro-elastic model was used to determine horizontal principal stresses and hydrocarbon intervals susceptible to sand production issues.The purpose of this study was achieved using Tech-Log software. Sand onset intervals are firstly predicted as an open hole using ten methods: Loading factor (LF), Critical drawdown pressure (CDDP), B-index, schlumberger (Sc), Elastic combination index (Ec-index), Interval Transit-Time (DTc), Total porosity method (PHIT), unconfined compressive strength (UCS), Shear modulus to bulk compressibility ratio (G/Cb), and shear failure method. Approximately ten methods refer to the same intervals that can produce sand. Secondly, sand depths were predicted by representing the well according to what actually existed as a cased hole by calculating the critical drawdown pressure (CDDP), which is calculated at various depletion rates (0%, 15%, 25%, and 35%) in order to measure the effect of reservoir pressure depletion on sand production. The results show that as the rock strength is increased, the amount of sand-free drawdown and depletion becomes larger. Also, a single-depth analysis was conducted for problem intervals, indicatg a sandwillproducefromdepth of 2527.7 m under certain conditions. Therefore, it is necessary to install sand control equipment in this well.

Estimating pore pressure in tight sandstone gas reservoirs: A comprehensive approach integrating rock physics models and deep neural networks

Pore pressure serves as an important driving power for subsurface fluid migration and therefore has a significant impact on gas accumulation and enrichment in tight sandstone reservoirs. Tight gas is typically produced in overpressure regions, where pressure coefficients are notably elevated. Thus, it is crucial to establish an effective methodology for precise pore pressure estimation. This study introduces an approach to improve pore pressure prediction by incorporating rock physical modeling and deep neural networks (DNNs) into the classical Eaton method. Compared to conventional techniques relying on empirical correlations between pressure coefficients and elastic properties, the proposed method considers the influence of porosity, fluids, and lithology, which could enhance reliability in pore pressure prediction. Meanwhile, a prediction model is developed using logging data and DNNs to estimate mineralogical volumetric fractions based on elastic properties. This prediction model allows improved estimation of rock matrix elastic properties using seismic-inverted data, which is crucial for estimating normal compaction velocity to extend pore pressure prediction from individual boreholes to the whole study area. Real data applications demonstrate that the predicted pressure coefficients derived from seismic data using the method presented in this paper align well with the gas enrichment estimated in previous studies for the tight sandstone reservoirs. Furthermore, regions with high values of pressure coefficients correspond to high gas content. These findings validate the effectiveness of the proposed methodology, which can provide valuable insights for identifying potential tight sandstone reservoirs.

BRIEF UNDERSTANDING OF CHARACTERISTICS DEPLETED RESERVOIR (INTERVAL-I) USING PORE PRESSURE AND FRACTURE GRADIENT ANALYSIS IN "X" FIELD, SANGA-SANGA AXIS, KUTAI BASIN

Field X, as this study area's focus, boasts over 200 wells proven to be hydrocarbon producers. Two pore pressure regimes exist: normal hydrostatic and overpressure. The primary hydrocarbon reservoir in the Balikpapan Formation within the Sanga- sanga axis at Field X consists of sandstone. Drilling disturbances, particularly in the lower intervals of this reservoir, often occur due to overpressure. The main challenge encountered in this reservoir is mud losses. The objective of this study is to develop the Pore Pressure and Fracture Gradient characteristics and its relation to the geological environment. Furthermore, the distribution of Sand (Interval-I) is determined, which is the primary cause of losses due to production. Besides that, the parameters used to create optimal fracture gradient is also defined. Thirteen wells in this field were examined to identify the controlling factors of pore pressure. This study integrates wireline logging, velocity, mud logs, pressure tests, drilling parameters and event which are subsequently processed for determining shale points, Normal Compaction Trend (NCT), calculating overburden gradient, and estimating pore pressure using the Eaton method. From the analysis result, the distribution of overpressure and the generating mechanism of overpressure are determined. This study also carried out analysis to determine overpressure and depleted Sandstone (Interval-I) distribution. Subsequently, facies and depositional environtment analysis are conducted, followed by modelling Sandstone (Interval-I) and determining Sandstone’s poisson ratio from loss data and shale’s from leak of test data for fracture gradient estimation. Overpressure is found in Interval-I with magnitude of 4000-4700 psi, which corresponds to delta plain to delta front depositional environment. Top of overpressure is observed shallower in the southern than northern due to geological structure conditions. The generating mechanism of overpressure is caused by loading and hydrocarbon generation. The experienced loss in overpressure zone, caused by reservoir production from the initial pressure of 4475 psi and has depleted to 373 psi. From the sandstone distribution model, the loss sandstones are a connected fluvial channel in southern ward. It is also observed several unconnected channel sandstones that did not experience loss. Poisson ratio parameter for sandstone is 0.35, which is calibrated from the loss event with minimum fracture gradient in Interval-I is approximately 4772 psi. It is expected that the understanding of pore pressure and Sandstone (Interval-I) distribution could be used to increase the success ratio for optimal drilling planning, including to choose effective casing design, mud weight, and appropriate total depth.

Necessary Extremum Conditions and the Neustadt–Eaton Method in the Time-Optimal Control Problem for a Group of Nonsynchronous Oscillators

Necessary Extremum Conditions and the Neustadt–Eaton Method in the Time-Optimal Control Problem for a Group of Nonsynchronous Oscillators

Predicating of Formation Pore Pressures in Tertiary Reservoirs Using Geophysical Wireline Log Data

Changes to certain geophysical characteristics, such as sonic transit time with depth, indicate the pressure system of over-pressured zones. In this study, well-log data is used to investigate the distribution of formation overpressures in the tertiary reservoirs of one Iraqi oil field. Three wells located within the tertiary carbonate reservoir of one Iraqi oil field are of interest in this study. To assess the reservoirs, a petrophysics log analysis was carried out. The reservoir's intervals are the area of interest. The sonic velocity data was used to create a normal compaction trend line, and the overpressure zones were identified by observing the reversals in the normal compaction trend line. The pore pressure, fracture pressure, and gradients were calculated using the Eaton method. The method relies on the correlation between the normal transit time found on the normal compaction trend and the observed transit time from the log reading. The sonic log determines the bulk density and matches the density derived from the density log. Abnormal pressures were identified primarily within the area of interest, especially with unit B in Well 1. In Well 2, the abnormal pressures were recognized in zones above the area of interest. Abnormal pressures in Well 3 were identified primarily within all areas of interest. The importance of this work is to help predict overpressure zones in the research area before drilling or workover.

Impact of disequilibrium compaction and unloading on overpressure in the southern Junggar Foreland Basin, NW China

Disequilibrium compaction caused by burial or tectonic compression and unloading are the common overpressure mechanisms in foreland basins. However, accurately identifying multiple overpressure origins in such basins is challenging, since it requires distinguishing impact of vertical and horizontal stresses on compaction and overpressure as well as identifying overpressure caused by unloading. This paper proposed a dual compaction factor method (DCFM) to address these issues. A basic DCFM schematic diagram was established through analyzing compaction behaviors before and after tectonic compression to distinguish overpressure derived from two types of disequilibrium compaction and overpressure induced by unloading. In DCFM, a compaction model for mudstone in the foreland basin, which was only controlled by burial, was established based on the relationship between mean deposition rate and compaction factor in weak tectonic compression zone (WTCZ). It was used to determine compaction factor, normal compaction trend (NCT), acoustic transit time, bulk density, and overburden pressure under this compaction condition, as well as to distinguish the impact of burial and tectonic compression on porosity and overpressure at strong tectonic compression zone (STCZ). The DCFM employs mean stress to revise the equivalent depth method (EDM). Pore pressures associated with compaction only derived from burial and derived from both burial and tectonic compression were estimated using conventional EDM and revised EDM, respectively. The total pore pressures were estimated using the Eaton method and were calibrated based on measured ones. Overpressure associated with unloading was determined by comparing total pore pressure with estimated pore pressure generated with disequilibrium compaction. This mode was further applied at two wells from the Sikeshu Sag in the southern Junggar Foreland Basin to reconstruct accurate overpressure profiles with multiple origins, which were consistent with measured pressures and matched well with geological characteristics. Disequilibrium compaction induced by tectonic compression and burial is responsible for overpressure at the Sikeshu Sag. Overpressure transfer is also one of the primary contributors to the excess pressure in the Jurassic and Cretaceous in the Gaoquan anticline at the Sikeshu Sag, with a maximum contribution of 38 %. Plays, consisting of sandstone reservoirs with both transferred overpressure and tectonic-induced overpressure as well as mudstone caprocks with overpressure due to disequilibrium compaction, are favorable for hydrocarbon accumulation. This study can quantify the impact of overpressures with multiple origins on hydrocarbon migration and sealing capacity of caprocks, as well as provide ideas for researching fluid evolution in sedimentary basins.

In situ stress prediction method for decoupled overburden pressure under tectonic constraints

Abstract The prediction of in situ stress based on azimuthal seismic data has extensive use in horizon crushability evaluation. Nonetheless, existing in situ stress seismic prediction models do not consider overburden pressure and tectonic strain, which limit the prediction accuracy. To this end, we propose a decoupled overburden pressure in situ stress prediction method under tectonic constraints. The key to this method is to consider that the overburden pressure could act on the rock skeleton and pore fluid, i.e. generating effective pressure and pore pressure; the pore pressure can be estimated using Eaton's method, and then the effective stress can be obtained. The relationship between tectonic strain and effective pressure is constructed based on Hooke's law, where tectonic strain can be calculated from curvature attributes extracted from seismic data. Introducing pore pressure and deriving a model for calculating the maximum and minimum horizontal stresses and the difference in horizontal stress ratio for orthotropic media (OA). When the Thomson anisotropy parameters and the pore pressure are neglected, the proposed model can be degraded to a conventional horizontal transverse isotropy medium in situ stress prediction model, which proves the validity of the model. The results of sensitivity analysis experiments affirm the need to decouple overburden pressure and account for tectonic strain when predicting in situ stress reasonably. Finally, single-well and azimuthal seismic prediction were carried out by using the a priori information from well logging and seismic data.

Kinetics of insulin and C-peptide and estimation of prehepatic insulin secretion rates after intravenous glucose stimulation using arterial versus venous blood sampling in healthy males

An intravenous glucose-infusion of 0.3 g glucose per Kg body weight was administered over 1 min in nine healthy males with simultaneous blood sampling from the hepatic vein, femoral artery and a peripheral vein. Insulin secretion rates (ISR) were determined by the Eaton method and the ISEC method using C-peptide concentrations from arterial and peripheral venous blood. First phase (0–10 min), second phase (10–60 min), and total insulin secretion (0–60 min) were calculated as the incremental areas (iAUC) above baseline. The primary endpoint was first phase insulin response. The first phase insulin response in artery and venous blood did not differ with the Eaton method (p = 0.25), but was significantly greater with the ISEC method in arterial compared with venous blood (p < 0.05). The first phase insulin responses did not differ between methods in artery (p = 0.73) or venous blood (p = 0.73). The first phase responses of insulin and C-peptide were significant higher in the hepatic vein compared with those in the artery (p < 0.05) and peripheral vein (p < 0.05) but did not differ significantly between the artery compared with the peripheral vein for insulin (p = 0.09) or C-peptide (p = 0.26). Prehepatic insulin secretion rates did not differ between the Eaton and ISEC methods, but with the ISEC method the first phase insulin response was significantly greater in arterial compared with venous blood. The first phase insulin response differs when calculated from plasma insulin or C-peptide and depends on sample sites.

A machine learning approach for the prediction of pore pressure using well log data of Hikurangi Tuaheni Zone of IODP Expedition 372, New Zealand

Pore pressure (PP) information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development. PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design, and mud weight estimation for wellbore stability. However, the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting, where sedimentation rate is relatively high and PP can be driven by various complex geo-processes. To overcome these difficulties, an advanced machine learning (ML) tool is implemented in combination with empirical methods. The empirical method for PP prediction is comprised of data pre-processing and model establishment stage. Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372. Gamma-ray, sonic travel time, bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction. The normal compaction trend (NCT) curve analysis is used to check the optimum fitting of the low permeable zone data. The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development. The dataset is prepared and divided into two parts: Training and Testing. The PP data and well log of borehole U1517A is pre-processed to scale in between [-1, +1] to fit into the input range of the non-linear activation/transfer function of the decision tree regression model. The Decision Tree Regression (DTR) algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.

Pore pressure prediction assisted by machine learning models combined with interpretations: A case study of an HTHP gas field, Yinggehai Basin

Accurately estimating pore pressure in high-temperature and high-pressure (HTHP) formations is crucial to prevent drilling problems and engineering disasters. In this study, four machine learning models were developed, namely decision tree, random forest, gradient boosting decision tree, and extreme boosting decision tree models, along with the classical Eaton's method, to rapidly predict pore pressure in an HTHP gas field in the Yinggehai Basin using seven variables, including ‘depth’ and six common well-logging series. The results demonstrate that the machine learning models outperform the classical Eaton's method in pore pressure prediction. Among the models, the decision tree model exhibits the best performance, with the highest correlation coefficient (R2 = 0.98) and the lowest root mean squared error (0.61). Additionally, the Graphviz tool and Shapley additive explanations method were used for the visualization and interpretation of the decision tree model to gain a deeper understanding of the model's working mechanism and the reasons behind the obtained results. The interpretation revealed that the variable of ‘depth’ was the most significant input parameter in the decision-making process of the model. This study provides insights into the ‘black-box’ interpretation of machine learning models and demonstrates that tree-based machine learning models can accurately predict pore pressure in undersurface HTHP formations.

Pore pressure prediction in offshore Niger delta using data-driven approach: Implications on drilling and reservoir quality

Despite exploration and production success in Niger Delta, several failed wells have been encountered due to overpressures. Hence, it is very essential to understand the spatial distribution of pore pressure and the generating mechanisms in order to mitigate the pitfalls that might arise during drilling. This research provides estimates of pore pressure along three offshore wells using the Eaton's transit time method, multi-layer perceptron artificial neural network (MLP-ANN) and random forest regression (RFR) algorithms. Our results show that there are three pressure magnitude regimes: normal pressure zone (hydrostatic pressure), transition pressure zone (slightly above hydrostatic pressure), and over pressured zone (significantly above hydrostatic pressure). The top of the geopressured zone (2873 mbRT or 9425.853 ft) averagely marks the onset of overpressurization with the excess pore pressure above hydrostatic pressure (P∗) varying averagely along the three wells between 1.06−24.75 MPa. The results from the three methods are self-consistent with strong correlation between the Eaton's method and the two machine learning models. The models have high accuracy of about > 97%, low mean absolute percentage error (MAPE < 3%) and coefficient of determination (R2> 0.98). Our results have also shown that the principal generating mechanisms responsible for high pore pressure in the offshore Niger Delta are disequilibrium compaction, unloading (fluid expansion) and shale diagenesis.

Offset Well Design Optimization Using a Surrogate Model and Metaheuristic Algorithms: A Bakken Case Study

Fracture-driven interaction FDI (colloquially called “Frac-hit”) is the interference of fractures between two or more wells. This interference can have a significant impact on well production, depending on the unconventional play of interest (which can be positive or negative). In this work, the surrogate model was used along with metaheuristic optimization algorithms to optimize the completion design for a case study in the Bakken. A numerical model was built in a physics-based simulator that combines hydraulic fracturing, geomechanics, and reservoir numerical modeling as a continuous simulation. The stress was estimated using the anisotropic extended Eaton method. The fractures were calibrated using Microseismic Depletion Delineation (MDD) and microseismic events. The reservoir model was calibrated to 10 years of production data and bottom hole pressure by adjusting relative permeability curves. The stress changes due to depletion were calibrated using recorded pressure data from MDD and FDI. Once the model was calibrated, sensitivity analysis was run on the injected volumes, the number of clusters, the spacing between clusters, and the spacing between wells using Sobol and Latin Hypercube sampling. The results were used to build a surrogate model using an artificial neural network. The coefficient of correlation was in the order of 0.96 for both training and testing. The surrogate model was used to construct a net present value model for the whole system, which was then optimized using the Grey Wolf algorithm and the Particle Swarm Optimization algorithm, and the optimum design was reported. The optimum design is a combination of wider well spacing (1320 ft), tighter cluster spacing (22 ft), high injection volume (1950 STB/cluster), and a low cluster number per stage (seven clusters). This study suggests an optimum design for a horizontal well in the Bakken drilled next to a well that has been producing for ten years. The design can be deployed in new wells that are drilled next to depleted wells to optimize the system’s oil production.

Estimation Pore and Fracture Pressure Based on Log Data; Case Study: Mishrif Formation/Buzurgan Oilfield at Iraq

Prediction of the formation of pore and fracture pressure before constructing a drilling wells program are a crucial since it helps to prevent several drilling operations issues including lost circulation, kick, pipe sticking, blowout, and other issues. IP (Interactive Petrophysics) software is used to calculate and measure pore and fracture pressure. Eaton method, Matthews and Kelly, Modified Eaton, and Barker and Wood equations are used to calculate fracture pressure, whereas only Eaton method is used to measure pore pressure. These approaches are based on log data obtained from six wells, three from the north dome; BUCN-52, BUCN-51, BUCN-43 and the other from the south dome; BUCS-49, BUCS-48, BUCS-47. Along with the overburden pressure gradient and clay volume, which were also established first, data such as gamma ray, density, resistivity, and sonic log data are also required. A key consideration in the design of certain wells is the forecasting of fracture pressure for wells drilled in the southern Iraqi oilfield of Buzurgan. The pressure abnormality is found in MA, MB21, MC1 and MC2 units by depending on pore pressures calculated from resistivity log. In these units, depths and its equivalent normal and abnormal pressure are detected for all sex selected wells; BUCS-47, BUCS-48, BUCS-49, BUCN-43, BUCN-51 and BBCN-52. For MA, MB21, MC1, and MC2 units, the highest difference in pore pressure values are 1698 psi @ 3750 m (BUCN-51), 3420 psi @ 3900 m (BUCN-51), 788 psi @ 3980 m (BUCS-49), and 5705 psi @ 4020 m (BUCN-52). On other hands, MB11 and MB12 units have normal pressure trend in all studied wells. Finally, the results show that the highest pore and fracture pressure values is existed in North dome, in comparison with that obtained in south dome of Mishrif reservoir at Buzurgan oilfield.

Pore Pressure Estimation for Shaly Sand Formations Using Rock Physics Models For Compressional and Shear Velocities: A 1D Geomechanical Approach

Pore pressure estimation in sedimentary basins has been made exclusively through the compressional velocity data since the 1960s, using the normal compaction trend and lithostatic pressure profile derived from wireline logs. Considering that seismic velocity is highly dependent on petrophysical parameters such as porosity and clay volume, pore pressure estimation is commonly associated with a high degree of uncertainty due to simplistic assumptions that neglect those dependencies. To improve that, we propose two empirical velocity models based on compressional and shear rock physics relations for pore pressure prediction in shaly sand formations. These formulations extend Bowers and Doyen formulae, linking compressional and shear velocities with effective stress and petrophysical parameters. Finally, we used a non-linear multidimensional inversion approach to calibrate the proposed models and apply them in the context of a 1D geomechanics and pore pressure prediction study of an upper cretaceous overpressured shaly sand oil reservoir. The results show good agreement with pore pressure data and pressure predictions from the traditional Eaton method. The advantage of the proposed approach is its consistency throughout the entire well-log petrophysical interpretation workflow, especially concerning porosity, clay volume and saturation.

Pre-drill pore pressure prediction using seismic interval velocity and wireline log: Case studies for some wells in Cuu Long and Song Hong basins

Pore pressure can be obtained from seismic interval velocity by the velocity to pore pressure transform technique. In this paper, the authors present the Eaton experimental method to calculate pore pressure for some wellbores in the Cuu Long and Song Hong basins, where complex processes of subsidence, burial, geological transformation, and geothermal activity took place, causing abnormal pressure zones.The obtained results show that the pore pressures calculated from the seismic interval velocity data are closely correlated with the values measured by well logging methods and the density of the drilling fluids. Therefore, using seismic interval velocities to calculate pore pressure values, identify and predict abnormal zones by the Eaton method can be effectively applied in frontier areas to improve safety, reduce risks while drilling.

Prediction of Shale Gas Pressure based on Multi‐channel Seismic Inversion in Fuling

AbstractThe accurate prediction of formation pressure is important in oil/gas exploration and development. However, the achievement of this goal remains challenging, due to insufficient logging data and the low predictive data accuracy from seismic data. In this work, a case study was carried out in the Baima area of Wulong, in order to develop a workflow for accurately predicting shale gas formation pressure. The multi‐channel stack method was first used, as well as the inversion of single‐channel seismic data, to construct velocity and density models of the formation. Combined with the existing well‐logging data, the velocity and density models of the whole well section were established. The shale gas formation pressure was then estimated using the Eaton method. The results show that the multi‐channel seismic stacking method has a higher accuracy than the inversion of the formation velocity obtained by the single‐channel seismic method. The discrepancies between our predicted formation pressure and the actual formation pressure measurement are within an acceptable range, indicating that our workflow is effective.

Developing a new approach for the anticipation of subsurface pressure in three oil wells from various fields in Iraq

Subsurface pressure quantification is the main goal for all drilling engineers due to its vital importance to minimizing drilling costs and preventing various excavating dilemmas such as stuck pipe, pipe collapsing, lost circulation, and well kick. Formation pressure is measured directly in producing hydrocarbon zones using special equipment; however, it is difficult to obtain pore pressure measurements in other intervals; therefore, many approaches were suggested and developed to anticipate the geopressure due to its importance on the drilling operation. Traditionally, Eaton's method is the most widely used for geopressure anticipation from well logs. In the current study, a new proposed formula for geopressure determination was derived from the modified Eaton's equation which is based on the inception of the mechanical specific energy. The study was applied on three oil-producing wells from different fields in Iraq (Missan C, West Qurna 15, and Zubair 171). The estimated subsurface pressure from the new suggested approach was validated by comparison with the actual in situ subsurface pressure obtained from Drill Stem Test (DST) and Repeated Formation Test (RFT). Statistical analysis is used for the validation by applying Mean squared error (MSE) and Mean Absolute percentage Error (MAE). Encouraging results were obtained from the present research for all wells being studied. The main finding of the current work is involving a new parameter (mechanical specific energy) for subsurface pressure determination, where it was not included in the previous techniques of geopressure equations. It was found that the value of exponent (m) in the new proposed equation has a significant influence on the predicted geopressure, where (m) varies from 0.1626 to 0.6896 depending on the type of the excavated rock plus the materials that the drill bit is manufactured of, where the interaction between these components is vital. The present work could be a useful method when planning to excavate a new well adjacent to the area of the investigated wells, where special well logs are unavailable for pore pressure measurement and suitable usage of mud weight is highly needed for the overall drilling process.

Overpressure’s top and generating mechanism in “APR” block, North Sumatra offshore area

The offshore North Sumatra Basin is one of the hydrocarbon-prone basins in Indonesia. The Malacca Formation acts as the reservoir in this basin. However, there are some drilling problems caused by abnormal pore pressures. Overpressure analysis is carried out so that drilling activities are avoided from drilling problems such as kicks, blowouts, stuck pipes, loss circulation, and collapse. This study aims to analyze the overpressure in the “APR” block, offshore North Sumatra Basin, which includes determine the depth of the top overpressure and the mechanism of overpressure formation. In the research area, analysis of 3 exploration wells was carried out. Overpressure is analyzed based on well log data, and pressure test data using the Eaton method (1975) supported by other data such as LOT (Leak-Off Test) data, drilling reports, mudlogs. At Well A, top overpressure depth is ± 3235 ft TVDSS (true vertical depth sub sea). At Well P, top overpressure depth is ± 3065 ft TVDSS, and at Well R, top overpressure depth is ± 2901 ft TVDML (true vertical depth mud line). The overpressure generating mechanism in the study area is caused by the loading mechanism (disequilibrium compaction) and caused by the nonloading mechanism (hydrocarbon buoyancy).

Overpressure-generating mechanisms in the Blok F3, North Sea, Netherland

Block F3 North Sea is a block with pore pressure values that vary over time due to complex geological conditions such as burial and various sedimentation zones. Pore pressure is one of the important aspects that need to be analyzed as a basis for the identification of zones and overpressure mechanisms. Overpressure is a greater pore pressure condition than normal pressure and may cause drilling problems, such as kicks, blowouts, etc. This study calculated pore pressure values using the eaton method approach with well data and seismic data. Both data are integrated for generating pore pressure values in 1D and 3D. 1D Modelling uses Interactive Petrophysics 3.5, while 3D modeling uses Petrel software. In 3D modeling, the variables used are interval velocity and inversion velocity obtained by acoustic impedance inversion. The sub-variables used are the inversion density and the regression density obtained from well density acoustic impedance inversion. The existence of a 1D overpressure zone at a depth of 1,100 – 1,800 m with an overpressure value of 3,836 – 18,975 kPa. In addition, the overpressure value based on the 3D model is 8,000 – 18,000 kPa. The overpressure zone is validated using an acoustic impedance inversion model with a high value of 5,200 – 5,380 (m/s)*(gr/cc). Overpressure in Block F3 is predicted to occur from disequilibrium compaction..