bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparatiive Economic Analysis on Offshore and Land-Based Liquefaction Facilities in Nigeria
Authors: Gbonhinbor Jeffrey , Adebanjo Adedotun , Oruene Azibator , Onyekonwu Michael , Ikporo Bibobra 
Published: July 2016

Abstract
This research examines the applicability of two possible configurations of LNG plants as a means of monetizing Nigeria's gas reserve. It involves an extensive evaluation of LNG technologies both offshore and onshore on an economic comparative basis. Economic analysis was performed on all quantitative data acquired from peer-reviewed academic journals, conference papers and technical reports. Feasible assumptions such as capacity of plants, life of projects and hurdle rate were adopted to enable a fair comparison without distortion. Cash flow analyses were conducted in relation to Nigeria's upstream natural gas utilization tax regimes as regulated by the Petroleum Profit Tax Act (PPTA). Economic yardsticks such as NPV, IRR, PI and PVR were used to rank proposed investments. Results from the deterministic evaluation were presented for offshore and land-based LNG facilities as a base case. These results were extended to evaluate the economic risks associated with uncertainties in feedstock costs, LNG prices, plant capacities, CAPEX and OPEX. Although all proposed investments ensure good profitability with respect to the deterministic approach, nonetheless risk assessment ranked the offshore LNG facility under PSC as the most preferable project. However, Nigeria's gas reserve and flaring practice purports a complementary application of both LNG configurations.
Optimising Reservoir Production in WRFM by Unleashing Locked-in Potential through Trusted Data at Finger's Tip
Authors: Sunday Ewhoforama , Nkonyeasua Oghene , Olumide Ayeni , Gbolade Ibikunle , Tamunotonye William , Efe Ori-Jesu 
Published: July 2016

Abstract
The benefits of credible data and data quality came to bear recently with the restoration of reservoir production in an SPDC field. The reference string produced dry at an average rate of 1500 bopd and Gas Oil Ratio (GOR) of 2000scf/bbl till June 2000 when water breakthrough occurred. GOR increased sharply by 2011 and the interval was closed-in in 2012 at GOR of 8Rsi (Rsi-initial solution gas oil ratio).
A previous integrated review proposed water shut-off (WSO) as well as gas shut-off (GSO) to be executed so that this interval can be brought back on stream. In September 2015, an integrated review of Closed-in Wells was done in which all available data pertinent to this well was analysed and reviewed. Based on this comprehensive data, the review team arrived at a conclusion to open up and test the well rather than WSO/GSO proposed in the previous review. In making this decision, the team was convinced it had access to credible and reliable data set. This is enabled with the creation of The Asset Standard Well Reservoir and Facility Management (WRFM) Technical Data Management Plan (TDM) that clearly states data repositories, data owners and focal points. These data sets were easily retrieved from the various data repositories.
The Asset Standard Well Reservoir and Facility Management (WRFM) Technical Data Management Plan (TDM) clearly states data repositories, data owners and focal points. The string was opened on October 25, 2015 and it was observed to be stable and flowing at 435 bopd, in line with the review forecast. Well testing of October 29, 2015 validated the production rate at 462bopd, 700scf/bbl (1.6*Rsi) at 27% BSW.
The availability of credible and trusted data has therefore helped in optimising production of this well. There is also a cost saving typical of a water shut-off activity. Credible data that are easily accessible can make a difference on the business bottom line especially in optimising production, managing integrity and emergency repairs of an asset. These were demonstrated in the closed-in well review that has led to increased production and cost savings – the benefits of credible data.
Enhanced Geosteering With While-Drilling Productivity Evaluation: A Methodology to Optimize Horizontal Well Productivity
Authors: Okoroafor Rita Esuru , Ekhaesomhi Peter , Ipinyemi Gbemiga , Oyenuga Funso , Ilesanmi Olumide , Haq Shahid , Zhou Wentao , Ortenzi Luca , Nnebocha Ezinne 
Published: July 2016

Abstract
Geosteering is a proven concept which enables the delivery of productive wells by ensuring such wells stay within the target reservoir and sweet spot, and are precisely placed relative to fluid contacts. When reservoir engineering concepts are combined with Geosteering, quantitative estimates of the well's productivity can be determined to further evaluate the performance of the Geosteering and aid in decision-making. This workflow, applied in real-time, is called Real-Time Productivity Steering (RTPS).
This paper describes the application of RTPS to a horizontal well placed in a thin layer of oil reservoir. Prior to performing the real time productivity estimates while drilling, a prejob model was built, using offset well data, to estimate productivity from the planned well. Sensitivity analysis was performed on the trajectory, well length and some rock and fluid properties, to determine the plan that would result in optimum productivity for the well.
While the well was drilled, Logging While Drilling (LWD) information was used to update the existing model, geosteer, and perform productivity prediction while drilling. Productivity was predicted at three target depths: 8400 ft MD, 8800 ft MD, and at Total Depth (TD). The increasing liquid productivity index (PI) at the selected target depths were further justification to continue drilling until the predetermined TD.
The results at TD showed that the open hole liquid PI of the well in the sand of interest was about 63.8 stb/d/psi for a stable production rate of 1200 blpd. When completions were imposed on this single well reservoir model, it was predicted that the well will produce at a stable rate of 750 bopd. This was compared with a preliminary test done on the well where for the same well and reservoir conditions, the well produced 800 bopd.
This paper demonstrates that quantitative estimates of productivity can be made while drilling to complement geosteering. In addition to complementing and enhancing geosteering, RTPS provides a reliable dynamic model for subsequent completion design.
The RTPS workflow of complementing geosteering with reservoir engineering will enhance the ability to drill more productive wells as it will enable wells to be steered in the direction of maximum productivity, and also provide operators with sufficient information while drilling to make informed decisions for completions and for future wells.
Dimensionless Inflow Performance Relationship IPR for Gas Wells Using the Back Pressure Equation
Authors: Akineste Oluwatoyin O. , Olujinmi Temitope O. 
Published: July 2016

Abstract
Dimensionless inflow performance relationship (IPR) has been developed for unfractured gas reservoirs using the laminar inertial turbulence (LIT) relation with various assumptions. This is evident from papers earlier presented where predictions from the LIT correlation closely match field data. Development with the backpressure empirical relation has had little progress. An assumption of n=1 for the backpressure relation over predicts gas flow rate by approximately 19% for an Alberta field.
This paper presents the development of dimensionless IPR correlations which predicts current and future deliverability of an unfractured gas reservoir using the backpressure empirical relation. The developed dimensionless IPRs accounts for turbulence by accounting for the range of turbulence factor (n) between 0.5 – 1. Developed using Microsoft Excel with several data points to account for varying reservoir properties. The best fit curves were determined, and the equations relating qgqgmax to pwfqr and that relating qgmaxfqgmaxp to prfprp for present and future deliverability respectively are expressed.
The developed dimensionless IPR correlations were tested for accuracy and validity by comparing with results from isochronal and modified isochronal tests. The IPRs shows consistency with field data, and therefore can be used in the calculation of the deliverability potential of a gas reservoir.
Artificial Neural Network Model for Predicting Wellbore Instability
Authors: Okpo E. E. , Dosunmu A. , Odagme B. S. 
Published: July 2016

Abstract
Drilling activities have progressed to deep and ultra deep seas in recent times and with it comes more challenges. Due to the difficulty of directly obtaining important parameters like in-situ stress and fracture gradient, simple models have been evolved. This study is a novel attempt to make up for the gap inherent in such models namely that they neglect chemical and thermal effects, settling for only effective stress and a time-dependent analysis. The study applied the Neural Network (NN) technology to predict geomechanical parameters. Neural Network (NN) as a branch of Artificial intelligence (AI) possesses the ability of training available parameters to replace data that cannot be immediately or easily acquired. Data of a well drilled in the Niger Delta Region of Nigeria was used as the case study. A training set of input data was used to train the network and a validation set ensured a completely independent measure of network accuracy. A Neural Network model was developed in Neuroph Studio, Java neural network platform and the Netbeans IDE. The model has the advantage of being easy to use, open source, cross-platform and generally designed to save the cost associated with wellbore instability.
Reducing Unit Technical Cost Through Improved Crude Handling Contract Management
Authors: Arabi Abubakar Bello 
Published: July 2016

Abstract
Profitability remains the key driver for investment in the oil & gas business. Profit margins are enhanced through cost minimisation or revenue maximization, and in some instances, companies deploy tools to guarantee the combination of both to deliver increased corporate profit.
Given the relative challenges associated with ability of players in the industry to grow production capacity and increase market share/revenue, cost minimisation becomes the tool deployed most often to achieve increased profitability. As the global oil prices continue to plummet, focus on cost reduction/saving as a tool for profitability has gained renewed attention from industry players. Thus, technical cost or unit technical cost remains a critical KPI for measuring operational efficiency/profitability in the E&P business.
Crude handling cost, which covers the cost of crude transportation and terminaling, constitute significant chunk of operating cost for most E&P companies in Nigeria. With an average contractual price of $3.5 per barrel of wet crude and $2.81 per barrel of dry crude, driving down this cost element underpins a significant cost saving for any E&P company. Alas, actual handling cost per barrel is always higher than the contractual rate per barrel. The magnitude of variance depends on the operational efficiency of the company and how best it has been able to manage Reserved Production Capacity (RPC) vis-à-vis the ability to perform the work programme upon which its production forecast that informed the choice of RPC was premised.
In this article we seek to demonstrate how NPDC was able to reduce unit technical cost through the combination of deployment of optimal RPC selection model and increased vigilance/effective CHA Management. In addition, we intend to also demonstrate how related actions such as increased-RPC-Review-Window (IRRW) could poster greater collaboration between facility-users and facility-owners and help achieve reduce crude handling tariff rates in a fairly win-win scenario.
Improved Correlation for Predicting Stock Tank Gas-Oil Ratio in Niger Delta
Authors: Okeke Hillary , Sylvester Okotie 
Published: July 2016

Abstract
The estimation of the stock tank gas-oil ratio (GOR) cannot be over-emphasized because it is a vital surface parameter required in the estimation of cumulative gas-oil ratio, the stock tank production rate and bubble point solution gas-oil ratio. Ideally, this property is determined via laboratory measurement which is not done regularly due to cost and sampling time. The three popular correlations developed for predicting this property were found to be less applicable in Niger Delta region. This challenge now led to the development of a new correlation by the author of this paper using Niger Delta data which is more applicable than the existing ones but still associated with appreciable error in its prediction. In this study, an improved stock tank GOR correlation for Niger Delta crudes is presented so as to reduce error of prediction. Unlike the former correlation, it is a non-linear four parameter mathematical model that depends on stock tank oil API gravity, separator pressure, separator temperature and separator gas gravity. A total of 222 PVT reports were obtained and validated using material balance, Campbell and Hoffman's Plots. Microsoft excel solver was used to generate the improved model for stock tank gas-oil ratio. The model was then validated with existing correlations including the one from Niger Delta region using statistical analysis and performance plots. The results of the validation showed an improvement in the accuracy of the prediction.
Artificial Neural Network Modelling of Bubblepoint Pressure and Formation Volume Factor at Bubblepoint Pressure of Nigerian Crude Oil
Authors: Adeeyo Yisa A. 
Published: July 2016

Abstract
Reservoir fluid properties data are indispensable in reservoir engineering evaluations and computations. Ideally, those data should be obtained experimentally. On some occasions, these data are either not available or grossly unreliable; then, empirically derived correlations are used to predict PVT properties. Most correlations in the literature were either modelled using regional data or their level of accuracy is low.
Artificial neural network is a new computation strategy that could be used to generate model for predicting PVT properties. It consists of simple arrangement of nodes, called neurons, used for pattern recognition, modeled after a representation of human brain.
The author has explored the use of neural network in estimating bubblepoint pressure and formation volume factor at bubblepoint pressure with the aim of getting more accurate prediction compared to available correlations in the literature.
The number of data set used for bubblepoint pressure is 2114 and oil FVF at bubblepoint is 2024. Various neural network designs with one-hidden, two-hidden and three-hidden layers were considered and tested. The number of neurons in the hidden layer was varied until stable network was achieved. Each successful trained model was tested to ensure that overfitting does not occur and can predict output from inputs that were not seen by the model during training. Sixty percent of the data was used to train the network, twenty percent to cross-validate the relationships established during the training process and the remaining twenty percent to test the model.
The results of this study show that the model gives better prediction and higher accuracy compared to the published bubblepoint pressure and formation volume factor empirical correlations.
Gibbs Excess Energy Model to Predict Phase Behaviour of Brine/Oil/Surfactant Mixtures for Chemical Enhanced Oil Recovery CEOR Methods
Authors: Adeeyo Yisa A. , Awang Mariyamni bint 
Published: July 2016

Abstract
With the increase in the world population, increases in scientific and industrial activities have engendered spontaneous upsurge in the global demand for oil and gas in the past few years. These resulted in depletion of major world oil reservoir without corresponding discovery of new major oil fields.
However, substantial amount of oil is trapped inside the reservoir. This oil is being held by capillary force which depends on interfacial tension (IFT) between oil and water. Therefore, reducing the IFT releases the oil and enables greater recovery.
Surfactant (surface acting agent) reduces IFT and has been tested to be appropriate for use under reservoir condition. A mixture of surfactant, brine and oil may produce microemulsion depending on the proportion of the amount of the components and thermodynamic parameters involved. Microemulsion reduces IFT. The practice in the industry is to conduct laboratory experiments to investigate various formulations and identify the thermodynamic conditions that will yield microemulsion phase.
This study is aimed at developing mathematical thermodynamic model and computation algorithm using excess Gibbs energy, fugacity and group contribution model (GCM) for the estimation of critical properties of the pseudo-components to predict phase behavior of water/oil surfactant mixture in the regions and beyond those their measurements have been conducted in the lab.
The model was validated by comparing its performance to lab results. The lab experiments involved measuring the phase behaviour of the mixture of Alfa Olefin Sulfonate (AOS) surfactant, decane and brine.
The model shows good performance and will be useful in the prediction of phase behavior of water/oil surfactant and can be used in compositional simulation of surfactant flooding operations and should be an invaluable tool in planning and evaluation of surfactant flooding processes.
Forecasting Gas Compressibility Factor Using Artificial Neural Network Tool for Niger-Delta Gas Reservoir
Authors: Irene Azubuike Ijeoma , Sunday Ikiensikimama Sunday , Orodu Oyinkepreye David 
Published: July 2016

Abstract
Accurate prediction of gas compressibility factor is important in engineering applications such as gas metering, pipeline design, reserves estimation, gas flow rate, and material balance calculations. This factor also is important in calculating gas properties such as gas formation volume factor, gas isothermal compressibility, viscosity and density. Compressibility factor value shows how much the real gas deviates from the ideal gas at a given pressure and temperature. Most often, compressibility factor values can be determined experimentally from collected laboratory samples but frequently this measurement is not always available. In such cases, the natural gas property can be determined using empirical correlations or iteratively using equation of state (EOS). Therefore, the aim of this work is to develop ANN model to accurately predict the gas compressibility factor; as well to compare its performance with existing empirical gas compressibility factor correlations. The new model was developed using 513 PVT data points obtained from Niger-Delta region of Nigeria. The data used wasrandomly divided into three parts, of which 60% was used for training, 20% for validation, and 20% for testing. Both quantitative and qualitative assessments were employed to evaluate the accuracy of the new model to the existing empirical correlations. The ANN model performed better than the existing empirical correlations by the statistical parameters used having the lowest rank of 1.37 and better performance plot.
Understanding the Geometry and Structural Controls of Fluid Levels in Relay Systems, Nigeria
Authors: Mora-Glukstad Miguel , Ugboaja Remmy 
Published: July 2016

Abstract
A review of many oil/gas fields in Nigeria shows that most structures are under-filled (the fluid contact is much shallower than the structural spill point). This could be explained in different ways. Hydrocarbon charge has been suggested as a possible explanation, but we do not believe this to be the main cause in such a rich hydrocarbon province, like the delta, and where there are excellent carrier beds and world class source rocks, all throughout the system.
Another possible cause could be what we call a "dynamic top seal," where there is some degree of leakage either due to capillary forces or facilitated by crestal faulting. An ongoing analysis on capillary entry pressure and mechanical strength of the top seal, shows that this could be the case in some shallow reservoirs, but a "leaky" top seal does not appear to be a major issue in relatively deeper reservoirs.
An updated seismic interpretation was carried out in field X where part of the focus was to understand what controls the current fluid contacts. This study has direct implications towards predicting fluid contacts in near field opportunities in undrilled blocks for proper placement of development wells. Field X has one single well but there are undrilled blocks with similar structural geometries. These learnings will also help to carry out predictions of in place volumes for near field development.
This work showed that more careful and detailed structural mapping of the fault systems provides a view of fault geometry where relay ramps clearly control the position of the fluid level. The position of the initially planned well changed. We are applying these learnings and testing if this observation can be replicated in other fields, once more detailed mapping is carried out.
Prediction of inventory sustainability during a drug shortage
Authors: Rhodes Nathaniel J. , Gilbert Elise M. , Skoglund Erik , Esterly John S. , Postelnick Michael J. , McLaughlin Milena M. 
Published: July 2016

AbstractPurposeA methodology for predicting how long the on-hand inventory of a given medication will last during a supply shortage is described; a practical example of application of the methodology is provided.MethodsSingle-site data on consumption of i.v. tobramycin over an eight-month evaluation period were collected using commercial software that tabulates barcode-assisted medication administration (BCMA) events; administered doses were standardized as 1200-mg “vial-equivalents” and summed over the review period. The total number of vial-equivalents consumed was divided by the number of “non-zero weeks of consumption” (i.e., weeks during which any tobramycin use occurred) to obtain a mean ± S.D. weekly consumption rate; this rate was multiplied by the total i.v. tobramycin on-hand supply (in vial-equivalents) to determine the mean number of potentially sustainable weeks of therapy in the event a shortage were to restrict the future supply of the drug.ResultsOverall, 99.6 vial-equivalents of i.v. tobramycin were used during the evaluation period. The mean ± S.D. number of vial-equivalents used per non-zero week of consumption was 3.11 ± 1.26. A manual count of pharmacy inventory revealed that 102.9 vial-equivalents were available at the time of analysis. The mean predicted duration of supply was 33 weeks (95% confidence interval, –126 to 192 weeks).ConclusionAvailable BCMA data on tobramycin consumption over eight months were used to calculate the mean number of weeks the on-hand supply of the drug could be expected to last during a persistent drug shortage.