Facilities decisions are often disconnected from anticipated reservoir performance. A frequent result of this disconnect is operating reservoirs in a sub-optimum manner to protect surface facilities that have inadequate strength. This paper will review the facilities decisions that have been made in several Coalbed Methane (CBM) and Coal Seam Gas (CSG) fields around the world and discuss the reasons for and the impact of those decisions on the performance of the reservoirs. The source of the disconnect is that "everyone knows" that CBM and CSG fields "require very low pressures". With that assumption you then apply safety and procurement principles to come to a design. There is a stage in the production life of these fields where very low pressures are required for reasonable recovery levels, but that stage is typically reached 10-15 years after first production. Setting late-life surface facilities at first-production results in choking the reservoir for over a decade, setting compression many years before it is actually required, and less ultimate recovery as a percentage of original gas in place than would otherwise be achieved. These problems can be overcome by understanding the life cycle performance and risks of an unconventional reservoir and accepting that any tradeoff between facilities performance and reservoir performance must be biased in favor of optimizing reservoir performance in order to have acceptable economic results.
The production profile of an unconventional resource play typically has a very steep decline after attaining the peak production rate. Consequently, operators are focused increasingly on drilling the wells faster and getting them on production quicker in order to improve early cash flows. This can sometimes come at the expense of gathering potentially useful data that may help improve reservoir characterization. This practice raises a couple of vital questions that traditional methods based on either deterministic or one-variable-at-a-time (OVAT) methods struggle to answer coherently. Examples of such questions include: How much impact does drilling and completion speed have on the overall project economic measures? What is the relative significance of all the key factors affecting the project economic measures? Is it worth taking the time to acquire data that can ultimately reduce the uncertainty in the reservoir characterization and production performance? What are the approximate models for the response variables?
How much impact does drilling and completion speed have on the overall project economic measures?
What is the relative significance of all the key factors affecting the project economic measures?
Is it worth taking the time to acquire data that can ultimately reduce the uncertainty in the reservoir characterization and production performance?
What are the approximate models for the response variables?
A new workflow based on experimental design concepts has been developed to answer the above questions and tested using an unconventional shale oil resource. In this case study, we used the D-Optimal design table and evaluated the impact of a number of diverse factors ranging from speed of drilling, put on production (POP) time, production ramp-up, expenditure, product price to production type curve on the project economic measures. The results, for example, show that while a reduction in drilling and completion times may affect early production metrics, some other factors like production type curve have much more impact on the project's net present value (NPV) and Discounted Profitability Index (DPI).
The profitability of a well in an unconventional play is significantly influenced by its completion. It is widely understood that tighter rock needs more stimulation to economically recover hydrocarbons. However, how does one know if a well is being over-stimulated (fracture area created does not justify cost incurred) or under-stimulated (lost potential/profitability in productivity from a well's limited contact to the formation)?
The objective of this paper is to develop and demonstrate an efficient workflow that will help stakeholders make better decisions in the area of completion planning. The workflow utilizes information from fracture modeling, production data analysis, and project economics to quantify the relationship between the key input parameters of the well completion (e.g. pumping rate, proppant and fluid pumped) and expected profitability expressed in net present value (NPV) terms. As a secondary objective, the case study demonstrates that a probabilistic approach (Monte Carlo Simulation) can be used to efficiently arrive at a consistent conclusion to the primary workflow. The output of the probabilistic model includes P90/P50/P10 production and net cash-flow forecasts, from which distributions of NPV can be obtained.
This workflow is intended to help engineers compare profitability among different completion options. A shale gas field example is presented to illustrate the methodology.
Technical professionals are often asked to estimate "ranges" for uncertain quantities. It is important that they distinguish whether they are being asked for variability ranges or uncertainty ranges. Likewise, it is important for modelers to know if they are building models of variability or uncertainty, and their relationship, if any.
We discuss and clarify the distinction between uncertainty and variability through strict definition, illustrative analogy and numerical examples. Uncertainty means we do not know the value (or outcome) of some quantity, eg the average porosity of a specific reservoir (or the porosity of a core-sized piece of rock at some point within the reservoir). Variability refers to the multiple values a quantity has at different locations, times or instances – eg the average porosities of a collection of different reservoirs (or the range of core-plugs porosities at different locations within a specific reservoir).
Uncertianty is quantified by a probability distribution which depends upon our state of
We show there is no objectively ‘right’ probability distribution for quantifying the uncertainty of an unknown event – it can only be ‘right’ in that it is consistent with the assessor's information. Thus, different people (or teams or companies) can legitimately hold different probabilities for the same event. Only in very restrictive, arguably unrealistic, situations can we choose to use a frequency distribution derived from variability data as a probability distribution to represent our uncertainty in an event's outcome.
Our experience as educators of students and oil & gas industry personnel suggests that significant confusion exists in their understanding of the distinction between variability and uncertainty. This paper thus provides a resource for technical professionals and teachers to clarify the distinction between the two, or to correct it where it has been wrongly taught, and thereby help to improve decision-making.
Ekweanua, U. Emmanuel (University of Oklahoma) | Sharma, Suresh C. (University of Oklahoma) | Wu, Xingru (University of Oklahoma) | Zhu, Zhen (University of Oklahoma) | Callard, Jeffrey G. (University of Oklahoma)
In the United States, hydrocarbon in unconventional resources such as shale gas has been dramatically changing the fossil energy prospect and transforming the energy consumption structure. Therefore, it is imperative to study how this trend has impacted the U.S. natural gas import, export and the domestic gas price. To understand the relationships, Neural Network would be used to model these variables (gas production, price, import and export) with the ultimate goal of understanding the gas price determination. The key input parameters for the Network are gas production, import and export data and the resulting output of the Network would be the gas price i.e. how well this inputs influence gas price and there magnitude of impact would be ranked in this study. Impact of Weather would be looked into as well but it is not part of the Network inputs. Data from Energy Information Administration (EIA) of the U.S. Department of Energy will be utilized in this study.
This work is motivated by the recent surging interest in converting existing gas import terminals to exporting terminals due to increase in gas production as a result of major technological advancement in getting formerly untapped gas out of the ground. The changes in the gas industry trend have prompted the government to consider policy changes as well. Our study will enable us to draw some policy implications regarding the U.S. energy policy.
In the oil and gas industry, the term "business planning" brings visions of late nights, additional meetings, and countless hours spent collecting and reconciling large amounts of data. This negative connotation has been reinforced over the years as companies struggle to pull together the information they need to create realistic and achievable plans and to forecast future development to guide the growth of their business.
It is unfortunate that business planning has such a bad reputation as it is critical to the success of any company in any industry. In business planning, the goals are simply to select the best projects from a portfolio of opportunities to maximize the return on investment, while being able to effectively communicate the details of how the different scenarios were created to provide confidence in the decision to invest.
This paper describes a case study in which one of Occidental Oil and Gas Corporation (Oxy BU) business units improved a few key elements in their business planning process which helped them create a more realistic, higher return plan, faster.
The Oxy BU saw the potential rewards that improvements to their planning process could generate by improving their planning efficiency, reducing errors, and breaking out of the same painful cycle they had experienced in previous years. In this paper, we present the results of the improved workflow, focusing on those which were seen to have the largest impact on results including:
Data consistency: Consistent capture and reporting of data across all teams Minimize bias: P50 curves developed, compared, and reviewed across teams Risk analysis: Improved ability to account for granular risk factors across plan Type well scheduling: Increased ability to rapidly build, explore, and turn-around new scenarios Opportunity selection: Increased value of the portfolio Visibility of the plan: Increased communication and buy-in from teams Time to market data: More realistic view of cash flows and activities Resource balancing: Increased confidence in ability to execute the plan
Using this new approach, the Oxy BU planning team was able to turn around three different investment scenarios, numerous development strategies, and create a five-year, long-range plan that the entire management team could present and stand behind.
Economic forecast for assessing reservoir performance is a strong function of geological modeling. Complexity of geological modeling may vary from semivariogram-based models to multiple-point simulation technique. Semivariogram-based stochastic simulation techniques are less complex than multiple-point simulation technique in terms of the amount and type of reservoir information needed to generate the porosity and permeability maps.
This paper assesses economic implication of using geological models of varying levels of complexity. For this we compared the uncertainty in reservoir's long term economic performance obtained by using geological models with varying levels of complexity. Reservoir economic performance is assessed using both the real options valuation (ROV) analysis, a probabilistic approach, and discounted cash flow (DCF) based approach.
The results suggest that it may be appropriate to use simpler geological models for the forecast of volumetric flow rate uncertainty. We see that the economics in terms of ROV obtained from a simple geological model is not significantly different from that of a complex geological model. Similar results also hold true with DCF analysis.
This study could help answer the question of how much detail in reservoir models are necessary if the end objective is to obtain realistic assessment of net economic risk (which would be used to make correct decisions)?
Energy, coming in its great majority from oil and gas has become a strategic factor in global geopolitics. It is key to national power and a major requirement for economic growth. Energy consumption has become the most palpable national characteristic that separates rich from poor countries. The United States, the richest nation in the "room" is also the most intense user of energy per capita.
There is a substantial imbalance in the location of energy producers and consumers, an imbalance that has precipitated world conflicts and one that will likely cause future upheavals. There is huge activity by China buying energy resources all around the world. Russia's recent ascendancy in the energy world has been an important counterbalance to the power of OPEC. However, recent events surrounding Russia's energy industry have exposed fissures within the economic and political makeup of the country.
The United States Shale Revolution has, and will, bring market distortions throughout the entire nation and to many others such as the energy-starving, Southeast Asia markets of China and Japan. The recent removal of restrictions on LNG exports by the American government means that new forces will be implemented on both demand and supply of those markets. I believe that the globalization of gas trade will make prices of natural gas to converge and thus we will witness a more "unified" price regime in the not-too-distant future. Predictions of the future supply of petroleum have typically been far less accurate than predictions of demand. Flawed predictions have caused public bewilderment, distrust and, more importantly, government inaction or poorly conceived reactions. The cause of every energy crisis, like oil climbing to $150 per barrel in 2008 before dropping to $40, is above the ground geopolitics and never behind the valve issues.
This paper applies basic economic principles to assess the effects of present-day geopolitical forces on energy markets, particularly those of natural gas, around the globe arriving on a number of interesting conclusions. Topics touched include Chinese urbanization, United States LNG exports, Keystone XL Pipeline, Russian nationalization over its energy industry and its relationship with former Soviet Union countries.
A company's performance in a lease sale can have serious implications for future growth and sustained value. Therefore it makes sense to approach each sale armed with the most powerful tools available. Surprisingly few firms go beyond spreadsheet analysis when it comes to such an important event, most likely because it is the most familiar method. Perhaps this is why we see bid levels with a significant amount of spread, representing "money left on the table" or skewed perspectives on the value of the block.
Firms that wish to move to the next level take a different view. They choose to engage their best talent in a
Building a bid strategy capability using simulation as a centerpiece is challenging, complex work, requiring key data sets and a mastery of the underlying technology simultaneously. That does not mean that best in class bid strategy is not attainable – it comes about with a focus on execution.
We will touch on many of the lessons we learned in working side by side with an aggressive lease sale bidder, and their journey from unsatisfactory auction performance to a place among the top tier of bidders: Use the talent you already have, but provide the team with an analytical system to translate knowledge into bidder intelligence. Build a serious blueprint of the bidding system before implementation Use the simulations to "steer" the bid strategy
Use the talent you already have, but provide the team with an analytical system to translate knowledge into bidder intelligence.
Build a serious blueprint of the bidding system before implementation
Use the simulations to "steer" the bid strategy
We will focus on the practical, actionable steps to build a better bid strategy through analytics, sustainable across a range of lease sales, both in the US and abroad. As of this writing, Mexico had recently announced plans to conduct auctions on offshore license blocks starting in 2015, and the US BOEM announced a major lease sale each in the Central and Eastern GoM regions scheduled for March 2014.
Oil and gas companies typically use discounted cash flow analysis in determination of the value of their oil and gas investments. "PV-10" is often accepted as the present worth of an oil and gas investment, but does a 10% discount rate represent the true cost of capital and include project-specific risk premium, or is it simply a rule-of-thumb? This original paper will discuss the factors that go into and how to calculate the weighted-average cost-of-capital (WACC) as the starting point to calculate the appropriate discount rate for their investment.