Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Reservoir Simulation
Summary Stochastic modeling provides a mechanism for incorporating risk and uncertainty considerations into portfolio production forecasts. Through this process, insight is gained into the likelihood of production targets being missed, met, or exceeded. This insight enables organizations to better manage operational, positioning, and strategic planning activities around stakeholders’ production expectations. Inherent in all capital programs are numerous uncontrollable, but definable, factors that affect overall corporate production performance. These factors can be categorized into four groups: (1) timing uncertainties, (2) performance uncertainties, (3) sequencing uncertainties, and (4) risk. Timing uncertainty considers spud scheduling, spud-to-first-production cycle timing, and production-ramp-up cycle timing. Performance uncertainty considers the historical or modeled distribution of period-specific production rates within the constituent plays (e.g., What is the unavoidable range of variability within a play as depicted in a peak-normalized composite-production plot of wells within the analog population?). Sequencing uncertainty considers performance-percentile clustering or sequencing within the program (e.g., the number of top-quartile wells that are, by chance, drilled early in the year vs. later in the year). Finally, risk addresses commercial failure within a program attributed to either geology or execution, or both. By integrating historical operational data with a standardized set of play-assessment deliverables, the building blocks of a stochastic capital-program forecast and analysis are readily available. Ultimately, the use of stochastic modeling in portfolio production forecasting provides an organization's decision makers with the information necessary to examine investment and strategic decisions in the context of corporate-risk tolerance.
- Energy > Oil & Gas > Upstream (1.00)
- Banking & Finance > Trading (1.00)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin (0.99)
- North America > United States > Mississippi > Grafton Field (0.98)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Production forecasting (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring (1.00)
- Management > Risk Management and Decision-Making (1.00)
Summary This paper presents original research on how to improve the predictive ability of type wells used in evaluating unconventional-resource-drilling programs by extending traditional Monte Carlo calculations. The paper addresses three critical questions engineers must answer before constructing a type well: which well to use in the construction, the relative importance (weighting) appropriate for each well, and how to adapt the results to reflect certainty (e.g., P10, P50, or P90). The proposed method involves determining the aggregated distribution of estimated ultimate recovery (EUR) for the specified number of wells by running a statistically significant number of Monte Carlo trials. From this distribution, one can determine mean EURs for the desired type-well certainties, such as P50 or P90. Additional Monte Carlo trials yielding the desired mean EUR will help determine which wells to average. Monte Carlo sampling results in several hundred trials that match the desired EURs. The relative frequency of well selection from these trials defines the weighting factor and thus the relative importance of each well. Type wells result from a weighted averaging of history and production from the selected wells. Engineers can use this new methodology to prepare production-profile forecasts for the evaluation of multiwell unconventional-resource-drilling programs. They can also gain an understanding of the effect that aggregation will have on their evaluation work. Our research concludes that current type-well-construction practices may not be appropriate for evaluating future drilling because the production profiles for the wells used to build the type well may differ from the production profiles of the planned wells. This paper presents a new method to obtain more-representative type wells. The method permits defining any uncertain parameters, such as EUR, net present value (NPV), or payout time, and then building a type well for various measures of probability of attaining that parameter. This paper presents new concepts that contribute to the technical knowledge base. We present a method to calculate probabilistic type wells dependent on the likelihood of drilling wells that make up the distribution of EUR (or other parameters). The method combines the concepts of aggregation and Monte Carlo simulation to calculate weighting factors for use when averaging rate/time profiles. We introduce the paradigm that one can build probabilistic type wells from distributions of parameters other than EUR. EUR is only suitable for determining reserves. When conducting an evaluation, one may want a more-relevant type well; for instance, one that examines the ability to self-finance by representing the probability of recovering a percentage of capital expenditure (Capex) in the first year. We present a method to scale rate/time profiles from representative wells to the fracture geometry and reservoir quality of future drilling prospects. These scaled wells are for use in building type wells. The scaling algorithms also prove useful in estimating fracture geometry and reservoir quality where it is unknown.
- Overview > Innovation (0.68)
- Research Report > New Finding (0.48)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > Canada > British Columbia > Western Canada Sedimentary Basin > Alberta Basin > Deep Basin > Heritage Field > Montney Formation (0.99)
- Asia > Indonesia > Java > Central Java > Blora Regency > Asset 4 Area > Trembul Block > Trembul Field > Ngrayong Formation > P1 Well (0.99)
Summary A better characterization of reservoir heterogeneity is indispensable to improve the prediction accuracy on production performance of heterogeneous petroleum reservoirs. Measuring physical properties at a single point can provide us with local information on reservoir heterogeneity, which is also beneficial in reducing some degree of uncertainty about global reservoir heterogeneity, and thus leads us to an improved prediction accuracy. It is not necessarily the case, however, that an improvement of the prediction accuracy implies an increase of our ability to make a proper decision. In this paper, we discuss how to quantify such a value of single-point data, obtained by measuring physical properties at a single point, for decision making in development of heterogeneous reservoirs by using the value-of-information (VOI) theory. We present an efficient algorithm to evaluate the value of single-point data with the help of reservoir simulation, Gaussian random field models, Monte Carlo integration, and the expectation-maximization (EM) algorithm. We validate our algorithm through a toy problem, and then demonstrate the practical usefulness of our algorithm through numerical experiments.
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Management > Risk Management and Decision-Making (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (0.89)
- (2 more...)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.49)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.46)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.46)
Summary Production-data analysis is a practice fraught with inconsistencies. In the application of any single model, the quantity of answers arrived at by experienced evaluators is often equal to the number of evaluators analyzing the data. The cause of such inconsistency is bias on the part of evaluators. Although the colloquial use of bias typically implies systematic error, in this paper, we define bias as an expression of belief by the evaluator. With the lack of recognition of bias, no means exists with which to gauge its accuracy. A method that requires explicit expression of one's bias in time/rate decline behavior can provide an objective means with which to evaluate it. In this work, we present a machine-learning method to forecast production in unconventional, liquid-rich shale and gas-shale wells. Methods were developed for probabilistic decline-curve analysis with Markov-chain Monte Carlo simulation (MCMC) as a means to quantify reserves uncertainty, to incorporate prior information (i.e., bias), and to do so quickly. We extend the existing approaches by (a) a modified likelihood-distribution function to improve “learning” of production data, (b) integration of the transient hyperbolic model (THM) to explicitly define the various flow regimes present in unconventional wells, (c) a method for construction of discretized “percentile neighborhood” forecasts, and (d) construction of type wells from an analyzed well population. The accuracy and calibration of the method are demonstrated by an analysis of 136 wells in the Elm Coulee Field of the Bakken. Quantification of change in time/rate behavior caused by completion design, and the inference of physical behavior and properties, is demonstrated with a tight oil play in the Cleveland sand formation of the Anadarko Basin, as well as a shale play in the Wolfcamp formation of the Permian Basin. We show that this implementation of supervised machine learning, in combination with well-calibrated bias, improves the estimation of uncertainty of the posterior distribution of forecasts. In addition, hindcasts performed at various time intervals result in accurate estimation of mean five-year cumulative production. We observe that the “percentile neighborhood” forecasts are reasonable fits of production data comparable to those that may be created by a human evaluator, and that the type well computed is representative of the decline behavior of the well population upon which it is based. We conclude that, given the speed and accuracy of the process, machine learning is a reliable technology as defined by the US Securities and Exchange Commission (SEC), and can significantly improve the process of production forecasting by human evaluators for most unconventional wells with consistent trends of production history.
- North America > United States > Texas (1.00)
- North America > United States > North Dakota > Mountrail County (0.24)
- North America > United States > Montana > Richland County (0.24)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.45)
- Geology > Petroleum Play Type > Unconventional Play (0.34)
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > North America Government > United States Government (0.34)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (30 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Production forecasting (1.00)
- (2 more...)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.69)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.68)
Summary Decision making under uncertainty can be quite challenging, especially when complex numerical simulations are considered in the work flow and the decision has to be made relatively fast (e.g., in hours). This is the case when one needs to rank a given field portfolio within a limited budget and with acquisition constraints. If the ranking measure associated with each field is properly and rapidly evaluated, new prospect opportunities, which may lead to a favorable strategic position, can be readily identified. In this paper, we propose an efficient methodology for computing a “production-potential” measure that can be used to rank greenfield portfolios in the presence of geological uncertainty, quantifying both uncertainty and risk propagation. Next, we briefly describe the basics of the method proposed. First, uncertainty in sedimentary variability and flow behavior has to be characterized by a number of representative geological realizations. Sampling techniques are used to significantly reduce the number of realizations while preserving accuracy in the description and uncertainty propagation. Thereafter, multiple and varied field-development plans, based on primary/secondary-recovery mechanisms, are automatically generated while accounting for key parameters related to the number, drilling locations, and drilling sequence of wells. In these plans the reservoir is clustered by areas with similar production/injection potential, and the well locations and drilling schedules are obtained accordingly. The well controls are determined through estimations of the field-recovery factor. By means of experimental-design techniques a relatively small number of field-development plans are selected to capture the most significant production profiles. Each of these development plans is simulated for the realizations sampled previously, and the production-potential measure [e.g., average net present value (NPV) over all sampled realizations] is computed for all the plans. The highest of these measures (i.e., the best development plan) can be used for ranking the greenfield in the portfolio. Response-surface procedures are considered to perform additional analysis computations within iterative optimization procedures. It is important to note that other statistics related to the exploitation potential (e.g., standard deviation of the NPV) can also be used to complement the ranking, thereby mitigating the decision makers’ risk tolerance. The methodology has been tested on the Brugge Field benchmark, which presents 104 realizations of multiple geological parameters. The benchmark has been modified to simulate a greenfield scenario. The ranking measure is the (discounted) NPV averaged over the 104 realizations. The proposed work flow yields a ranking measure of USD 5.43 billion, and the computational cost is approximately 1,900 simulations (performed in a parallel-computing environment). This NPV is somewhat higher than those found for the Brugge benchmark (with similar modified settings) by other researchers. To validate the results, we performed more-exhaustive checking by use of approximately 17,000 simulations, and the ranking measure found was USD 5.51 billion. The new work flow presented allows one to efficiently and in a sufficiently accurate manner support decision making in greenfield-portfolio evaluation. Fast reservoir-performance-evaluation engines open new prospect opportunities that, with traditional decision-making techniques, may be frequently lost.
- South America (1.00)
- Europe (1.00)
- North America > United States > Texas (0.93)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation (1.00)
- (4 more...)
Summary Over the past two decades, pendulum arbitration has been increasingly incorporated into dispute-resolution procedures for the redetermination of tract participation (equity) for unitized oil and gas fields that straddle domestic license boundaries or international borders. In such cases, the pendulum has been prescribed for use in expert redetermination, ideally so that an expert determines tract participation and then selects the closer submission made by one of the parties. The tract participation of the selected submission then constitutes the expert's final decision. The process can function reasonably well in the originally envisaged situations (e.g., two opposing cases each originating from one of two straddled license areas). It is much more difficult to apply in the more-complex situations to which it has been extrapolated subsequently. These include multiphase reservoirs with separately unitized fluids, several straddled license areas, and the use of the same subsurface model for both field-development and equity-redetermination purposes. An analysis of such situations has allowed the further identification of those circumstances for which the pendulum can be applied meaningfully in expert redetermination and those for which it should not be adopted. These additional expectations include a meaningful basis for tract participation in terms of one (equivalent) unit substance, fully prescribed technical procedures leading to a compliant reservoir model, and a single (as opposed to a staged) expert redetermination that post-dates the parties' submissions but allows for rebuttal in the event of manifest error. Case histories illustrate the difficulties that can arise where these principles are infringed. They also reaffirm the overarching message that each straddling field situation differs from others and therefore every redetermination of tract participation must be assessed separately and thoroughly.
- Europe (0.94)
- North America > United States > Alaska (0.28)
- Management > Asset and Portfolio Management (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation > Estimates of resource in place (0.95)
- Reservoir Description and Dynamics > Reservoir Simulation (0.75)
Summary Operational conditions in the remote highlands of Papua New Guinea (PNG) bring a multitude of challenges, including complex geology, steep jungle terrain, extreme rainfall, a sensitive ecosystem, a variety of landowner cultures, ageing facilities, mature reservoirs, and fraught logistics. These factors conspire to increase the complexity of production forecasting well beyond just reservoir performance. After missing production targets for a number of years, a significant improvement in forecasting was needed to fully capture the uncertainties, both identified and unforeseen. A probabilistic forecasting tool by use of Monte Carlo simulation has been developed, which incorporates assessments of all major variables and takes account of historical performance and system changes. Successful implementation has resulted in the generation of realistic targets that have been met within 4% for 4 years. Further, a clearer understanding of potential threats and opportunities, combined with their impact on production, has been achieved. These results have delivered material benefits to business planning, decision making, and company reputation. Data to be presented on the forecasting tool will describe the quantification of uncertainty for each major variable, including reservoir and facility performance, incremental production opportunities, project schedules, and major unplanned downtime. This approach could be applicable to exploration and production companies operating in difficult conditions worldwide, in areas where unreliable production targets have a major business impact. It is flexible enough for both short- and long-term forecasts, tracking and reporting, and adaptation to new or changed operating conditions.
- Oceania (1.00)
- North America > United States > Texas (0.47)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reserves Evaluation (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring (1.00)
- (2 more...)
Summary Many oilfield optimization problems concern expensive numerical-reservoir-simulation-based objective functions. These are computationally demanding and can require a significant amount of time to evaluate. With the addition of uncertainty in the underlying models, the computational cost is exacerbated and the collective cost necessary to realize the efficient frontier for the decision maker can become considerable. Although approximation methods are often used to alleviate the cost of optimization, methods to improve the efficiency of the entire process in the presence of uncertainty are less established. This paper presents a scheme that, unlike the conventional approach, ensures that a convex efficient frontier is generated while also reducing the number of simulation evaluations necessary. This is achieved by reuse of sampled data through a procedure referred to as "recasting." Here, existing sample data in the solution space are mapped to a particular objective space of choice dictated by the weights assigned to the mean and standard-deviation terms. Results from an analytical test case, a numerical reservoir-simulation model, and a coupled reservoir plus surface-facility-simulation model are presented. The results show that the proposed workflow can help reduce the high computational cost associated with expensive simulation based function optimization under uncertainty.
- Europe (0.93)
- North America > United States > Texas (0.28)
- Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
- Production and Well Operations > Artificial Lift Systems > Gas lift (1.00)
- Data Science & Engineering Analytics (1.00)
Summary We discuss the two-factor oil-price model in valuation and analysis of flexible investment decisions. In particular, we will discuss the real options formulation of a typical oilfield-abandonment problem and will apply the least-squares Monte Carlo (LSM) simulation approach for calculation of project value. In this framework, the two-factor oil-price model will go a long way in the analysis of decisions and value creation. We also propose an implied method for estimation of parameters and state variables of the two-factor price process. The method is based on implied volatility of option on futures, the shape of the forward curve, and the implicit relationship between model parameters.
- North America > United States > Texas (0.46)
- Europe > United Kingdom > England (0.28)
- North America > United States > New Jersey (0.28)
Summary Equity redetermination is most commonly encountered where a straddling field is developed as a discrete entity through a process of unitization. It is enacted through evaluation procedures that prescribe the technical methodology for requantifying different license shares, or tract participations, in a field unit as more data become available. The formulation of these procedures usually takes place at unitization, it is based on appraisal data, and, therefore, it is guided by simplified perceptions of reservoir character. For this reason, many such technical procedures have been found to be lacking when they are applied later at the equity redetermination stage. These shortcomings can take the form of ambiguous wording, misleading definitions, technically inappropriate specifications, contradictory prescription, or simply a lack of sufficient detail to render the intended process meaningful. They have impeded the determination of revised tract participations by triggering interlicense disagreements that might otherwise have been avoided. With the objective of reducing this unhelpful impact, experience of redetermination situations is used to illustrate the nature and consequences of poorly constructed procedures for the recomputation of tract participations. The analysis is then flipped to generate a framework of key elements of technical procedures together with indications of how they are best implemented. These matters form the basis for a high-level set of protocols for a more efficient and effective redetermination of equity that would avoid the previously encountered shortcomings. The protocols encompass the proper incorporation of data character, a sound technical basis for redetermination, a balance between under- and over-prescription, an auditable deterministic ethos, and adherence to good international petroleum practice. They constitute recommendations for a better approach to the compilation of fit-for-purpose evaluation procedures within those unitization agreements that make provision for a future redetermination of equity. The recommendations are equally applicable to domestic and international unitizations. The principal benefit lies in an enhanced efficiency of the equity-redetermination process, which feeds through to a greater collective asset value.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Borehole Geophysics (1.00)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Markham Field (0.99)
- North America > United States > Texas > Fort Worth Basin > Woodbine Field (0.99)
- North America > United States > Texas > East Texas Salt Basin > Hawkins Field > Woodbine Formation (0.99)
- North America > United States > Texas > East Texas Salt Basin > East Texas Field > Woodbine Formation (0.99)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (1.00)
- (6 more...)