Open hole/cased hole log analysis

Abstract Unconventional reservoirs like shale oil/gas are expected to play a major role in many unexplored regions, globally. Shale resource evaluation involves the estimation of Total Organic Carbon (TOC) which correlates to the prospective capability of generating and containing hydrocarbons. Direct measurement of TOC through geochemical analysis is often not feasible, and hence researchers have focused on indirect methods to estimate TOC using analytical and statistical techniques. Accordingly, this work proposes the application of artificial intelligence (AI) techniques to leverage routinely available well logs for the prediction of TOC. Multiple algorithms are developed and compared to rank the most optimum solution based on efficiency analysis. Support Vector Regression (SVR), Random Forest (RF), and XGBoost algorithms are utilized to analyze the well-log data and develop intelligent models for shale TOC. A process-based approach is followed starting with systematic data analysis, which includes the selection of the most relevant input parameters, data cleaning, filtering, and data-dressing, to ensure optimized inputs into the AI models. The data utilized in this work is from major shale basins in Asia and North America. The AI models are then used to develop TOC predictor as a function of fundamental open-hole logs including sonic, gamma-ray, resistivity, and density. Furthermore, to strengthen AI input-output correlation mapping, a k-fold cross-validation methodology integrating with the exhaustive-grid search approach is adopted. This ensures the optimized hyperparameters of the intelligent algorithms developed in this work are selected. Finally, developed models are compared to geochemically derived TOC using a comprehensive error analysis schema. The proposed models are teted for veracity by applying them on blind dataset. An error metrics schema composed of root-mean-squared-error, and coefficient of determination, is developed. This analysis ranks the respective AI models based on the highest performance efficiency and lowest prediction error. Consequently, it is concluded that the XGBoost and SVR-based TOC predictions are inaccurate yielding high deviations from the actual measured values in predictive mode. On the other hand, Random Forest TOC predictor optimized using k-fold validation produces high R values of more than 0.85 and reasonably low errors when compared to true values. The RF method overpowers other models by mapping complex non-linear interactions between TOC and various well logs.

doi: 10.2118/214383-MS

SPE EuropEC - Europe Energy Conference featured at the 84th EAGE Annual Conference & Exhibition

SPE-214383-MS

Country:

Asia (0.90)
North America > Canada (0.29)
Europe > Austria (0.28)

Geophysics: Geophysics (0.46)

Industry: Energy > Oil & Gas > Upstream (1.00)

Oilfield Places:

North America > United States > South Dakota > Williston Basin > Bakken Shale Formation (0.99)
North America > United States > North Dakota > Williston Basin > Bakken Shale Formation (0.99)
North America > United States > Montana > Williston Basin > Bakken Shale Formation (0.99)
(6 more...)

SPE Disciplines:

Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale oil (1.00)
Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)

Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Support Vector Machines (0.87)

Applying Machine Learning to Minimize Wireline Formation Testing Failures in Thin Laminated Sands

Fayzullin, Maksim (OMV) | Schmidt, Dennis (OMV) | Deckers, Marie (OMV)

Abstract This paper demonstrates how supervised machine learning (ML) aids planning and acquisition of wireline formation testing (WFT) in thin laminated sands. Available well data was used to train a set of algorithms to identify intervals where tests are likely to fail. The trained model aims to prevent WFT failures what in turn results in reduced rig downtime, increased efficiency of the logging contracts and improved reservoir characterization. Wireline formation testing is essential to acquire rock and fluid characteristics in multilayered reservoirs setting up the base for the upcoming decisions. This becomes significantly complicated in thin laminated sands with a thick hydrocarbon column, requiring hundred(s) of points to meet given objectives. The percentage of failed tests can be much higher than those of being successful. In this context an automated advisory system, based on the abundant historic WFT dataset, can mitigate personal biases of the subsurface team and boost the share of successful tests in future wells. A combined set of wireline logs served as features to predict WFT outcome in two classification approaches. Binary classification predicts likelihood of having a good test or a failure, whereas multi-classification further details failure types into 5 categories. The overall dataset comprised more than 500 points (testing attempts) within the concession to train various ML models, using variety of preprocessing and hyper parameters. Their accuracy and area under curve (AUC) were used as the ranking criteria. The performance mostly depends on the number of classes to be predicted, the number of input features and the number of data points available for training. Less classes to predict and more input features result generally in better model metrics. The final selected model attained a maximum accuracy of 0.75 in two exploration wells in the adjacent concessions, i.e. correctly predicting 75 outcomes out of 100 wireline formation tests. A log interpretation suite accesses the deployed model via a cloud endpoint for the upcoming infill wells. The approach could improve wireline formation testing in other reservoirs or regions prone to WFT failures, where accumulated data is sufficient for machine learning applications. This could result in tangible savings during well operations.

doi: 10.2118/214390-MS

SPE EuropEC - Europe Energy Conference featured at the 84th EAGE Annual Conference & Exhibition

SPE-214390-MS

Country: Europe > Austria (0.28)

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines:

Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Reservoir Description and Dynamics > Formation Evaluation & Management > Formation test analysis (e.g., wireline, LWD) (1.00)
Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)

Technology: Information Technology > Artificial Intelligence > Machine Learning > Performance Analysis > Accuracy (1.00)

Abstract This work describes a statistical rock-physics driven inversion of seismic acoustic impedance and Ultra Deep Azimuthal Resistivity (UDAR) log data, acquired while drilling, to estimate porosity, water saturation and litho-fluid facies classes around the wellbore. Despite their limited resolution, surface seismic data integrated with electromagnetic resistivity log measurements improve the description of rock properties by considering the coupled effect of pore space and fluid saturation in the joint acoustic and electrical domains. The key aspect of the proposed inversion is that it does not explicitly use a forward model, rather the correlation between the petrophysical properties and the resulting geophysical responses is inferred probabilistically from a training dataset. The training-set is generated combining available borehole information with statistical rock-physics modelling approach. In the inversion process, given co-located measurements of seismic acoustic impedance and logging-while-drilling electromagnetic resistivity data, the pointwise probability distribution of rock-properties is derived directly from the training dataset by applying the kernel density estimation algorithm. A non-parametric statistical approach is employed to approximate non-symmetric volumetric distributions of petrophysical properties and to consider the characteristic non-linear relationship linking water-saturation with resistivity. Given an a-priori facies classification template for the samples in the training-set, it is possible to model the multimodal, facies-dependent, behavior of the petrophysical properties, together with their distinctive correlation patterns. A facies-dependent parameterization allows the effect of lithology on acoustic and resistivity response to be implicitly considered, even though the target properties of inversion are only porosity and saturation. To provide a realistic uncertainty quantification of the estimated rock-properties, a plain Bayesian framework is described which accounts for rock-physics modelling error and to propagate seismic and resistivity data uncertainties to the inversion results. In this respect, the uncertainty related to the scale difference among the well-log data and seismic is addressed by adopting a scale reconciliation strategy based on probabilistic function. This allows transforming physically equivalent measures from one resolution to another and consistently estimate the corresponding changes in the probability distributions. The described rock physics-driven inversion can be performed efficiently during drilling, following the acquisition and inversion of UDAR data, as the time-consuming step of estimating a probabilistic model from the training-set, can be separated from inversion itself. This is of particular interest in geosteering, where the training-phase can be performed before drilling, during well planning operations. After training, the resulting probabilistic model can be stored as a look-up table. Hence, the prediction of rock-properties, given the co-located measurements of seismic acoustic impedance and log-while-drilling electromagnetic resistivity, reduces to a fast look-up table search. The inversion workflow is validated on a clastic oil-bearing reservoir located offshore Norway, where geosteering was used to guide the placement of a horizontal appraisal well in a complex structural setting. A complete set of well logs from four nearby exploration wells is used to construct the training dataset. Porosity, water-saturation, and litho-fluid facies are estimated along the geosteered well path given a 2D curtain section of ultra deep azimuthal resistivity and the corresponding acoustic impedance section available from the 3D surface seismic data. Prior to running the inversion, the acoustic impedance data was properly depth-matched with the resistivity section using a non-rigid matching algorithm. The joint inversion results show that the proposed methodology provides realistic estimates of the rock-property distributions around the wellbore to depths of investigation of 50m. These results constitute useful information to support geosteering decisions and can also be used, post-drilling, to update or optimize existing reservoir models.

doi: 10.2118/214407-MS

SPE EuropEC - Europe Energy Conference featured at the 84th EAGE Annual Conference & Exhibition

SPE-214407-MS

Country:

North America > United States (0.46)
Europe > Norway (0.34)
Europe > Austria (0.28)

Genre:

Research Report > New Finding (0.34)
Research Report > Experimental Study (0.34)

Geophysics: Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines:

Well Drilling > Drilling Measurement, Data Acquisition and Automation > Logging while drilling (1.00)
Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
Reservoir Description and Dynamics > Reservoir Characterization > Seismic modeling (1.00)
(2 more...)

Technology:

Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.88)
Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.66)

Andersen, Pål Østebø (Department of Energy Resources, University of Stavanger, Norway)

Intercept Method for Accurately Estimating Critical Fluid Saturation and Approximate Transient Solutions with Production Time Scales in Centrifuge Core Plug Experiments

Abstract The centrifuge experiment is used to measure capillary pressure in core plugs by forced displacement (imbibition or drainage): strong gravitational forces (imposed by rotation) displace fluid held in place by capillary forces. This setup is also used to measure and establish critical saturation, the saturation where a fluid loses connectivity and can no longer flow. Obtaining this saturation is challenging as the capillary end effect causing outlet fluid accumulation theoretically only vanishes at infinite rotation speed. Practical speed limitations include maintaining core integrity and avoiding unrepresentative capillary desaturation. In tight or strongly wetted media the capillary forces are strong and more challenging to overcome. Firstly, we demonstrate an ‘intercept method’ to estimate critical saturation. It states that average saturation is proportional to inverse squared rotation speed (at high speeds) allowing to determine critical saturation by linear extrapolation of a few measurements to the intercept where inverse squared speed is zero. The linear trend is valid once the core saturation profile contains the critical saturation. The result follows as the saturation profile near the outlet is invariant and only compressed while the other saturations equal the critical saturation. Although it was assumed the gravitational acceleration is uniform (reasonable for short cores and long centrifuge arm), the result was highly accurate even for extremely non-uniform gravity along the core: the data are linear and the correct critical saturation value is estimated. This was justified by that the end effect profile is uniformly compressed even under those conditions since most of it is located in a narrow part of the core. Secondly, an analytical solution is derived for transient production after the rotation speed is increased starting from an arbitrary initial state towards equilibrium. For this result we assume the outlet profile compresses also during the transient stage. The two regions have fixed mobilities, while the regions occupy different lengths with time. Time as function of production has a linear term and logarithmic term (dominating late time behavior). An analytical time scale is derived which scales all production curves to end (99.5 % production) at same scaled time. We validate the intercept method for high rotation speed data with synthetical and experimental data. For the synthetical data, the input critical saturation is reproduced both for uniform and highly non-uniform gravity along the core. Given the same input as a reservoir simulator, including saturation functions, the analytical transient solution is able to predict similar time scales and trends in time scale (with e.g. rotation speed and viscosity) as numerical simulations. The numerical simulations however indicate that the saturations travel with highly different speeds rather than as a uniformly compressed profile. Especially saturations near the critical saturation are very slow and caused production to span 5 log units of time (the analytical solution predicted 2-3) when the critical saturation was in the core. The correlation better matched low speed data where the critical saturation had not entered the core.

doi: 10.2118/214402-MS

SPE EuropEC - Europe Energy Conference featured at the 84th EAGE Annual Conference & Exhibition

SPE-214402-MS

Country: Europe > Austria (0.28)

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines:

Reservoir Description and Dynamics > Reservoir Simulation (1.00)
Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
Reservoir Description and Dynamics > Formation Evaluation & Management > Core analysis (1.00)
Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (0.68)

Technology: Information Technology (1.00)

A Field-Scale Real-Time Prediction of Reservoir Porosity from Advanced Mud Gas Data

Anifowose, Fatai (Saudi Aramco) | Mezghani, Mokhles (Saudi Aramco) | Badawood, Saleh (Saudi Aramco) | Ismail, Javed (Saudi Aramco)

Abstract In our previous study, we presented the preliminary results of the first attempt to predict reservoir rock porosity from advanced mud gas (AMG) data within the wellbore. The objective was to investigate the feasibility of generating a porosity log while drilling prior to wireline logging and core description processes. Knowing that porosity remains a critical property of petroleum reservoirs, this work improves on the previous research to predict porosity within a field. The methodology leveraged the machine learning (ML) paradigm in the absence of established physical relationship between AMG data, comprising light and heavy flare gas components, and reservoir rock porosity. More than 15,000 data points collected from representative wells in a field were used to prove the possibility of predicting the missing porosity in a well within the field. Optimized models of artificial neural network (ANN), decision trees (DT) and random forest (RF) were applied to the combined dataset. The dataset was randomly split into training and validation subsets in 70:30 ratio simulating the complete and missing sections respectively. Comparing the results of the ANN, DT, and RF models using statistical model performance evaluation metrics, the RF model consistently outperformed the others. In one of the test cases, the RF model gave a correlation coefficient (R-Squared) value of 0.84 compared to 0.46, and 0.78 for ANN and DT models respectively. The RF model also has a mean squared error (MSE) of 0.001 compared to 0.02 and 0.01 respectively for ANN and DT models. Having showed in a previous publication that a multivariate linear regression model could not handle the complexity in the relationship between porosity and the flare gas components, these results have further confirmed the robustness of nonlinear solutions based on the ML methodology. It can be deduced that the ML approach to predicting reservoir rock porosity from advanced mud gas data is feasible and better results are achievable with more research. This study has confirmed the feasibility of predicting porosity at the field scale and the huge benefit in utilizing AMG data beyond the traditional fluid typing and petrophysical correlation processes. The presented approach has the capability to complement existing reservoir characterization processes in assessing reservoir quality at the early stage of exploration. Future work will investigate the impact of integrating the AMG with surface drilling parameters to possibly increase the prediction accuracy.

doi: 10.2118/214398-MS

SPE EuropEC - Europe Energy Conference featured at the 84th EAGE Annual Conference & Exhibition

SPE-214398-MS

Country:

Europe (0.68)
Asia > Middle East > Saudi Arabia (0.46)
North America > United States > Texas (0.28)

Genre: Research Report > New Finding (0.48)

Industry: Energy > Oil & Gas > Upstream (1.00)

Oilfield Places:

Oceania > Australia > South Australia > Cooper Basin > Patchawarra Formation (0.99)
Oceania > Australia > Queensland > Cooper Basin (0.99)
Asia > Japan > Honshu Island > Kumano Basin (0.99)
(2 more...)

SPE Disciplines:

Reservoir Description and Dynamics > Reservoir Characterization (1.00)
Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)

Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Regression (0.69)

NMR-Mapped Distributions of Dielectric Dispersion

Funk, James (University of Houston) | Myers, Michael (University of Houston) | Hathon, Lori (University of Houston)

Abstract Combinations of NMR and dielectric measurements frequently address challenging saturation and wettability determinations in conventional reservoirs. When pore structure effects are addressed, the nuclear magnetic resonance (NMR) characteristics are interpreted based on the evaluations of surface relaxivity, and the dielectric structural response is attributed to the “texture” of the rock matrix. Both pore structure descriptors can be improved if the molecular motions and charge mobility common to the measurements are considered. Similar to the dipolar relaxation equivalence of NMR and dielectric correlation time measurements in the Bloembergen, Purcell, and Pound (BPP) model, we develop a relaxation time correlation assuming representative Maxwell-Wagner relaxations. Dielectric dispersion curves for the carbonate matrix and vug pore components demonstrated by Myers are quantified using a dielectric relaxation time (DRT) model. The modeled pore system fractions are spectrally mapped to the NMR T1 or T2 distributions based on enhanced Debye shielding distances correlated with the conductivity. The characterized NMR distributions are validated with micro-CT pore-size determinations and diffusion correlations. The mapped distributions provide petrophysical insight into the frequently used Archie exponent combination (mn) associated with conductivity tortuosity and additional wettability screening criteria.

doi: 10.30632/PJV64N3-2023a7

SPWLA

SPWLA-2023-v64n3a7

Petrophysics

Country:

Asia > Middle East (1.00)
North America > United States > Texas (0.94)

Geophysics: Geophysics (0.46)

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines: Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)

Technology: Information Technology (0.93)

Analytical Models for Predicting the Formation Resistivity Factor and Resistivity Index at Overburden Conditions

Abstract In this study, new parameters referred to as rock resistivity modulus (RRM) and true resistivity modulus (TRM) were defined. Analytical models were developed based on RRM, TRM, and Archie’s equation for predicting formation resistivity factor (FRF) and resistivity index (RI) under overburden pressure conditions. The results indicated that overburden FRF is dependent on FRF at initial pressure (ambient FRF), RRM, and net confining pressure difference. RRM decreases with cementation factor and rock compressibility. The proposed FRF model was validated using 374 actual core data of 79 plug samples (31 sandstone and 48 carbonate plug samples) from three sandstone reservoirs and four carbonate reservoirs, measured under four to six different overburden pressures. The developed FRF model fitted the experimental data with an average relative error of 2% and 3% for sandstone and carbonate samples, respectively. Moreover, the applications and limitations of the models have been investigated and discussed. Further theoretical analysis showed that overburden RI is a function of RI at initial pressure, TRM, and net confining pressure difference. The developed models supplement resistivity measurements and can be applied to estimate FRF, RI, and saturation exponent (n) variations with overburden pressure.

doi: 10.30632/PJV64N3-2023a3

SPWLA

SPWLA-2023-v64n3a3

Petrophysics

Country:

North America > United States > Texas (0.93)
Europe (0.71)
Asia > Middle East (0.68)

Genre: Research Report > New Finding (0.68)

Industry: Energy > Oil & Gas > Upstream (1.00)

Oilfield Places:

North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
North America > United States > Texas > Permian Basin > Yates Formation (0.99)
North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
(22 more...)

SPE Disciplines:

Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)

Technology: Information Technology (0.93)

Wireless Acquisition for Resistivity Index in Centrifuge – WiRI: A Comparative Study of Three Pc-RI Methods

Abstract Technological improvements and innovations are made to offer solutions with superior efficiency in terms of cost, quality, speed, or all of them. In the special core analysis (SCAL) field, the conventional resistivity index measurement (the porous plate technique) is a cost-effective method that provides good-quality results but is very time consuming. For this purpose, several methods were developed to reduce the time taken to acquire resistivity measurements. In 2017, we proposed the ultra-fast capillary pressure and resistivity index measurements (UFPCRI) combining centrifugation, nuclear magnetic resonance (NMR) imaging, and resistivity profiling. Since 2021, the wireless resistivity index (WiRI) method allows the acquisition of capillary pressure and resistivity index in a matter of days. This method is based on a new in-house system to acquire wirelessly resistivity indexes along a rock sample during centrifugation. The determination of the resistivity vs. saturation curve and the n exponent of Archie’s law is done thanks to an optimization algorithm. In this paper, we present the results obtained from multiple simulations and experiments for WiRI, UFPCRI, and porous plate to discuss the advantages and drawbacks of each method in terms of reliability and experimental duration. Six rock samples are studied. A comparison of the three methods regarding Archie’s n exponent, resistivity indexes, and capillary pressure curves is performed.

doi: 10.30632/PJV64N3-2023a2

SPWLA

SPWLA-2023-v64n3a2

Petrophysics

Country:

Europe (1.00)
Asia (0.68)
North America > United States > Texas (0.28)

Genre: Research Report (0.46)

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines:

Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Reservoir Description and Dynamics > Formation Evaluation & Management > Core analysis (1.00)

Technology: Information Technology > Artificial Intelligence (0.34)

Summary This work describes a statistical rock-physics-driven inversion of seismic acoustic impedance (AI) and ultradeep azimuthal resistivity (UDAR) log data, acquired while drilling, to estimate porosity, water saturation, and facies classes around the wellbore. Despite their limited resolution, seismic data integrated with electromagnetic resistivity log measurements improve the description of rock properties by considering the coupled effects of pore space and fluid saturation in the joint acoustic and electrical domains. The proposed inversion does not explicitly use a forward model, rather the correlation between the petrophysical properties and the resulting geophysical responses is inferred probabilistically from a training data set. The training set is generated by combining available borehole information with a statistical rock-physics modeling approach. In the inversion process, given colocated measurements of seismic AI and logging-while-drilling (LWD) electromagnetic resistivity data, the pointwise probability distribution of rock properties is derived directly from the training data set by applying the kernel density estimation (KDE) algorithm. A nonparametric statistical approach is used to approximate nonsymmetric volumetric distributions of petrophysical properties and to consider the characteristic nonlinear relationship linking water saturation with resistivity. Given an a priori facies classification template for the samples in the training set, it is possible to model the multimodal, facies-dependent behavior of the petrophysical properties, together with their distinctive correlation patterns. A facies-dependent parameterization allows the effect of lithology on acoustic and resistivity responses to be implicitly considered, even though the target properties of inversion are only porosity and saturation. To provide a realistic uncertainty quantification of the estimated rock properties, a plain Bayesian framework is described to account for rock-physics modeling error and to propagate seismic and resistivity data uncertainties to the inversion results. In this respect, the uncertainty related to the scale difference among the well-log data and seismic is addressed by adopting a scale reconciliation strategy. The main feature of the described inversion lies in its fast implementation based on a look-up table that allows rock properties, with their associated uncertainty, to be estimated in real time following the acquisition and inversion of UDAR data. This gives a robust, straightforward, and fast approach that can be effortlessly integrated into existing workflows to support geosteering operations. The inversion is validated on a clastic oil-bearing reservoir, where geosteering was used to guide the placement of a horizontal appraisal well in a complex structural setting. The results show that the proposed methodology provides realistic estimates of the rock-property distributions around the wellbore to depths of investigation of 50 m. These constitute useful information to drive geosteering decisions and can also be used, post-drilling, to update or optimize existing reservoir models.

doi: 10.2118/214407-PA

SPE Reservoir Evaluation & Engineering

SPE-214407-PA

Country:

North America > United States > Texas (0.28)
Europe > Austria (0.28)

Genre:

Research Report > New Finding (0.48)
Research Report > Experimental Study (0.48)

Geophysics: Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)

Industry: Energy > Oil & Gas > Upstream (1.00)

Oilfield Places: Asia > Indonesia (0.93)

SPE Disciplines:

Well Drilling > Drilling Measurement, Data Acquisition and Automation > Logging while drilling (1.00)
Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
(2 more...)

Technology:

Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.87)

Dynamic Well Control Simulation to Ensure Safer Sampling and Deep Transient Testing: Case Studies from the Southeast Asia Region

OnePetroMay, 2023

Abstract The objective of this paper is to present well control challenges, and results of utilizing wellbore dynamic simulation to achieve safer formation tester (FT) sampling and deep transient tests (DTT) operations. Insight will be provided based on the first implementation in a Southeast-Asia offshore well, with focus on pre-job simulation that is validated with measured data to help improve understanding of gas/hydrocarbon interaction with wellbore mud during and after FT pump-out operations. FT involves obtaining formation pressure, pressure transients, and downhole fluid samples, and the latest DTT technology enables larger gas/hydrocarbon volumes to be pumped into the wellbore which requires a comprehensive understanding of the processes involved. Wellbore dynamics accurately predicts the interactions between downhole pumped hydrocarbon and drilling fluid using a dynamic multiphase flow simulator. For the sampling operation, a maximum allowable downhole gas volume is evaluated prior to operation and simulations are compared to surface gas observation obtained during a wiper trip (mud circulation). During DTT operations, pumped formation fluids are routed to a circulating sub, where they are mixed with circulated mud and the mixed fluids are simultaneously carried to surface. Downhole wellbore pressure measurements are sent to a real time cloud-based dashboard and compared with simulations. The ability to weigh measurements against simulations creates a comprehensive understanding of well control scenarios and provides a much safer execution of FT operations than conventional methods. For wireline FT operation, post job comparison showed that the simulation matched well with surface observations during the wiper trip. The simulator accurately predicted the surface free gas arrival compared to mud-gas logging measurements, which confirmed that gas stayed dissolved in the Synthetic Based Mud (SBM) downhole without migrating upwards. For DTT, wellbore pressure measurements were sent in real time to a cloud-based dashboard and are compared to simulations and simulations could be quickly re-run to account for changes in observed formation fluid, downhole flowrates or mud circulation rates. The FT and DTT operations were conducted successfully and safely and in both cases the measured data agreed well with the simulations. With the accurate wellbore dynamics simulator, changes in drilling fluid design, circulating rates, hydrocarbon composition, downhole pump rates, and pump duration for various FT design sequences are quantified, and the downhole well pressure, free-gas distribution along the well geometry, and gas rates on surface can be predicted. This insight provides more flexibility and understanding to plan advanced FT operations and enables larger volumes of hydrocarbon to be pumped downhole. Furthermore, adopting an advanced pressure transient testing method like DTT also aligns with the industrial effort of reducing carbon dioxide emission footprint.

doi: 10.2118/214600-MS

SPE/IADC Middle East Drilling Technology Conference and Exhibition

SPE-214600-MS

Country:

Asia (1.00)
North America > United States (0.68)

Industry: Energy > Oil & Gas > Upstream (1.00)

SPE Disciplines:

Well Drilling > Pressure Management > Well control (1.00)
Well Drilling > Drilling Fluids and Materials (1.00)
Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Data Science & Engineering Analytics > Information Management and Systems (1.00)

Technology:

Information Technology > Architecture > Real Time Systems (0.69)
Information Technology > Sensing and Signal Processing (0.54)