This work detailed the procedure employed in inverse calculation of gas composition using separator information. Starting with flash calculation, we develop a method to determine the separator measurement (Condensate gas ratio (CGR), Gas specific gravity (γ
Unlike previous methods such as described in [
Idowu, Joseph Adekunle (African University of Science & Technology) | Iledare, Omowunmi O. (Emerald Energy Institute, University of Port Harcourt) | Adeogun, Oyebimpe (Emerald Energy Institute, University of Port Harcourt) | Echendu, Joseph C. (Emerald Energy Institute, University of Port Harcourt)
The paper aims to; estimate relative technical efficiency (RTE) for each of the Nigeria 32 active Operators using vDEA model, rank the performance of the Operators considering output orientation, categorize each of the Operators into its respective return to scale (RTS) group using Non-Increasing Return to Scale model (NIRTSM) and suggest necessary policies that could improve the performance of the Operators.
The approach is based on variable return to scale assumption of DEA model framework (vDEA). Based on this, four variables were considered; two inputs, petroleum reserves in million BOE and Number of producing Wells and two outputs; total annual oil production in million BBLS, total annual gas production in million SCF. vDEA model assumes operational variability due to constraints such as: projects financing, competition, government policies, operational terrains, etc. A dual linear programming model (DLPM) is formulated and a Microsoft excel solver aided with a visual basic application (VBA) is used to generate RTE for each Operator simultaneously. RTS indexes are estimated using output oriented NIRTSM. The relationship between the two models' results is used to categorize each Operator into different production scale group.
The empirical results reveal a decreasing trend in the Operators' RTEs from 2010 to 2016. Twelve (12) of the Operators were operating on efficient production frontier within the period under investigation. Based on this, Operators were classified into three different production scale group. Eight (8 or 25%) were operating on decreasing return to scale (DRTS), Six (6 or 19%) on constant return to scale (CRTS) while the remaining Eighteen (18 or 56%) were on increasing return to scale (IRTS). Significantly, it is discovered that more than 80% of the JV Operators have been operating on DRTS.
Consequently, the paper provides information on RTEs of the 32 active upstream Operators in Nigeria considering more realistic assumptions as recommended, (
Experimental design method is very useful in green field development. It helps to understand the impact of uncertainties on ultimate recovery, and hence, gives guidance on business decision making. Outputs from the process (Pareto/Tornado charts, selected models) are good exhibits for use in the uncertainty management plan, which also drives data acquisition and work plans for future stages/phases of the project.
This work shares some lessons from using experimental design for development planning of two Non-Associated Gas (NAG) condensate reservoirs. It demonstrates the importance of selecting appropriate design for proxy generation/Monte Carlo simulation runs – and eventual model selection.
This paper has two case studies: (i) a gas condensate Reservoir "A," with total of 20 parameters (9 discrete and 11 continuous variables), and (ii) another gas condensate Reservoir "B", which has 22 parameters.
Folded Plackett-Burman (FPB) design was first used in both cases. However, owing to limited number of runs, only linear proxies could be created. This did not meet the objective of the process, because it does not allow for generating interaction terms among parameters. The FPB runs were therefore used only as screening studies, while 3-level D-Optimal designs were subsequently used for response surface model (proxy) generation.
Four sensitivities were done on Reservoir "A": (i) 20 parameters with 300 D-Optimal runs; (ii) 14 parameters with 300 D-Optimal runs (after screening out less impacting parameters on objective functions); (iii) 20 parameters with 500 D-Optimal runs; and (iv) sensitivity (i), but with additional fresh 200 D-Optimal runs. The two sensitivities done on Reservoir "B" are: (i) 22 parameters with 200 runs; and (ii) 15 parameters with 300 runs. It was observed that only sensitivities (ii) and (iii) for Reservoir "A", and sensitivity (ii) for Reservoir "B" yielded meaningful proxies.
In conclusion, using Folded Plackett-Burman (FPB) designs alone in cases with many variables, as shown in this work, may not lead to meaningful proxies (especially, when there are interactions among parameters) because it is restricted to only linear proxies. Also, it is important to have adequate number of 3-level design (D-Optimal) runs for both process efficiency and proxy generation. Too few runs result in unreliable proxies, whereas too many runs take more time/computing resources. In addition, carrying large number of variables into the 3-level design stage requires more runs and also leads to more cumbersome proxies.
Awasthi, Amit (Shell Nigeria Exploration and Production Company) | Ikwueze, Obinna (Shell Nigeria Exploration and Production Company) | Itua, Osazua J. (Shell Nigeria Exploration and Production Company) | Tendo, Fidelis (Shell Nigeria Exploration and Production Company) | Osayande, Francesca (Shell Nigeria Exploration and Production Company)
For reservoirs that are not fully appraised, fluid type and resource volumes above Shallowest Known Oil (SKO) or Deepest Known Oil (DKO) are often unknown. This paper discusses the workflow adapted to predict the fluid type above the SKO with reasonable certainty. This enables to book SPE compliant resource volume (SCRV) above SKO to be proposed for booking and up dip wells to be justified.
As part of SCRV estimation for Field-X to book hydrocarbon volumes above the SKO, the oil properties, reservoir pressure data and seismic information were integrated utilizing the limitations and uncertainties around each data for the candidate reservoirs. The fluid properties (specifically bubble point pressure) are the most essential element in this analysis, which sometimes are not available or not of sufficient quality. This article will discuss the methodology and workflow adopted to use the existing limited information with their corresponding uncertainties to circumvent these limitations.
This methodology was demonstrated as a reliable technology supporting SCRV for Field X, resulting in the increase of ~ 25-30 % of the oil SCRV. Furthermore it has potential to be applied to other fields with similar circumstances.
The development of depleted oil reservoirs for simultaneous gas injection for underground natural gas storage and enhanced oil recovery in Nigeria was evaluated using IZ-2, a depleted oil reservoir located South Eastern Nigeria. The geological information and the production history of the reservoir were applied in estimating the storage capacity at any given pressure. The plot of well flowing pressure (Pwf) against flow rate (Q), shows the deliverability of the reservoir at various pressures. The verification of inventory and assurance of deliverability were evaluated for the reservoir. The results of the estimated properties indicated that IZ-2 is a good candidate for conversion into storage reservoir and enhanced oil recovery. A reservoir engineering simulator (ECLIPSE) was used to forecast oil production from the reservoir without gas storage and with gas storage in the reservoir respectively. The results of the estimated parameters shows that the storage capacity is; 437Tscf and deliverability: 46.4MMscf/d. The production forecast indicated that the cumulative oil production was higher when the reservoir contain gas than when there is no gas. The novelty of this research work is the performance analysis for the determination of the suitability of the reservoir for gas storage and also the recommendation of gas storage in the reservoir based on the analyses conducted for simultaneous gas storage and enhanced oil recovery in Nigeria.
Bello, Opeyemi (Institute of Petroleum Engineering, Clausthal University of Technology) | Teodoriu, Catalin (Mewbourne School of Petroleum and Geological Engineering, University of Oklahoma) | Yaqoob, Tanveer (Institute of Petroleum Engineering, Clausthal University of Technology) | Oppelt, Joachim (Institute of Petroleum Engineering, Clausthal University of Technology) | Holzmann, Javier (Institute of Petroleum Engineering, Clausthal University of Technology) | Obiwanne, Alisigwe (Institute of Petroleum Engineering, Clausthal University of Technology)
Artificial Intelligence (AI) has found extensive usage in simplifying complex decision-making procedures in practically every competitive market field, and oil and gas upstream industry is no exception to it. AI involves the use of sophisticated networking tools and algorithms in solving multifaceted problems in a way that imitates human intellect, with the aim of enabling computers and machines to execute tasks that could earlier be carried out only through demanding human brainstorming. Unlike other simpler computational automations, AI enables the designed tools to "learn" through repeated operation, thereby continuously refining the computing capabilities as more data is fed into the system.
Over the years, AI has led to significant designing and computation optimizations in the global Petroleum Exploration and Production (E&P) industry, and its applications have only continued to grow with the advent of modern drilling and production technologies. Tools such as Artificial Neural Networks (ANN), Generic Algorithms, Support Vector Machines and Fuzzy Logic have a historic connection with the E & P industry for more than 16 years now, with the first application dated in 1989 for development of an intelligent reservoir simulator interface, and for well-log interpretation and drill bit diagnosis through neural networks. Devices and softwares with basis from the above mentioned AI tools have been proposed to abridge the technology gaps hindering automated execution and monitoring of key reservoir simulation, drilling and completion procedures including seismic pattern recognition, reservoir characterisation and history matching, permeability and porosity prediction, PVT analysis, drill bits diagnosis, overtime well pressure-drop estimation, well production optimization, well performance projection, well / field portfolio management and quick, logical decision making in critical and expensive drilling operations.
The paper reviews and analyzes this successful integration of AI techniques as the missing piece of the puzzle in many reservoir, drilling and production aspects. It provides an update on the level of AI involvement in service operations and the application trends in the industry. A summary of various research papers and reports associated with AI usage in the upstream industry as well as its limitations has been presented.
Oil and Gas investments are inherently risky, especially in upstream exploration where technical risk is predominant. Because of the large upfront expenditure required for these projects, it is imperative that investors in the business are well informed of the risk to which their capital is exposed. However, most commonly used performance measures focus on one aspect of what is a multidimensional problem. While expected value gives an indication of the amount of value created by the investment, the standard deviation gives an insight into the distribution of expected returns. The Performance Index combines these two insights into a single measure, value created per unit of variability (the standard deviation), which is a significant improvement over the one dimensional measures.
However, the Performance Index, PI is based on total variance and does not reflect the uncertainty structure of the risky investment. PI does not distinguish between upside and downside variances and can sometimes give a lower ranking to a prospect with large upside variance (High gain situation) leading to the Paradox of aversion to incremental reward (PAIR). We show this flaw in PI in an expected value maximization context as well as in an expected utility maximization context.
Semi-variance analysis gives an insight into the uncertainty structure of a risky investment. We use semi-variance analysis to propose a modification to PI to eliminate the problem of PAIR. Total variance is decomposed into upside and downside variances and the modified PI estimated based on downside variance alone. In decomposing the variance, we recommend using a threshold value to avoid the problem of a "shifting mean". We demonstrate through two examples (1) that though conventional PI may correctly rank risky prospects, non-recognition of the uncertainty structure does not accurately reflect the risk characteristics of an investment, and through a second example, a well drilling situation in which conventional and the modified PI lead to different ranking and investment decision recommendations.
Shale oil reservoirs are characterized by low porosity and very low to extremely lowpermeability which makes the oil content difficult to produce at economic rates without being enhanced. The current production optimization technique in the oil industry uses a combination of horizontal well and fracking. However, there is a possibility of linearly increasing inflow with high liquid loading at the horizontal well heel which can seriously choke offtake of the fluid.
This paper, therefore presents a holistic approach based on wellbore hydraulic optimization and systematic inflow control to maximize deliverability from shale oil reservoirs. Wellbore hydraulic optimization was achieved through rheological characterization of viscous liquid-rich shale at different nodes whilst systematic inflow control was achieved through selective inflow control into the wellbore in order to distribute fluid influx uniformly and avoid early breakthrough of unwanted fluids.
The results presented prove that using the new modelling approach offers better deliverability overall compared to the current approach known.
Hitherto, there has not been a holistic characterization of fluid rheology cum systematic inflow control, this work offers this novel approach as a contribution to the existing methods of optimizing production from shale reservoirs.
David, Olayinka (Shell Petroleum Development Company) | Laoye, Abiodun (Shell Petroleum Development Company) | Odegbesan, Seyi (Shell Petroleum Development Company) | Isimbabi, Onoze (Shell Petroleum Development Company) | Obeahon, Percy (Shell Petroleum Development Company)
Millions of dollars are spent across the oil and gas industry on data gathering activities with a view to reducing subsurface uncertainties towards optimizing reservoir development and management. However, suboptimal attention is often paid to assessing the value of the information (VoI) during data acquisition requirement planning and before requesting for such information. The capital intensiveness of the industry and emerging low oil price regime has necessitated scrutiny on every dollar spent on data gathering in the current business terrain.
The application of the VoI concept in the oil & gas industry provides a predictive, analytic and quantitative framework for decisions and justifications for data gathering activities including but not limited to log data acquisition; downhole fluid sampling; subsurface diagnostic tests; core data acquisition; appraisal drilling and seismic acquisitions. Value of Information in simple terms is described as the amount a decision maker should be willing to pay for a piece of information. It is evaluated as the difference between the future value of a project given the availability of particular information versus its value without it.
This paper demonstrates the methodology and application of VoI analysis to support a key decision on whether or not to drill an appraisal leg of a well to test for fluid contact and possible presence of an oil rim in a reservoir prior to initial gas development. The major uncertainty in this study is the evaluation of the hydrocarbon extent in a gas reservoir with a gas-down-to (GDT). To evaluate the options, a VoI analysis was carried out by integrating results of different data sources; well log data; formation pressures; seismic data and analogue information. Integration of the different data was used to arrive at different subsurface realizations which fed into 3D static and dynamic simulation models. The modeling result for the different scenarios was used as input for economics in the VoI analysis.
Using the multiscenario analysis, the range of oil rim thickness proved to be non-commercial and the VoI analysis showed that drilling an appraisal will result in a negative value of appraisal giving the estimated VoI and cost of drilling an appraisal well. The analysis has led to significant cost saving of about $8million which is the appraisal cost of an earlier planned appraisal/development well. The analysis also helped to challenge the pre-existing appraisal paradigm and provided a robust basis for a commercial decision without compromising on regulator standards and industry best practices.
With the recent decline in price of crude, more cost effective ways are explored to ensure that the production rates are sustained and increased. The need for cost effective subsea well intervention has been discussed and documented over the last few years. The increasing number of installed subsea wells combined with the increasing age of subsea fields continues to drive demand for more efficient subsea well intervention.
Traditionally the accessibility to subsea wells is considered more difficult and represents a large cost compared to wells with direct platform access. Even minor jobs represent large expenses, leaving a gap between intervention frequency on subsea wells and wells with direct platform access. The average recovery rate for a subsea well is considerably lower than that of a comparable surface well due to the relatively more complicated well intervention and maintenance issues. Using heavy and traditional rigs for subsea intervention is costly and time consuming due to the high day-rates and mobilization aspects. The base costs are therefore considerable higher as compared to surface well intervention where tools can be deployed directly through the risers from the production unit.
At Addax Petroleum, Nigeria, the re-entry and intervention of existing poor producing subsea wells was identified as a cost effective method to maintaining production in the reasonably high cost oil subsea environment. However the major challenge with subsea intervention is the uncertainty that surrounds the planning and operations which grows exponentially with the age of the well. As factors like corrosion, and well integrity (cement and casing) all become critical in the successful delivery of the well. The availability of required service equipment and modes of operation also become very critical especially for old and obsolete Subsea equipment. There is always a significant extra cost spent if any of the identified or unidentified risks manifest.
In 2014 Addax Petroleum embarked on a successful re-entry campaign as part of the second phase of its Okwori Field Development Plan. This paper highlights on the lessons learned from the campaign, discussing the challenges, planning, and execution phases of the successful well re-entry campaign in deep water operations.