bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cement Packer: Recovery of Bypassed Reserves in Highly Deviated Well Using Large-Volume Cement Through Coiled Tubing, Offshore Niger Delta
Authors: Amos C. , Osanaiye E. , Louis S. , Ighavini E. , Ahabike G. , Olofin A. , Ejukorlem-Okusi R. 
Published: July 2019

Abstract
Recently, a multinational exploration and production (E&P) company identified an opportunity for production optimization in a shallow reservoir with the zone of interest behind casing and without using any form of isolation for completions.
A major challenge during this operation was the engineering design, as this was the first time a catenary cement operation of more than 31.8 m3 (200 bbl) of slurry would be pumped through coiled tubing (CT), in addition to the high well deviation (~79°) consideration for installation of the cement retainer. Additionally, insufficient deck space on the platform and load-bearing capacity to accommodate the intervention spread presented a potential derailer for the attic oil development.
A rigless well intervention program was designed using a 1.75-in. (125K) CT catenary system unit, which could be installed on a barge. The system would allow better CT reach and a higher injector snub/pull capacity. CT was used to install the cement retainer, punch holes in the 4 1/2-in. tubing, and pump cement.
A total of 34.6 m3 (218 bbl) of 15.8-lbm/gal resilient slurry was pumped through CT, which was equivalent to pumping a cement volume approximately six times greater than the CT volume. This was necessary to place a 1056-m cement packer in the 4 1/2-in. tubing 9 5/8-in. casing annulus and place 200 m of cement on the cement retainer.
The operation was successfully completed using CT, avoiding more costly rig options for recompletion of the well. Zonal isolation was achieved, and the bypassed reservoir pay was perforated with ~600 B/D production. This approach proved to be cost-effective, with savings of approximately USD 1.3 million, which was achieved without compromising operational and safety performance.
Rigless recompletion has been a cost-effective approach for production optimization in mature fields. However, existing well architecture, access to bypassed hydrocarbon pays, and environmental characteristics present challenges for developing some of these reserves.
Between PPT and PIFB 2018: Risk Assessment of Stakeholders for an Integrated Petroleum Asset Development
Authors: Gbakon Kaase , Iledare Omowumi , Adeogun Oyebimpe 
Published: July 2019

Abstract
This paper performs uncertainty quantification (UQ) to capture the risk – both from investor and government perspective – to which an integrated petroleum development project is exposed to. The current fiscal system will be compared against the proposed PIFB 2018. Following the field development concept, the comparative economics are developed using Discounted Cash Flow (DCF) in recognition of the extant fiscal provisions. The DCF is expressed in nominal terms with sensitivity and stochastic modelling. The integrated development concept incorporates a 12kbpd refinery and a 150mmscfd gas plant on a 250mmboe onshore marginal field. The results indicate that the Petroleum Industry Fiscal Bill (PIFB 2018) delivers nearly twice in expected investor value than the current Petroleum Profit Tax/Marginal Field Regulations (PPT/MFR) on the integrated project. Furthermore, government take (GT%) shrinks from 45% under the PPT/MFR to 28% under the proposed regime. Stochastic analysis shows that investors are less exposed to failure under PIFB fiscal terms and instruments than the PPT/MFR. There is a higher likelihood (54%) of investor failure under the PPT than the 46% probability of a loss under the PIFB. The expected GT under PIFB is lower than that under PPT, however, there is significant likelihood (&gt;65%) that neither system will deliver to government as much as the expected lifecycle Take. However, decoupling the value chain reveals varying risk reward profiles for the different segments with implications for policy formulation. A key insight drawn from the study is for policy makers to encourage the development of integrated projects to deliver a "portfolio" government take. This will smoothen out volatilities in tax receipts given that in this integrated development, government inflows from the different value chain components have different timings, levels and uncertainty.
Using Cloud Based Well Engineering Data and Wellbore Integrity Analysis to Reduce Risk in Niger Delta Workovers
Authors: Seymour K. P. , Stuart C. 
Published: July 2019

Abstract
The Niger Delta is a prolific oil and gas province with almost 160 oilfields and 1,500 wells put on production since the 1960s. Many of these wells have been producing for decades and now find themselves in hands of new operating companies of limited resources. There are strong commercial drivers to keep these wells in production through rapidly planned workover operations. The historical records for these wells are poor and in many instances nonexistent, often being no more than a very simplistic A4 completion diagram lacking in details and summary. The basis for design and the history of numerous interventions has long been lost and the focus of new operators is often on only the production casing and the tubing strings. However, it is imperative to consider the full well architecture and construction history because some were design optimized with minimal casing setting 13 3/8" shallow and completing in 9 5/8". Worst case consequence of this design approach is well blowout and cratering if the production casing fails with a gas column to surface. A generic case is presented to emphasize the reality of this risk. The risks of extending well life, compounded by change of use in late life are discussed in the context of design validation. The causes and challenge of poor well engineering data are reviewed, to establish the historical norms in not only the Niger Delta but globally. The importance of tracking down data in the well Intervention planning process is emphasized. It is further proposed that controlled, as-built drawings of all wells, which do not omit casing details, lithology, and critical component features such as wellheads casing hanger seals and Xmas tree should be electronically created, cloud stored and used as a basis for well bore integrity analysis prior to undertaking interventions/workovers. An example from a North Sea Development Project is presented. Such a system should also be used to establish which critical barriers exist during the intervention, how they will be validated, and how is evidence captured in order to address the typical human factors which are fundamental in many blowouts. Critically the system should produce accurate well integrity reports during operations to track compliance with the plan. A generic example for a typical Niger delta dual bore completion well is presented.
Leak Detection in Natural Gas Pipelines Using Intelligent Models
Authors: Akinsete Oluwatoyin , Oshingbesan Adebayo 
Published: July 2019

Abstract
Detection of small leaks in gas pipelines is an important and persistent problem in the oil and gas industry. However, the industry is beginning to investigate how tools of Machine Learning, Artificial Intelligence, Big Data, etc. can be used to improve current industry processes.
This work aims to study the ability of intelligent models to detect small leaks in a natural gas pipeline using operational parameters such as pressure, temperature and flowrate through existing industry performance metrics (sensitivity, reliability, robustness and accuracy). Observer design technique was applied to detect leaks in a gas pipeline using a regresso-classification hierarchical model where an intelligent model acts as a regressor and a leak detection algorithm acts as a classifier. Five intelligent models (Gradient Boosting, Decision Trees, Random Forest, Support Vector Machine and Artificial Neural Network) were used in this present work.
Results showed that the Random Forest and Decision Tree models are the most sensitive as they can detect a leak of 0.1% of nominal flow in about 2 hours. All the intelligent models had high reliability with zero false alarm rate in testing phase. However, due to this level of reliability, the models had low accuracy with the Artificial Neural Network and Support Vector Machine performing best and better regressors than the others. All the intelligent models are robust. The average time to leak detection for different leak sizes for all the intelligent models were compared to a real time transient model in literature. The intelligent models had a time savings of 25% to 48%.
Results in this present work further suggest that intelligent models could be used alongside a real time transient model to improve leak detection. Also, that the tools of big data, data analytics, artificial intelligence can be harnessed to improving leak detection results.
The Use of 4D & Dynamic Synthesis in Brown Field Development: A Case Study of S-P3 Infill Well Maturation, Preparation and Drilling
Authors: Uwerikowe G. , Oshewa O. , Wantong P. , Usman M. 
Published: July 2019

Abstract
Based on the Field Development Plan (FDP), AKPO Reservoir ST was developed with two crestal producers (ST-P2 and ST-P1) and two water injectors at the periphery (ST-W2 on the eastern flank and ST-W1 on the western flank). However, the results of a time lapse seismic monitor (4D M-2) in addition to dynamic simulation showed that the attic oil of S-Reservoir would be bypassed at the end of Field life. The time lapse seismic monitor (4D M-2) results also showed that the main producer in the S-Reservoir; ST-P2 was risking early water breakthrough from the ST-W2 water injector at the toe. Hence, the need for an infill well to mitigate these identified risks. This paper presents the maturation & drilling strategy, well results and the production performance of the first infill well of the AKPO Field.
The infill maturation strategy involved the update of the geological model with recent well/ 4D seismic information, evaluation of the present static uncertainties on in-place volumes, history matching of the dynamic model in line with current field behaviour and the evaluation of the incremental production and economic value of the infill well.
S-P3 trajectory was designed as a sub-horizontal drain to target and produce the attic oil in the S-Reservoir up-dip of producer ST-P2. It was completed across two fault blocks to ensure drainage of the southern panel as fault behaviour remains one of the main uncertainty. The well trajectory was placed as far up-dip as possible, with its toe set higher than the shallowest completion of ST-P2 in the S-reservoir.
The final well result for S-P3, drilled in Q4 2016, was outstanding, thanks to excellent integration of seismic, geological and dynamic data. The main results included: Optimal well placement, as a result of real time adjustments of the well trajectory aided by reservoir navigation toolsMore attic oil found as a result of shallower top of S-ReservoirIncremental production of 15 kboepd over 2 years and an incremental reserves of ∼10 Mboe.
Logging While Drilling Cement Evaluation: Case Study in Deep Water Gulf of Guinea
Authors: Itua Osazua Joshua , Jenakumo Timipere , Ringim Mohammed Hafiz , Kuijper Andre de 
Published: July 2019

Abstract
Assessing well integrity through cement bond analysis is recommended and sometimes legally required because the potential risk of a poorly executed cement job on people, assets and environment during- and post well construction can be catastrophic. Traditionally, cement bond logs were acquired with wireline tools. Following advances in tool response modelling and log data processing, Logging While Drilling (LWD) acoustic data for cement bond analysis - acquiring the data while tripping in or tripping out of hole - has become an attractive and viable option to consider over traditional wireline data acquisition. The LWD option comes with the added advantage of substantial savings in rig time while eliminating operational complexity and safety risks associated with additional wireline logging operation particularly in high angle- and extended reach wells.
To assess the robustness of the LWD cased hole cement evaluation logs and to validate its application to Deep Water (DW) wells, LWD and wireline acoustic logs were acquired back-to-back in three wells in the DW Gulf of Guinea (GoG), and the bond index estimated from both acquisitions were compared. This paper presents the outcome of the work and provides support to the opportunity to consider LWD acoustic data acquisition for quantitative cement bond evaluation.
Effect of Water Salinity on Crude Oil Viscosity in Porous Media at Varying Temperatures
Authors: Ogolo Naomi A. , Adesina Daniel O. , Akinboro George O. , Onyekonwu Mike O. 
Published: July 2019

Abstract
Viscosity is one major property of crude oil that is highly dependent on temperature and affects production operations. But temperature is not the only factor that affects oil viscosity; other factors include oil composition, pressure and dissolved gas. However, it has been observed that water salinity can also affect crude oil viscosity. It is therefore imperative that the effect of water salinity on crude oil viscosity be studied since water injection schemes using produced water or sea water is a common practice because it is economical and efficient in oil displacement from porous media.
This paper studies the effect of water salinity on crude oil viscosity at temperatures of 30°C, 70°C and 110°C. Sand samples were flooded with crude oil of known viscosity and brine of different salinity values ranging from 30g/l to 70g/l, after which the oil viscosities were determined again as well as the brine density. The flooding experiments were conducted like relative permeability experiments under steady state with an overburden pressure of 3000psi, using a standard core flooding equipment at a flow rate of 2ml/min.
The experimental results show that water salinity can increase crude oil viscosity. It was observed that the viscosity of the oil varied from 0.5cp to 2.3cp at different water salinity levels which could have been as a result of the chemical interaction between the two fluids. The effect of water salinity on the oil viscosity was more obvious at 30°C than at 70°C and 110°C, indicating that temperature could have played a vital role in the interaction process. On the other hand, the variation in water density after the oil-water interaction was more significant at higher temperatures than at 30°C. It was also observed that the maximum oil viscosity at 30°C was 2.26cp at 50g/l water salinity while 30g/l salinity gave the highest viscosity values of 1.29cp and 1.06cp for 70°C and 110°C respectively. This stresses the importance of conducting such studies with water samples intended to be used for water injection. It is therefore recommended that the effect of water salinity on crude oil viscosity be studied before embarking on any water injection scheme since high oil viscosity is not desired during oil production operations.
Predictive Data Analytics for Effective Electric Submersible Pump Management
Authors: Sherif Sanusi , Adenike Omisore , Obehi Eremiokhale , Funso Adebowale , Eyituoyo Blankson 
Published: July 2019

Abstract
Electrical Submersible Pump (ESP) failures cause disruptions that lead to production deferement besides the cost of interventions/workovers post failure. The service life of an ESP is difficult to predict as it is affected by several factors which include reservoir characteristics, pump operating conditions and even the installation procedure. Measurement and monitoring of both dynamic and static ESP parameters play a critical role in extending the run-life. However, due to the complex nature of ESP failures, it can be difficult to identify anomalies by simply trending data.
Notable progress has been made in the past years with respect to the development of systems for monitoring but most operators are yet to fully leverage on a system that will allow for proactive ESP health condition monitoring.
In this paper, generalized machine-learning techniques and information acquired through real-time streaming was used to predict impending failures. This study applies Principal Component Analysis (PCA) on ESP intallations for a marginal field in the Niger Delta where production optimization and cost reduction are key to sustenance. Python was used for the data processing/statistical analysis and the algorithm development. The major objective of the PCA was to identify correlations in the dynamic ESP parameters: Intake Pressure, Intake Temperature, Discharge Pressure, Vibrations, Motor Temperature, Motor Current, Systems Current and Frequency recorded by the Variable Speed Drive (VSD) at regular intervals. Once the correlation/pattern was identified, the PCA approach found the directions of maximum variance in the high-dimensional data (in this study eight-dimensional) and projected it onto a smaller dimensional subspace while retaining most of the information. For each installation, a stable region for the operating frequency was identified and failed ESPs showed a clear drift from the stable region months before the failure occured, which was not apparent in the recorded parameters from the VSD.
The paper describes how to use Machine Learning (ML) algorithms to predict an ESP's runlife bringing the industry a step closer to proactive ESP monitoring as opposed to the current reactive methods.
Bottom-Hole Pressure Estimation from Wellhead Data Using Artificial Neural Network
Authors: Akinsete Oluwatoyin , Adesiji Blessing Adetoye 
Published: July 2019

Abstract
Accurate pressure losses prediction for flow in tubing installations is of great importance in the petroleum industry. Historically, the Bottom-Hole Pressure (BHP) determination was obtained using down-hole pressure gauges, because of the economic disadvantage and redundancy, this procedure seems to be less effective, this led to the adoption of the BHP prediction process in estimation. The wide acceptance of data-driven analytics makes the estimation procedure a valid approach in the industry today. Recently, the Artificial Neural Network (ANN), a technique which has been widely accepted because the model proved to predict better than the conventional correlations.
This present work aims to develop a prediction model for BHP based on input and output data obtained from the Volve production field in Norway. Machine learning algorithm based on ANN was used to predict and further improve the accuracy of the prediction while considering a large production dataset from different wells of the field. In developing the model, the initial dataset was processed to about 2,500 data points; the model was trained, tested and cross-validated based on the parameters from the data. Results affirmed that ANN has the ability to handle large dataset, results also revealed that ANN outperformed other models, with a Coefficient of Determination of 0.99997, Root Mean Squared Error of 0.07405 and Mean Absolute Error of 0.02657, which shows high predictability of the model. These results indicated that the ANN model gives a better prediction of BHP when compared to other mechanistic models.
Finally, this work supports the claim, that Production engineers can accurately predict the pressure at the sand-face of a producing well without the use of expensive BHP gauge.
Delineating Fracture Patterns from Seismic and Well Derived Multi-Attributes Data
Authors: John Rotimi Oluwatosin , Liang Yao , Wang Zhenli 
Published: July 2019

Abstract
Structural complexities are concerns frequently encountered as peculiar features in most hydrocarbon provinces which various attempts has been made to resolve using conventional spatial images of the subsurface as continuous seismic reflections. These structural patterns are either tectonic or sedimentonic in origin and confers anisotropy and heterogeneity in subsurface formations thereby increasing the uncertainty associated with exploring hydrocarbon locked in their pores. Diffraction imaging has been used in various instances to delineate subtle discontinuities that synthesize diffraction waves in hydrocarbon formations, in tandem with reflection seismic and other invasive geophysical logs and core, in comparatively understanding fracture orientations and establishing relationships with the temporal data that are often regarded as most reliable. These data points have been used to infer structural relationship between the naturally fractured clastic formations in an oil and gas field with success. Rock elastic properties and signal anisotropy derived from core and logs resulted in structural attributes that correlated well with seismic diffraction volume inferred properties, giving credence to the relationships established and usefulness of the diffraction volume for characterizing and emphasizing small scale features hitherto classed as sub-seismic.
A Machine Learning Approach to Shear Sonic Log Prediction
Authors: Bukar Idris , Adamu M. B. , Hassan Usman 
Published: July 2019

Abstract
A machine learning approach to shear sonic log prediction is demonstrated. The results of this approach were compared to that of an approach based on the Greenberg-Castagna empirical method. This approach is based on supervised machine learning and is implemented in MATLAB. While the Greenberg-Castagna method is an empirical method that attempts to predict shear velocity log from compressional velocity log for various pure and composite lithologies, this approach uses, in addition to compressional velocity log as the main predictor, several other logging measurements as predictors including gamma ray, bulk density, neutron, resistivity, porosity and water saturation logs. A dataset which includes wells with recorded shear velocity logs is used to train and validate the machine learning model. A feature selection process is performed to highlight which of the logs would be good predictors of shear velocity (VS). Various regression models are then trained, and the predicted values compared to the actual for the various models by their root-mean-square errors (RMSE), and the model with the smallest RMSE is chosen. Predictions are then carried out on another well within the dataset, which serves as the validation set. The results show improvement in the accuracy of the predictions over the linear regression model based on the Greenberg-Castagna method, as measured by the RMSE. The case study also demonstrates the potential of carrying out shear sonic log prediction in hydrocarbon-bearing intervals, which is a limitation of the Greenberg-Castagna method which only works in brine-saturated rocks. This approach would provide improved accuracy where shear sonic logs are absent and need to be predicted for geomechanics, rock physics and other applications. This is particularly important in older fields where shear sonic logs were never acquired in the older wells.