The process of upstream planning for the oil and gas industry has historically been a time consuming and inefficient process. The majority of effort is spent collecting data from multiple functional areas and organizational teams within an organization to form a holistic view of a company. By introducing an integrated solution that combines the opportunity catalog, economic analysis, and optimization into a single workflow, planners are experiencing efficiency gains, reduced time spent planning, and more accurate results. Through an analysis of the implementation of such a system at Chevron's Gulf of Mexico Business Unit (GOMBU), the challenges and benefits of implementing an integrated business planning approach as described will be demonstrated.
This paper will reflect on the systems, processes and workflows involved in the planning process at GOMBU both before and after deploying an integrated planning solution consisting of an opportunity catalog and linear optimization tool. This paper will also describe the challenges faced during the implementation and the benefits gained after the solution was deployed. Define "Vision" and reason for move to linear optimization Discuss state prior to deployment Evaluate technical challenges with current state Review state after deployment Examine results and gained benefits Proposed model for integrated planning framework Improved efficiency of portfolio optimization was significant, optimization time went from several weeks to days Portfolio optimization process was more fluid and responsive to changing business environments Secondary value generated to the entire organization in data integrity, consistency, management visibility into data, and traceability through opportunity maturation. Significant improvement in planning efficiency and accuracy achieved through implementation and execution of proposed architecture Integrated planning saves time, money and leads to significant value realization Learnings are applicable to any mid-to-large sized organization with any aspect of the planning process Combination of opportunity tracking and planning into a single workflow Combination of Opportunity Catalog, Economics Engine, and Linear Optimizer into a single portfolio analysis package Cross-functional visibility and input into the plan and communication of results
Define "Vision" and reason for move to linear optimization
Discuss state prior to deployment
Evaluate technical challenges with current state
Review state after deployment
Examine results and gained benefits
Proposed model for integrated planning framework
Improved efficiency of portfolio optimization was significant, optimization time went from several weeks to days
Portfolio optimization process was more fluid and responsive to changing business environments
Secondary value generated to the entire organization in data integrity, consistency, management visibility into data, and traceability through opportunity maturation.
Significant improvement in planning efficiency and accuracy achieved through implementation and execution of proposed architecture
Integrated planning saves time, money and leads to significant value realization
Learnings are applicable to any mid-to-large sized organization with any aspect of the planning process
Combination of opportunity tracking and planning into a single workflow
Combination of Opportunity Catalog, Economics Engine, and Linear Optimizer into a single portfolio analysis package
Cross-functional visibility and input into the plan and communication of results
Technical recoverable volumes estimation and reserves booking are two of the most significant and complex tasks in the petroleum industry, since figures add value to company assets and/or the economy of a country. Both processes, which not always are quite compatible because of project maturity or non-technical issues, usually embrace uncertainty due to lack of information and different criteria of reserves auditors.
Conventional incremental deterministic approach ruled by guidelines of the Petroleum Resources Management System (PRMS-SPE/WPC/AAPG/SPEE) and the Securities Exchange Commission framework is quite restrictive to book for proved reserves of a field in an early development phase or few years of production history; in addition, 2P and 3P estimations are closely linked to current geological interpretation, and are not flexible to account for the whole uncertainties inherent to project at that level of maturity.
A new probabilistic approach has been proposed to provide consistency and confidence to conventional 1P, 2P and 3P deterministic estimations in recent gas condensate fields. The incorporation of strong descriptive statistics concepts in traditional probabilistic volumetric models, such as central limit theorem, dependency between model inputs (Bayes’ theorem), aggregation of volumes, as well as, the use of the standard error of mean, have given consistency to volumetric model outputs. These probabilistic calculations, adjusted to SEC definitions for proved reserves, and SPE standards, for 2P and 3P reserves categories, as well as, contingent resources, allows dealing with uncertainty inherent to a new field on development phase.
Shale asset property consolidation, fractional acquisition of proven properties, and outright company acquisitions generate a growing demand for technical-economic appraisal support. Proven concept plays, attractive to larger oil and gas companies and investment firms, often have large amounts of geology and geophysical data, but may have limited production histories in as many as 100 wells within the regional play. Acquirers must address data management needs, secureaccess to experienced shale professionals, and meet timeconstraints to review, interpret, understand, and value the opportunity. The acquirer must validate the seller’s technical work and assessments, gather additional supporting technical data, develop an economic model from decline curve analyses (including CAPEX and OPEX insights), establish relevant transaction benchmarks, and qualitatively evaluate the operator’s capabilities.
Key components of the appraisal and valuation effort include setting technical and business metric thresholds, organizing key professionals’ work product and interactions, managing data inventory, validating existing analyses, filling in missing data with insights from analog plays, and consolidating results into actionable recommendations. The results of the appraisal and valuation exercise includes an itemization of positives, negatives, and residual uncertainties that the acquirer must incorporate into their portfolio evaluation process and estimate the value created, if the acquisition is consummated. This paper uses a recent successful acquisition to illustrate a workflow that compresses the time required to screen and evaluate a shale play acquisition in the US.
There is little commonality of fiscal incentives in the oil and gas sector. This is demonstrated in the North Sea, where rapidly changing market fundamentals had led to a range of fiscal measures aiming to incentivize the sector. Until very recently, rising cost of development against the backdrop of a maturing basin made the North Sea a less attractive place to invest in. Renewed interest in the North Sea seen in the last 5 years has been a direct result of the UK and Norwegian governments engaging with the stakeholders and introducing new policies which have the right incentives to extend basin life. However, in the UKCS, some would argue that these changes have come too late and investment and production levels have been already damaged. A brief review of the challenges in each country, the impact of fiscal incentives and to what extent they have been successful will form the main part of this paper.
Governments, regulatory agencies, the petroleum and mining industries, the financial community, international organizations and professional societies worked together under the umbrella of the United Nations (UN) to develop a principles-based resource classification system -- United Nations Framework Classification for Fossil Energy and Mineral Reserves and Resources ("UNFC-2009") -- that is suitable for all extractive activities, whether mined as a solid or produced as a fluid through wells. The system is designed to be fully compliant with the SPE/WPC/AAPG/SPEE Petroleum Resources Management System (PRMS).
A key benefit of UNFC-2009 is that it provides a platform for harmonizing (mapping) classification systems in use around the world, and for translating volumes both within and between commodity sectors. Another benefit of UNFC-2009 is that it uses numbers instead of words for classification. Numbers transcend communication barriers created by differences in language and cultural references.
The system is designed as a three-dimensional framework where volumes are classified based on: (1) economic and social viability, (2) project status and feasibility, and (3) geological knowledge; however, it can also be represented in a practical, two-dimensional, abbreviated version. A task force formed by a UN expert group, the Expert Group on Resource Classification (EGRC), is studying the application of UNFC-2009 to classify injection projects. Work is also underway to extend the utility of UNFC-2009 to atomic energy and renewable resources. If successful, it will allow, for the first time, classifications and comparisons of projects across most energy sectors.
The importance of improved guidance for the estimation and classification of "Resources other than Reserves" (abbreviated herein as ROTR) has recently been driven by the evolution of the petroleum industry away from maturing conventional accumulations towards unconventional accumulations such as petroleum in ultra-low permeability reservoirs (including shales) and heavy oil/bitumen resources. Commonly these unconventional opportunities involve newer, capital intensive technologies to exploit areally extensive deposits; accordingly, early-stage estimates of in-place petroleum quantities and potentially recoverable volumes may be very large, creating new and critical challenges to communicating clear and consistent information, including the associated uncertainties, relating to resource opportunities. To date, evaluators involved in estimation and reporting of ROTR have had to rely on only limited guidance.
Under Canadian Securities Administrators National Instrument (NI) 51–101 Standards of Disclosure for Oil and Gas Activities, companies are obligated to disclose Proved and Probable Reserves but have the option to additionally disclose Possible Reserves, Contingent Resources and Prospective Resources. NI 51–101 references technical guidelines contained in the Canadian Oil and Gas Evaluation Handbook (COGEH) maintained by the Canadian Chapter of the Society of Petroleum Evaluation Engineers (SPEE). Disclosure of ROTR estimates in Canada has increased substantially in recent years and based on reviews of many filings, is often unclear, inconsistent or potentially misleading. A SPEE Calgary committee has recently proposed additional guidance to promote and improve consistency in the estimation of ROTR for business operations and public disclosure. It is intended to augment other guidelines in COGEH Volumes 1 to 3. Issues addressed with respect to estimation, classification and reporting of ROTR include the following: Clarification of definitions and principles pertaining to petroleum accumulations, particularly in relation to continuous deposits, Clarification of definitions and principles pertaining to recovery projects and technologies, Exploration and technical and commercial risks, Aggregation of resource estimates. Guidance on reporting the results of ROTR evaluations
Clarification of definitions and principles pertaining to petroleum accumulations, particularly in relation to continuous deposits,
Clarification of definitions and principles pertaining to recovery projects and technologies,
Exploration and technical and commercial risks,
Aggregation of resource estimates.
Guidance on reporting the results of ROTR evaluations
Reporting of the results of a resource evaluation under the recommended revised guidelines should provide information for both internal management and external investors sufficient to make informed investment decisions. The revised definitions and classification guidance may be equally applicable in future updates of the Petroleum Resources Management System (PRMS).
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.
Declining global economic conditions and increased pressure to find new, affordable sources of energy create a turbulent macro environment for oil and gas companies. Stakeholders present conflicting goals of increased return on investment while simultaneously reducing risk and providing greater transparency into project performance. The traditional models of large scale investment into major capital projects can no longer guarantee success; instead, organizations must focus on capital management throughout a project's life cycle. Significant effort, time, and resources are invested in project delivery. However, poor decisions during the planning phases may result in loss of value during execution. By combining decision analysis tools and techniques with integrated, dynamic planning systems, companies are able to carefully manage their capital across the project life cycle to understand their costs, resources, and schedules. These companies benefit from increased collaboration leading to more informed decision making balancing risk and reward.
This paper presents an innovative method for evaluating and dynamically planning the development of uncertain upstream investments. It centers on a paradigm shift in the way upstream managers assess investments, toward an approach that incorporates decision analysis tools and techniques with integrated dynamic planning systems. Case study examples are provided to illustrate key principles.
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.
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.