Pore Pressure Prediction Research Articles

Statistical modeling for real-time pore pressure prediction from predrill analysis and well logs

The challenge of pore pressure prediction in an overpressured area near a well is studied. Predrill understanding of pore pressure is available from a 3D geologic model for pressure buildup and release using a basin modeling approach. The pore pressure distribution is updated when well logs are gathered while drilling. Sequential Bayesian methods are used to conduct real-time pore pressure prediction, meaning that every time new well logs are available, the pore pressure distribution is automatically updated ahead of the bit and in every spatial direction (north, east, and depth), with associated uncertainty quantification. Spatial modeling of pore pressure variables means that the data at one well depth location will also be informative of the pore pressure variables at other depths and lateral locations. A workflow is exemplified using real data. The prior model is based on a Gaussian process fitted from geologic modeling of this field, whereas the likelihood model of well-log data is assessed from data in an exploration well in the same area. Results are presented by replaying a drilling situation in this context.

RETRACTED ARTICLE: Predictability and controlling factors of overpressure in the North Alpine Foreland Basin, SE Germany: an interdisciplinary post-drill analysis of the Geretsried GEN-1 deep geothermal well

For the first time, drilling- and velocity-based well analysis and 3D basin modeling were combined to test the predictability and controlling factors of overpressure in the North Alpine Foreland Basin in SE Germany. More specifically, the techniques were tested in the sub-regional context of the deep geothermal well Geretsried GEN-1 (TVD = 4852 m), located in the south of Munich. A 3D basin model based on a total of 20 wells was calibrated to the pressure distribution of four petroleum wells and tested against the Geretsried GEN-1 well. The results demonstrate that overpressure in the North Alpine Foreland Basin SE Germany can be predicted from a simple 3D basin model calibrated to a minimum number of wells. Thereby, disequilibrium compaction likely acts as the main overpressure mechanism in the study area, underpinned by significantly higher sedimentation rates at overpressured locations. 3D basin modeling also confirms the role of Upper Cretaceous shales, which, if present, serve as an important pressure barrier between the under- to normally pressured Jurassic and overpressured Cenozoic basin fill. In addition, overpressure magnitudes of the Chattian might be higher than previously expected. The results of this study have great impact on future drilling campaigns in the North Alpine Foreland Basin in SE Germany. Minimized non-productive time and drilling cost, improved well planning and increased safety are amongst the most important benefits of accurate pore pressure and overpressure prediction. The newly derived insights on the mechanisms of overpressure will greatly influence future geomechanical and tectonic studies, since pore pressure drives rock strength and principle stress magnitudes. Finally, the study is a great example for the importance of an interdisciplinary approach and the incorporation of geological conditions, when investigating drilling-related problems.

Enhanced velocity-based pore-pressure prediction using lithofacies clustering: a case study from a reservoir with complex lithology in Dezful Embayment, SW Iran

The primary goal of this paper is to improve accuracy and reliability of the conventional Bowers and Tau methods in a reservoir with complex lithology. We demonstrate the capability of the proposed method through a case study in a reservoir in the Southwest of Iran. Velocity based pore pressure prediction methods are widely accepted as a routine technique in the petroleum industry. Despite recent improvements, still, literature suffer from inconsistencies and uncertainties mostly arise from velocity anomalies due to complex lithostratigraphic setting or presence of various formation fluids. Our proposed workflow aims to address those issues and improve the accuracy of the estimations by clustering the input data into zones with specific geomechanical characteristics. We hypothesis each major zones at the offset test wells might have distinct Normal Compaction Trend with a different empirical constants. Thus, Bowers and Tau methods should be calibrated for each cluster rather the whole stratigraphic column. The clustering task was done by statistical analyses of a suite of well logs and validated with core derived lithologies. Several clustering techniques namely K-means, basic sequential algorithmic scheme, single, and complete linkage hierarchical were applied and compared to find the best algorithm. We found that the self-organizing map (SOM) method provide the best results by maximizing lithology likelihood within each cluster and improve the efficiency of the Bowers and Tau methods. Satisfactory results of this study offer a safe ground for implementation of the proposed method in other sedimentary basins.

Prediction of Formation Pressure Gradients of NC98 Field-Sirte Basin-Libya

The prediction of formation pore pressure and fracture pressure gradients is a significant step towards the drilling plan. In this study, the formation pressures of twelve wells from NC98 field-Sirte Basin (Waha Oil Company) were calculated by employing empirical methods, Eaton's equations, that depend on the real drilling and well-logging data. Regarding the results, the normal pore pressure in the NC98 field in Sirte basin is 9.89 kPa/m (0.437 Psi/ft), and it is extending from the top of the wells in the investigated area to 2134 m (7,000 ft). A subnormal to normal pore pressure zone is noticed in the interval of 2134 m to 2743 m (7,000 ft. - 9,000 ft). Then, slightly subnormal to somewhat abnormal (overpressure) region is seen from 2743 – 3414 (9,000 ft. - 11,200 ft). Beyond to that depth and down to the top of the reservoir, the overpressure zone was clearly observed. Based on the results, the casing seating depth and the equivalent mud weight were simply determined for the area of study.

Variation of vertical stress in the onshore Canning Basin, Western Australia

Vertical stress is one of the three principal stresses and is an important parameter in geomechanical studies that are focussed on the prediction of pore pressure, fracture gradients and wellbore stability. Variations of the vertical stress magnitude can be attributed to variations in lithology or diagenetic history, localised uplift and overpressures caused by disequilibrium compaction. This study uses wellbore data from 102 open-file petroleum wells to characterise vertical stress within the onshore Canning Basin of north-western Australia. Vertical stress magnitudes are interpreted from density logs and checkshot data and at 1 km depth below the ground surface range from 20.5 to 25.0 MPa km–1 with a mean value of 22.1 MPa km–1 (s.d. = 1.0 MPa km–1). Significant variation is evident within the calculated stress magnitudes, and when presented spatially, three regions of elevated vertical stress are identified: the Barbwire Terrace, the Devonian reef complexes of the northern Lennard Shelf and the Mowla Terrace. Lithology, abnormal pore pressures and tectonic uplift are investigated as potential mechanisms of the observed variation. Although abnormal pore pressures are identified, no direct correlation between overpressured areas and elevated vertical stress magnitudes is observed. The Canning Basin has an extensive history of uplift; however, there is little evidence for significant recent inversion. While uplift is likely to exert some influence over vertical stress magnitudes in the Canning Basin, the primary cause is interpreted to be lithological: areas of elevated vertical stress magnitude are also areas where thick intervals of carbonate sediments are present.

An integrated deep learning solution for petrophysics, pore pressure, and geomechanics property prediction

In unconventional plays, wells are drilled at an unprecedented rate. This, together with technical challenges in terms of complex stratigraphy, multiple play types, variable rock properties, and various elements of pore pressure, geomechanics, fracturing, and diagenesis, calls for more sophisticated, faster, consistent, and wider ranging analytical tools. Given the scale of the work — i.e., the number of wells — performing classical workflows for petrophysics, pore pressure, and geomechanics prediction can be impractical (if not impossible) due to turnaround considerations. Also such workflows might not use any preexisting regional studies efficiently. In principle, a machine learning approach can mitigate these shortcomings. We show that a supervised deep neural network approach can be an alternative innovative tool for petrophysical, pore pressure, and geomechanics analysis enabling the use of all the previously interpreted data to devise solutions that simultaneously integrate wide-ranging wellbore and wireline logs. Beyond that, a similar approach is taken to predict a certain number of attributes solely from seismically derived properties, which allows one to compute volumetric models. The application of such an algorithm is shown on a Permian case study in which the automatic neural-network-based algorithms achieve reasonable accuracy in a fraction of the time.

One-dimensional nonlinear consolidation behavior of structured soft clay under time-dependent loading

This research investigated the nonlinear compressibility, permeability, the yielding due to structural degradation and their effects on consolidation behavior of structured soft soils. Based on oedometer and hydraulic conductivity test results of natural and reconstituted soft clays, linear log (1+e) ~ log sigma\'v and log (1+e) ~ log kv relationships were developed to capture the variations in compressibility and permeability, and the yield stress ratio (YSR) was introduced to characterize the soil structure of natural soft clay. Semi-analytical solutions for one-dimensional consolidation of soft clay under time-dependent loading incorporating the effects of soil nonlinearity and soil structure were proposed. The semi-analytical solutions were verified against field measurements of a well-documented test embankment and they can give better accuracy in prediction of excess pore pressure compared to the predictions using the existing analytical solutions. Additionally, parametric studies were conducted to analyze the effects of YSR, compression index (lambda_r and lambda_c), and permeability index (eta_k) on the consolidation behavior of structured soft clays. The magnitude of the difference between degree of consolidation based on excess pore pressure (Up) and that based on strain (Us) depends on YSR. The parameter lamda_c/eta_k plays a significant role in predicting consolidation behavior.

Drilling Optimization of Tight Sands and Shale Gas Reservoir in Jambi Sub-Basin Based on Pore Pressure Estimation Using Drilling Efficiency Mechanical Specific Energy (DEMSE) and Bowers Methods

The subsurface pressure analysis is used to detect the overpressure and problems in the well that will be drilled based on exploration well data. Various problems were found while drilling operations carried out on A and B wells, namely, Kick and Pipe sticking which cause a high Non-Productive Time (NPT). This research is conducted to identify the mechanism of overpressure formation in Tight Sand Gas and Shale Gas in the Jambi Sub-Basin. Furthermore, to predict pore pressure using the Drilling Efficiency and Mechanical Specific Energy (DEMSE) and Bowers method. The final result will be a 3D pore pressure cube in the area based on quantitative analysis of post-stack seismic inversion. The results of the pore pressure analysis from the wells and the 3D pore pressure model indicate that top of overpressure occurs in the Gumai Formation, then it is decreasing gradually approaching the hydrostatic pressure on the Basement. The mechanisms of overpressure are caused by under compaction, fluid expansion (kerogen maturation). The Gumai Formation and Talang Akar Formation are shale rocks so the type of mud weight that is well used is oil based mud (OBM).

Short term memory efficient pore pressure prediction via Bayesian neural networks at Bering Sea slope of IODP expedition 323

Pore pressure (PP) study can provide insightful information about evolution history and/or geological process taking place over a region. Conventional methods, mostly are of deterministic, and they do not allow considering underlying variability and uncertainty. Here we implement Bayesian neural networks (BNN) optimized by Scaled Conjugate Gradient (SCG) and Hybrid Monte Carlo (HMC) approach to model the PP and estimate the uncertainty in prediction from well log data of well U1343E located at Bering sea slope region of the IODP Expedition 323. In the first step, to create representative samples of well log and corresponding PP samples, Eaton’s and porosity methods are employed to estimate PP empirically from well log data (e.g., gamma ray, sonic velocity, bulk density and sonic derived porosity). In the second step, in total 357 representative samples are used to build a statistical model in Bayesian framework to model the PP against depth. Prior to actual data analysis, we conducted a series of experiments combining with auto-correlation function (ACF) and/or partial autocorrelation function (PACF) analysis to fix network structure (e.g., input lag, number of hidden node) and the bounds of network hyper-parameter. In contrast to previous approach, we seek to develop a mechanism which allows to explore the link between past PP and/or well log history and present PP under rapid sedimentation rate and changing environment. The model exhibits excellent performance between predicted and computed PP with Pearson's correlation coefficient, reduction of error (RE) (RESCG-BNN ∼ 0.99; REHMC-BNN ∼ 0.99), and index of agreement (IA) (IASCG-BNN ∼ 0.99; IAHMC-BNN ∼ 0.98). Comparison based on coefficient of determination, R2, it is obtained that BNN produced superior results than the conventional artificial neural networks (ANNs). Moreover, at 530 mbsf (meter below sea floor), abrupt PP change could be linked to the transition from Pliocene to Pleistocene. The approach used here, could be useful to identify overpressure zones (OPZ) and to understand the role of past PP/well log to present PP history in many other complex geo-environmental applications.

Energy-based formation pressure prediction

Conventionally, pore pressure predictions from the drilling parameters have the advantage of estimating the formation pressure at the bit at relatively low cost. The limitations on the application of d-exponent concept to pore pressure prediction have long been established. Recent development in pore pressure prediction from the drilling parameters uses the concept of mechanical specific energy (MSE) and hydro-rotary specific energy (HRSE). These energies are usually computed from the downhole measurements. However, majority of readily available field data in older (offset) and present-day wells are in the form of surface measurements. In this paper, a new pore pressure prediction technique based on the concept of hydro-mechanical specific energy (HMSE) is being proposed. The HMSE is the combination of axial, rotary and hydraulic energies required to break and remove a unit volume of rock. The new technique uses drilling parameters that are obtained only from surface measurements. Pore pressure prediction using the concept of HMSE is based on the theory that total energy consumed in breaking and removing a unit volume of rock beneath the bit is a function of effective stress: the higher the effective stress, the greater the total energy required to break and remove a unit volume of rocks. Abnormally high formation pressure intervals with lower effective stress will require less energy to drill than the normally compacted series at the same depth. The new technique is test using a recently drilled near vertical deep High-Pressure High-Temperature (HPHT) exploratory well in the Tertiary Deltaic System of the Niger Delta basin where the main cause of overpressure mechanism is under-compaction. The well drilled to a total depth of more than 17,000 ft-TVD covers both the normally pressured and overpressure intervals. Pore pressure estimates derived from the HMSE concept are then compared to the actual pore pressure measurements taken from the formations of interest. There is an excellent agreement between the predicted and measured formation pore pressure. The new technique can provide a reliable means of estimating the formation pore pressure from the drilling parameters in absence of reliable downhole measurements at relatively low cost.

PORE PRESSURE ESTIMATION IN HARD UNLOADING-OVERPRESSURE ZONE USING SINGLE COMPACTION EQUATION, CASE STUDY: LOWER KUTAI BASIN

Pore pressure estimation in hard-overpressure zone is very important in drilling activities. Correct and precise estimation are able to reduce the cost for drilling due to reduced non-productive time (NPT) and in increase in safety aspect while drilling. Pore pressure estimation is done by post-mortem analysis in exploration wells using validation points from pressure measurement data such as Drill Stem Testing (DST), Modular Dynamic Tester (MDT), and Repeat Formation Tester (RFT). A wireline logs data used for identification of the type of overpressure and predicts pore pressure magnitude in the shale section. Through the correlation between effective-stress and velocity, a single compaction equation was obtained for the Lower Kutai Basin with the value of : 5.097 * ? 0.854. By using this single equation, pre-drill pore pressure prediction activity will be more easy and could estimate pore pressure in hard-overpressure zone precisesly.

New Model for Pore Pressure Prediction While Drilling Using Artificial Neural Networks

Pore pressure is one of the main formation conditions that affects the efficiency of drilling operations and impacts its cost. Accurate prediction of the pore pressure and the parameters controlling it will help reduce the drilling cost and avoid in some cases catastrophic accidents. Many empirical models reported in the literature were used to predict the pore pressure based on either drilling parameters or log data. Empirical models require trends such as normal or abnormal pressure to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the pore pressure using one or maximum two AI methods (which are black box). There is no developed empirical correlation for pore pressure prediction based on optimized AI techniques. The objective of this paper is to predict the pore pressure based on both drilling parameters and log data, namely weight on bit (WOB), rotary speed (RPM), rate of penetration (ROP), mud weight (MW), bulk density (RHOB), porosity ( $$\phi $$ ), and compressional time ( $$\Delta {t}$$ ). Real field data will be used to predict the pore pressure using artificial neural network (ANN). Finally, for the first time, a new empirical correlation for pore pressure prediction will be developed based on the optimized ANN model. The obtained results showed that it is very important to combine the drilling parameters and the logging data to predict the pore pressure with a high accuracy. A new empirical correlation was developed using the optimized ANN method that can estimate pore pressure with high accuracy (correlation coefficient of 0.998 and average absolute percentage error of 0.17%). Unlike the published empirical models, the new model requires no prior pressure trends (such as normal or abnormal pressures) to perform prediction.

THE IMPORTANCE OF GEOLOGICAL AND HYDROGEOLOGICAL KNOWLEDGE IN JUSTIFYING PORE PRESSURE PREDICTION: THE CASE STUDY OF THE PECIKO FIELD, LOWER KUTAI BASIN

One significant factor affecting pore pressure prediction is choosing a method of calculation. If we choose the inappropriate method, the result may not refl ect not only pore pressure condition in an area, but also geological processes operating in the whole basin. In this research, two methods are applied to wireline-based pore pressure calculation in the Peciko Field: the Eaton Method and the Soil Mechanics Method. The results of the calculation show a signifi cant difference between these methods. The Eaton Method resulted in reservoir-shale pressure discrepancy (by over-predicting the reservoir pressure) at shallower depth, and reservoir-shale pressure equilibrium at depth. On the contrary, the Soil Mechanics Method resulted in reservoir-shale pressure equilibrium at shallower depth and an under-predicting at depth. It seems that, in terms of processes operating in this fi eld which affect pore pressure regimes, i.e. lateral reservoir drainage and rapid mud-dominated deltaic sedimentation, the result of the Eaton Method is more plausible than that of the Soil Mechanics Method. This research also reveals an important inference: if there is a pressure discrepancy, then it is likely that a hydrodynamic condition is present.

Construction of a 3D Geomechanical Model for Development of a Shale Gas Reservoir in the Sichuan Basin

SummaryCurrently, there is large-scale shale gas exploration and development in the Sichuan Basin, western China. Caused by high tectonic stress and presence of fracture systems at various scales in the lower Silurian Longmaxi reservoir formation, hydraulic fracturing in shale gas reservoirs in the Sichuan Basin has encountered many difficulties, such as placing sufficient proppant, poor-production performance for some wells, and ambiguity as to the factors controlling the production of reservoirs. It has been recognized that lack of geomechanical understanding of the shale gas reservoirs is a major obstacle to effectively addressing these difficulties.A 3D full-field geomechanics model was constructed for the Changning shale gas reservoir in the Sichuan Basin through integrating seismic, geological structure, log, and core data by following a newly formulated work flow. The 3D geomechanical model includes 3D anisotropic mechanical properties, 3D pore pressure, and the 3D in-situ stress field. Through leveraging measurements from an advanced sonic tool and core data, the anisotropy of the formation was captured at wellbores and propagated to 3D space guided by prestack seismic inversion data. The 3D pore-pressure prediction was conducted with seismic data, and calibrated against pressure measurements, mud-logging data, and flowback data. A discrete-fracture-network (DFN) model, which represents multiscale natural-fracture systems, was integrated into the 3D geomechanical model during stress modeling to reflect the disturbance on the in-situ stress field by the presence of the natural-fracture systems.The 3D pore-pressure model was used to calculate more-reliable estimates of gas in place in the shale gas reservoir, and the geomechanical model was used to reveal the root cause of difficulties of proppant placement in this tectonically active and unevenly fractured shale gas reservoir.The paper presents the highlights and innovations in constructing the 3D geomechanical model for the shale gas reservoir, and explains how the 3D geomechanical model is used to address technical challenges encountered during drilling and completion. Also, it demonstrates that a reliable 3D geomechanical model, with proper characterization of anisotropy, pore pressure, and natural fractures, provides a critical opportunity to improve the development in this shale gas reservoir.

A 2D geomechanical model of an offshore gas field in the Bredasdorp Basin, South Africa

This case study involves the unique application of density correction software applied to density data, prior to the estimation of geopressure gradients. The K-R gas field was discovered in 1983 about 50 km west, off the F-A gas field offshore South Africa. During exploration; gas discoveries were made in well KR-1 and KR-8, potentially commercial gas and encouraging oil flow rates in well KR-2, KR-4 and KR-6, a dry well for KR-5 and a dry well with encouraging oil shows in KR-3. The aim of this study is to create a model that evaluates the geomechanical behaviour of the upper shallow marine reservoir (USM) of the Bredasdorp Basin, South Africa and provide a safe drilling mud window for future work in the area. The K-R field has a strong NW–SE fault trend, resulting in a maximum horizontal stress orientation of 125°, determined from structural depth maps. All geopressure gradients were modelled using the drillworks@ software at the top (TUSM) and bottom (BUSM) of the reservoir. The Eaton method, that can predict pore pressure from either velocity or resistivity, was used to calculate both pore pressure and fracture gradient and then calibrated using “real” data from well completion and driller’s reports. The pore pressure and fracture gradient are what set the upper and lower mud weight limits. These values range between 8.46 and 9.60 ppg and 10.12–15.33 ppg, respectively. The rock mechanical properties (Friction angle, cohesive strength and uniaxial compressive strength) were empirically derived and show a similar trend for all wells. The drilling mud window becomes more constricted at depths below 2600 m, to the TD of the well.

Pore pressure prediction methods using normal compaction trend based on seismic inversion

Pore pressure prediction plays an important role in shale gas exploration and fracking technology. The pore pressure in shale cannot be directly measured but to be inferred by the normal velocity trend, so methods based on the effective stress theory are dedicated to establishing a function between seismic interval velocity and pressure. Among them the mostly used method is Eaton’s equation. However, how to precisely quantify the state of compaction remains unsolved. In this study, the AVO/AVA simultaneous inversion was introduced to estimate P-velocity. According to the exponential relationship between the pore pressure and the ratio of velocities, three different methods including the fitting method, the direct calculation method and the model-based direct calculation method based on the Eaton’s equation were used to estimate shale gas pore pressure, respectively. And then a comparative analysis was performed to see the impact of normal compaction trend on the result. It was found that the horizontal continuity of the model-based direct calculation method was the best. The result shows that the approach of estimating the normal compaction trend impacts the pore pressure significantly.

Wave (Current)-Induced Pore Pressure in Offshore Deposits: A Coupled Finite Element Model

The interaction between wave and offshore deposits is of great importance for the foundation design of marine installations. However, most previous investigations have been limited to connecting separated wave and seabed sub-models with an individual interface program that transfers loads from the wave model to the seabed model. This research presents a two-dimensional coupled approach to study both wave and seabed processes simultaneously in the same FEM (finite element method) program (COMSOL Multiphysics). In the present model, the progressive wave is generated using a momentum source maker combined with a steady current, while the seabed response is applied with the poro-elastoplastic theory. The information between the flow domain and soil deposits is strongly shared, leading to a comprehensive investigation of wave-seabed interaction. Several cases have been simulated to test the wave generation capability and to validate the soil model. The numerical results present fairly good predictions of wave generation and pore pressure within the seabed, indicating that the present coupled model is a sufficient numerical tool for estimation of wave-induced pore pressure.

Overburden stress and pore pressure prediction for the North Rumaila oilfield, Iraq

The aim of this study is to give an estimation of the pore pressure in all formations starting from surface to the main reservoir (Zubair Formation) in North Rumaila oilfield. Pore pressure estimation is fundamental in minimizing the nonproductive time (NPT) caused by wellbore instability or mud losses. Two methods were used to predict pore pressure: first, direct measurements of by RFT tool where almost all measurements indicated low pore pressure within reservoirs intervals (Zubair, Nahr Umr and Mishrif Formations) as a result of hydrocarbon production. Second, the indirect prediction, this technique relied on the disparity of the petrophysical characteristics of normally and abnormally compacted formations determined by the compressional transit time data (∆t). This method showed that most of the formations in Rumaila field are equal or less than the normal compaction trend line (NCTL) except the Shale layers in Tanuma, Ahmadi and Zubair Formations as these layers exhibit low permeability and subjected to under-compaction which occurs in rapidly subsiding basins.

Is CO2 injection at Aquistore aseismic? A combined seismological and geomechanical study of early injection operations

Fluid injection is known to induce seismic events if the injection causes fracturing of the surrounding rock or if resulting pressure changes reactivate pre-existing faults and fractures. Carbon dioxide (CO2) storage projects where CO2 is injected into deep geological formations for permanent containment are one sector where induced seismicity has been observed. The Aquistore storage project in Saskatchewan, Canada began CO2 injection into the basal Cambrian sandstone at ∼3.2 km deep in April 2015 and the site has been extensively monitored for seismicity. Passive seismic monitoring instrumentation includes a small network of broadband seismometers, a continuously recording array of near-surface geophones and temporary deployments of downhole geophones at depths from 2950 m to 3010 m in an observation well. To date no injection-related induced seismicity has been observed. The seismic arrays are functioning as expected and local mine blasts, orientation shots and perforation shots have been detected using standard detection algorithms. Data stacking algorithms have also been tested on short-periods of data. Using synthetic data added to noise models, the estimated minimum detectable event local magnitude is −0.8 for the broadband stations and between −1.6 and −0.6 for the near-surface geophones. Thus far, small volumes of CO2 have been injected at Aquistore (∼140 kt) and injection has generally occurred below the fracture pressure. As a result, predicted pore pressure changes are small and periods without injection have allowed relaxation of the pressure plume. Geomechanical modelling suggests insignificant effective stress changes at an identified fault near the Aquistore injection well. It is therefore not surprising that no induced seismicity has been detected. With further injection, continued seismic monitoring is essential to provide warning of any fault reactivation and thus any potential increase in seismic risk or CO2 leakage risk.

Overpressure prediction using the hydro-rotary specific energy concept

Pore pressure predictions from the drilling parameters have experienced little improvement since the inception of the d-exponent concept. Applications of the d-exponent method to pore pressure predictions have produced mixed results, especially in deviated wells and under drilling conditions where bit hydraulic energy has significant influence on the rate of penetration (ROP). In this paper, a new energy-based pore pressure prediction technique using the concept of hydro-rotary specific energy (HRSE) is presented. The HRSE approximates the total energy required to break and remove a unit volume of rock. Overpressure prediction using the HRSE method is based on the principle that overpressure intervals with lower effective stress will require less energy to drill than the normally pressured intervals at the same depth. The new technique is tested using a recently drilled deep vertical exploratory gas well in the Tertiary Deltaic System in the central swamp region of the Niger Delta in Nigeria. The pore pressure estimates from the HRSE concept are compared to: (1) the pore pressure estimates derived from the d-exponent and shale compressional velocity, (2) the actual pore pressure measurements taken in the reservoir sands of interest. An excellent agreement is observed in magnitude and trend between the pore pressure estimates derived from the HRSE concept and the actual pore pressure measurements. This clearly demonstrates the applicability of the HRSE concept in predicting the onset of overpressure and estimating the formation pore pressure. The HRSE method of overpressure prediction has the potential to be more accurate in some drilling environments where the d-exponent method may have produced erroneous results.