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Collaborating Authors
project valuation
The Implementation of Big Data and Machine Learning (ML) processes as part of the Fourth Industrial Revolution (4IR) are already having a significant impact in the Oil & Gas Industry. Where direct access of streaming data to Big Data Systems is possible, the ML Algorithms are able to improve operational efficiency with a corresponding significant reduction in costs. Today the identification and reduction of both Invisible Lost Time (ILT) and Non-Productive Time (NPT) is even more critical than it has ever been in the past as many projects strive to maximise the rate of return on investments. However, many remote sites are linked to Big Data Systems through a lower bandwidth satellite system, resulting in loss of data granularity and time lags that make the response times of analytical solutions based upon ML processes too slow to be of any immediate value to the well construction process. As a consequence, it is vital to move the ML Algorithms as close to the source of the data as possible.
Develop simple examples of project metrics using spreadsheet monte carlo simulations for stochastic analysis. Build financial models to show project cash flow streams for both capital investment and income cash flow and calculate key metrics such as profit/investment ratio, profit, payout period, net present value, internal rate of return and expected monetary value. Where there are uncertainties in the variables, use standard software to prepare sensitivity and stochastic analysis to show the potential variations in the metrics because of these uncertainties. The objectives of this course are to introduce the learner to: analyze the economics of a prospect under conditions of uncertainty, build deterministic and stochastic models, build E&P financial models to demonstrate project cash flow, calculate project economic metrics including profit/investment ratios, profit, payout period, net present value, internal rate of return and expected monetary value, and prepare sensitivity and stochastic analysis to measure potential variations in project metrics.
Project Economics and Risk Analysis presents the process energy companies follow to analyze the economic attractiveness of capital investments, whether it is an investment in oil and gas exploration, a new power plant or LNG facility, a new refinery, or simply the capital expansion of an existing facility. The analysis includes three basic ways to incorporate uncertainty into the calculation of project metrics: Deterministic estimates, Spider and Tornado Diagrams, and Monte Carlo Simulation.
- Management > Strategic Planning and Management (0.80)
- Facilities Design, Construction and Operation > Natural Gas Conversion and Storage > Liquified natural gas (LNG) (0.80)
- Management > Risk Management and Decision-Making > Risk, uncertainty, and risk assessment (0.74)
- Management > Asset and Portfolio Management > Project economics/valuation (0.74)
UK-based energy services firm Wood is set to design the platform topsides for Trion, a greenfield development offshore Mexico from Australia's Woodside Energy. Wood secured the contract from engineering, procurement, and construction (EPC) provider HD Hyundai Heavy Industries (HHI) for the detailed engineering of the topside facilities for the Trion floating production unit (FPU) in Mexican waters of the Gulf of Mexico. "We are pleased to have been selected as the topsides engineering provider for Trion by Woodside Energy and the project's EPC contractor, HHI. Wood's innovative design process on the pre-FEED and FEED work positioned us well for the detailed engineering scope on Trion," said John Day, president of oil, gas, and power at Wood. "Applying a practical approach to decarbonization in the design process has been an important part of this project while ensuring safety and quality. Our team has a proven history with Woodside, having worked together for 2 decades, and our experience designing and delivering solutions for Trion will improve productivity, reduce emissions, and maximize the return on investment for our client," he continued.
Rejuvenating Profitability and Performance of Aging Gas Field through Multi-Pronged Strategy: System De-Bottlenecking, Well Optimization and Opex Management
Awase, Azar (United Energy Pakistan Limited, Karachi, Sindh, Pakistan) | Memon, Sikandar Ali (United Energy Pakistan Limited, Karachi, Sindh, Pakistan) | Zafar, Jahangeer (United Energy Pakistan Limited, Karachi, Sindh, Pakistan) | Khan, Zaeem Hasan (United Energy Pakistan Limited, Karachi, Sindh, Pakistan) | Noman, Muhammad (United Energy Pakistan Limited, Karachi, Sindh, Pakistan) | Ejaz, Muhtashim (United Energy Pakistan Limited, Karachi, Sindh, Pakistan) | Ali, Mushtaque (United Energy Pakistan Limited, Karachi, Sindh, Pakistan) | Amjad, Muhammad (Prime International Oil and Gas Company, Karachi, Sindh, Pakistan) | Sirati, Muhammad Attar (Pakistan Petroleum Limited, Karachi, Sindh, Pakistan)
Abstract This paper presents a success case of optimizing a challenging mature gas field, producing at tail-end, with low reservoir pressure and increasing water gas ratio trend, complex surface network, and high operating cost. The situation demanded cost efficiency, novel strategies to optimize production and prudent decisions to safeguard reserves and sustain production. S-Field was put on production in 2003 and peaked production of 460 MMscfd in 2007. In 2021, reservoir pressure dropped from 5400 to 300 psi, wells became critical and front-end compressors approached lower limits. Additionally, field was close to economic threshold, owing to late life conditions and high Opex. An integrated approach was taken to mitigate the challenges. A network modeling exercise with multiple sensitivities were performed to simulate the application of different deliq techniques (soap sticks, velocity string, CSI) and operating field with expanded compressor envelop. Subsequently, the strategy was implemented in phases to optimize the field. Simulations performed for no further activity case with prevailing production technical limit suggested additional recoveries of only 4 Bscf for one year remaining. The simulated optimization cases predicted additional recoveries of around 20 Bscf with field life extension by 5 years. On top of production and reserves, the strategy envisaged significant improvement in field profitability by saving fuel gas (โผ1 Bscf annual) and maintenance cost (2.5 $MM annual). The capital investment for the project was $10MM, with estimated project NPV of $17MM. The project implemented in Oct 2021 with optimized compression scheme, smaller tubing size, velocity strings, and cost-effective foam injection. The actual field delivery was vigilantly monitored and till day additional volumes of 8 Bscf have been materialized and field is demonstrating very stable performance, closely matching the premise. A novel aspect is that brown fields may be rejuvenated by expanding techno-commercial boundaries besides application of optimization techniques. At the stage where many operators would consider abandoning the fields, results driven strategy coupled with multidisciplinary inputs resulted in higher profits and additional recovery.
- North America > United States > Texas > Kleberg County (0.24)
- North America > United States > Texas > Chambers County (0.24)
Abstract The paper presents a novel technique for probabilistic risk modelling to evaluate the feasibility of an exploration and development offshore project. Decisions regarding the feasibility of an offshore project using deterministic methods do not capture the complete range of risks and prospects are ranked based on the net present value with discount factors determined by the company's cost of capital. To improve the decision making strategy, especially in a new exploration asset, a new approach has been worked out in this paper. The results are supported and modelled using a global database of economic parameters related to Capital Expenditure (CAPEX), Drilling Expenditure (DRILLEX), Operating Expenditure (OPEX), development strategy, recoverable volumes which significantly improves the analyst's confidence when ranking new/wildcat exploration prospects. The model integrates multiple offshore development scenarios and accounts for sub-surface uncertainties based on the resource estimates, fluid properties, well productivity. Further, volatility of oil prices, uncertainty range of CAPEX and OPEX estimations are considered to model the market fluctuations and project management risks. The technique is implemented using an analysis performed in three segments. The first part incorporates the creation of a global benchmarking model of more than around 1000 assets that provides the correlation parameters between the variables used in the probability analysis. The second part describes the estimation of a probability distribution model for each variable using an optimizer. This is used as an input to a monte-Carlo simulation model that is calibrated using the global benchmarks and uses the Latin Hypercube sampling method. Finally, the third part provides an offshore field case study to describe the risk modelling and decision-making parameters that are provided as the output. The results of the probabilistic economic analyses include risk profiling based on key performance indicators such as Net Present Value (NPV), and the internal rate of return (IRR). Further, we also present the process to interpret the results with sensitivities related to breakeven oil price, maximum feasible increments in CAPEX, OPEX and the minimum recoverable resource. The results are compared to a global analogue to access the validity and development potential of a project. The paper provides an additional tool for critical management decisions related to exploration and development strategy of a company. The current technology and literature focus on probabilistic modelling of techno-economic variables based on a mean resource volume. However, the current technique fully integrates uncertainties associated with the sub-surface and the other techno-economic variables. This probabilistic technique has all the major advantages of a deterministic approach like it can be quickly implemented, does not have a black box approach and the results are reproducible. It is simple yet robust enough to be quickly deployed on multiple projects in a company.
Statistical Aggregation - A Simple Tool in the E&P Industry for Estimating the Economic Value of a Complex Project
Khan, I. (Pakistan Petroleum Limited, Karachi, Pakistan) | Aslam, M. A. (Pakistan Petroleum Limited, Karachi, Pakistan) | Hasan, A. (Pakistan Petroleum Limited, Karachi, Pakistan) | Palekar, A. H. (Pakistan Petroleum Limited, Karachi, Pakistan)
Economic evaluation is an important decision-making tool for drilling an exploratory well. The paper is focused on a well drilled in the Mesopotamian basin in Iraq. Before its drilling, it needs to be passed through the technical and commercial gates. The prospect contains multiple (five) reservoir targets. To properly evaluate its technical and commercial potential, it was prudent to give weightage to each reservoir. To assess the Expected Monetary Value (EMV) of the prospect, a total of 32 economic cases needed to be built in the decision tree which makes the working & decision complicated for the management. It may be noted that in Iraq most companies operate under Service Contracts which itself is not common in the industry to assess commerciality. To cater for the above, a statistical aggregation methodology was adopted in which hydrocarbon volumes (HCIIP) and the chance of success (CoS) of five individual reservoirs were transformed into one number. Accordingly, the economics was performed on this number and the corresponding prospect EMV is calculated. The results were presented to the technical and commercial gates and were accepted by the management and approval was granted. It may be noted that results from statistical aggregation were rechecked by performing the economics on 32 cases and both results were found comparable. Therefore, this technique was used to quickly assess the EMV of a complex project for decision making and the same technique can be helpful in commercial decision-making of any scale. The fit-for-purpose approach was utilized in view of the complexity of the project. This work will serve as a reference for statistical aggregation in the oil industry, to evaluate the commerciality of the project with multiple reservoirs. The findings can also be used to gauge the feasibility of any farm-in block where multiple leads and prospects are available, and a quick decision is required to ascertain its value.
Improving the Value: A Case Study of Integrated Asset Modelling of a Giant Contaminated Gas Field
Farooq, U. (Weatherford) | Ashraf, Z. (Oil & Gas Development Company Limited, Pakistan) | Saqib, C. M. (Oil & Gas Development Company Limited, Pakistan) | Ali, M. (Oil & Gas Development Company Limited, Pakistan)
Abstract The pace of natural gas discoveries globally is growing, but comes with challenges from entrained acid gases, primarily carbon dioxide and hydrogen sulfide. The development of these sour gas fields becomes more complex when there is varying gas composition along the field. Considerations are gas calorific values and corrosive fluids which are detrimental to pipelines and facilities. This paper presents a case study of an integrated asset modeling for three different compartments of varying heating values sharing a joint surface production facility. The aim is to optimize future sale gas contracts, avoid operational problems, and appraise water production effects. The field has three hydraulically isolated compartments, each with unique compositions, with more than 6 Tscf reserves. The gas heating values range from 300 to 700 BTU/CUFT, with CO2 concentrations from 9% to 45%. Field economics requires all three compartments to be produced through a single production platform. This poses many operational challenges, e.g., production strategy to meet sale gas heating values, and pressure constraints to balance gas and water production. A major consideration is the mixing ratio of gas from each compartment. The combined gas composition and produced water will govern acidization effects in the surface facilities, which would need to be minimized. After geological and geophysical modelling, historical production data is matched. Next step is network modeling of surface facilities. The final piece is an economic analysis focused on costs of production, production improvement/remediation costs, and gas sales. The result is the ability to maximize the rate of return on investment while keeping optimized production rates that meet the contractual requirement. Final outputs include gas reserve estimates and the optimized development scheme. The proposed scheme considers workovers, infill wells and gas compression to increase the estimated ultimate recovery and prolong field production life. The final integrated model improved the understanding of the interface between all the components of the system (i.e., reservoir, wellbore, surface, and facility). The model was validated when field production exceeded the maximum historical gas rate and fulfilled the high energy demand requirement. As expected, the field data closely matched the model predictions. The model considered the difficulty in producing the required energy rate and delivery pressure due to bottlenecks in the pipeline and predicted the need for compression due to rising water-gas ratio. The ability to model complex reservoirs and surface production equipment in a single platform is a powerful tool for reservoir management. By carefully modelling the entire system and forecasting, the model transitions from a simple analysis tool to a test bench for altering the production strategy without impacting the actual physical assets. This allows the operator to minimize the cost of ownership and maximize return on investment.
- South America (0.93)
- Africa (0.68)
- North America > United States > Texas (0.46)
- North America > United States > Wyoming > Powder River Basin > Hartzog Draw Field > Shannon Formation (0.99)
- North America > United States > Wyoming > Powder River Basin > Hartzog Draw Field > Cody Formation (0.99)
- North America > United States > California > Santa Barbara Field (0.99)
- North America > Trinidad and Tobago > North Atlantic Ocean > Eastern Venezuela Basin (0.99)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Production and Well Operations > Well Operations and Optimization > Produced water management and control (1.00)
- (4 more...)
Innovative FDP Reframing to Reverse Unfavorable Project Economics of Underdeveloped Field via Modular Approach Utilizing Digital FDP Ecosystem
Isa, A. Bin Mohd (PETRONAS, Kuala Lumpur, Malaysia) | Tauphy, R. Rj Kamal (PETRONAS, Kuala Lumpur, Malaysia) | Mokhtar, Z. (PETRONAS, Kuala Lumpur, Malaysia) | Seah, Y. (Schlumberger, Kuala Lumpur, Malaysia) | Rahman, M. Abdul (Schlumberger, Kuala Lumpur, Malaysia)
Field S is the last oilfield with relatively significant 2C remaining volumes in Peninsular Malaysia of which its oil is at premium category. It is an under-developed brownfield with 12 years of production history, 145 km offshore Peninsular Malaysia. It consists of four main areas: North, North-West, Graben, and South, having an East-West elongated anticline field structure. The field holds relatively huge oil in place; however, its recovery factor is still less than 5% and full Field Development Plan (FDP) is advancing to fully uncover its true potential. In 2021, over 150 development scenarios were evaluated encompassing optimum recovery strategies, various facilities capacity limits, multiple schedules, and CAPEX phase sequencing.
A Digitized Tool for Well Candidate Selection for Matrix Acidizing in Sandstone Reservoir
Okologumw, W. C. (Department of Petroleum Engineering, Federal University of Petroleum Resources Effurun, Delta State, Nigeria) | Onyeoru, J. O. (Department of Petroleum Engineering, Federal University of Petroleum Resources Effurun, Delta State, Nigeria)
Abstract Matrix acidizing is a well-stimulation that has evolved and is still used to increase productivity when the productivity index drastically decreases and the production rate declines. A candidate well-stimulation selection method and software are suggested in this work. The process is based on technical, workover complexity, production decline curve analysis (for future forecasting), and economics since candidate selection must be rigorous. Production data from four onshore Niger Delta stimulation candidate wells were used to validate the software developed. R-factor, productivity index, and flow efficiency were the technical parameters used, and eleven (11) indicators were used for workover complexity evaluation. The future forecast was done using the production decline curve analysis and different economic indicators such as the Internal Rate of Return (IRR), the Net present value (NPV), and the Payback time. Profitability Index (PI) was used for analysis and make decisions. All wells analyzed in this study met the technical parameter criteria, making each well a potential candidate; hence, further studies can be conducted. From further reviews based on the author selection criteria carried out with regard to production trend (decline curve analysis) and economics, it was seen that WELL XX-01 was ranked first due to its shortest payback time of 2.0899 months and highest NPV of $23,636,983, WELL XX-03 was ranked next, having a payback of 2.2472 months and an NPV of $9,627,221, WELL XX-02 was ranked following having NPV of $7,260,917 and a payback of 2.2560 months and lastly, WELL XX-04 having a payback time of 2.3615 months and NPV of $6,777,548.