Decline curve analysis is widely used in industry to perform production forecasting and to estimate reserves volumes. A useful technique in verifying the validity of a decline model is to estimate the Arps decline parameters, the loss ratio and the b-factor, with respect to time. This is used to check the model fit and to determine the flow regimes under which the reservoir produces. Existing methods to estimate the b-factor are heavily impacted by noise in production data. In this work, we introduce a new method to estimate the Arps decline parameters.
We treat the loss ratio and the b-factor over time as parameters to be estimated in a Bayesian framework. We include prior information on the parameters in the model. This serves to regularize the solution and prevent noise in the data from being amplified. We then fit the parameters to the model using Markov chain Monte Carlo methods to obtain probability distributions of the parameters. These distributions characterize the uncertainty in the parameters being estimated. We then compare our method with existing methods using simulated and field data.
We show that our method produces smooth loss ratio and b-factor estimates over time. Estimates using the three-point derivative method are not matched with data, and results in biased estimates of the Arps parameters. This can lead to misleading fits in decline curve analysis and unreliable estimates of reserves. We show that our technique helps in identification of end of linear flow and start of boundary dominated flow. We use our method on simulated data, with and without noise. Finally, we demonstrate the validity of our method on field cases.
Fitting a decline curve using the loss ratio and b-factor plots is a powerful technique that can highlight important features in the data and the possible points of failure of a model. Calculating these plots using the Bourdet three-point derivative induces bias and magnifies noise. Our analysis ensures that this estimation is robust and repeatable by adding prior information on the parameters to the model and by calibrating the estimates to the data.
Production forecasting always play an important role in decision-making for the corporate management and architecting company’s strategy. In an oil or gas upstream company, the yearly projection of future hydrocarbon production is a routine practice for preparing annual business plan. As sub surface uncertainties pose a huge threat for upstream company, achieving the forecasted crude oil and gas production is challenging in most cases. For national oil companies, having multiple Assets and thousands of wells where reservoirs underpinning different projects at different stages of field development, there arises a need of unified production forecast process that ensures forecast quality. In view of this, Kuwait Oil Company (KOC) initiated a process for improving forecast process adopting best reservoir management practices. In order to make reliable predictions, the engineer must be knoweledgable in variety of techniques to cover the wide range of conditions that are encountered in forecasting process. At the same time, engineer should have enough experience of reservoir behavior to alert the ways in which a given procedure might be changed to yield the most reasonable outcome. In case of using multiple methodology during preparation of forecast, the results obtained by each method and its underlying factor of judgment must be accounted.
The topic intend to share here is focused on developing a quality assurance process for base forecast in a mature reservoir. Base forecast, which also known as no further capital investment case refers to the production forecast that would result from existing wells and facilities. For national oil company’s having huge portfolio of brown reservoirs, base case wedge comprises a significant part of forecast volume. For the base forecast of brown reservoirs, it is always useful to use multiple methodology as applicable and compare their outcome for quality check. A combination plots from multiple methodology helps the field engineer to understand the underlying uncertainty in the forecast. Now a day it is common practice to generate forecast from numerical reservoir simulation model. In contrast, performance base methodology like decline curve analysis (DCA) for brown reservoir shows higher accuracy. However an effort should be made to obtain accurate rate and pressure data to improve the reliability of decline curve analysis (
Zaluski, Wade (Schlumberger Canada LTD) | Andjelkovic, Dragan (Schlumberger Canada LTD) | Xu, Cindy (Schlumberger Canada LTD) | Rivero, Jose A. (Schlumberger Canada LTD) | Faskhoodi, Majid (Schlumberger Canada LTD) | Ali Lahmar, Hakima (Schlumberger Canada LTD) | Mukisa, Herman (Schlumberger Canada LTD) | Kadir, Hanatu (Schlumberger Canada Limited now with ExxonMobil) | Ibelegbu, Charles (Schlumberger Canada Limited) | Pearson, Warren (Pulse Oil Operating Corp) | Ameuri, Raouf (Schlumberger Canada Limited) | Sawchuk, William (Pulse Oil Operating Corp)
Enhanced oil recovery (EOR) is an economic way of producing the remaining oil out of previously produced Devonian Pinnacle Reefs in the Nisku Formation within the Bigoray area of Alberta. To maximize the recovery factor of the remaining oil, it was necessary to first characterize the geological structure, matrix reservoir properties, vugular porosity and the natural fracture network of these two carbonate reefs. This characterization model was then used for reservoir simulation history matching and production forecasting further discussed by (
In the absence of well-developed calibrated geologic and simulation models, empirical approaches such as decline curve analysis (DCA) are normally used for production forecasting and reserve estimation. DCA is computationally more efficient compared to simulation models when the active well base exceeds hundreds of wells. However, the underlying assumption for conventional DCA is no change in well operation settings. Moreover, the common approach for production forecasting consists of manual outlier detection and removal, interpretation of missing measurements and data fitting using different models for each well. Therefore, the process of conventional DCA is subjective due to the lack of a standard workflow for preprocessing and data cleansing. The common practice for doing DCA has three main steps: 1. Finding the most representative period in the history of well, 2. Detecting the initial rate (start point) of forecast, 3. Selecting the type of decline and fitting the appropriate model to data points. The solutions to these problems could vary from engineer to engineer and it can be time consuming to analyze all wells manually. To address these issues, we developed a novel workflow based on stochastic methods for detecting various well interventions including change in artificial lift, pump changes and acid treatment, and for forecasting oil production rate more accurately in the presence of uncertainty. The novelty of the proposed ensemble-based approach is forecasting conditioned on various well interventions. Furthermore, the proposed unsupervised stochastic anomaly detection method will detect various well works (or events) in the case of missing records of time and type of events. In this paper, we designed two experiments to test the proposed workflow for oil production rate forecasting and evaluation of acid treatments.
The oil & gas industry uses production forecasts to make a number of decisions as mundane as whether to change the choke setting on a well, or as significant as whether to develop a field. As these forecasts are being used to develop cashflow predictions and value and decision metrics such as Net Present Value and Internal Rate of Return, their quality is essential for making good decision. Thus, forecasting skills are important for value creation and we should keep track of whether production forecasts are accurate and free from bias.
In this paper we compare probabilistic production forecasts at the time of the development FID with the actual annual production to assess whether the forecasts are biased; i.e., either optimistic, overconfident, or both.
While biases in time and cost estimates in the exploration & production industry are well documented, probabilistic production forecasts have yet to be the focus of a major study. The main reason for this is that production forecasts for exploration & production development projects are not publicly available. Without access to such estimates, the quality of the forecasts cannot be evaluated.
Drawing on the Norwegian Petroleum Directorates (NPD) extensive database, annual production forecasts, given at time of project sanction (FID), for 56 fields in the 1995 – 2017 period, have been compared with actual annual production from the same fields. The NPD guidelines specify that the operators should report the annual mean and P10/90-percentiles for the projected life of the field at the time of the FID; that is, the forecasts should be probabilistic. The actual annual production from the fields was statistically compared with the forecast to investigate if the forecasts were biased and to assess the financial impact of such biases.
This paper presents the results from the first public study of the quality of probabilistic production forecasts. The main conclusions are that production forecasts that are being used at the FID for E&P development projects are both optimistic and overconfident. As production forecasts form the basis for the main investment decision in the life of a field, biased forecasts will lead to poor decisions and to loss of value.
PI (Productivity Index) degradation is a common issue in many oil fields. To obtain a highly reliable production forecast, it is critical to include well and completion performance in the analysis. A new workflow is developed to assess and incorporate the damage mechanisms at the wellbore, fracture and reservoir into production forecasting. Currently, most reservoir models use a skin factor to represent the combined well damages mechanisms. The skin factor is adjusted based on the user's experience or data analysis instead of physical modeling. In this workflow, a detailed model is built to explicitly simulate the damage mechanisms, assess the dynamic performance of the well and completion with depletion, and generate a physics-based proxy function for reservoir modeling. The new workflow closes the modeling gap in production forecasting and provides insights into which damage mechanisms impact PI degradation.
In the workflow, a detailed model is built, which includes an explicit wellbore, an explicit fracture and the reservoir. Subsurface rock and flow damage mechanisms are represented explicitly in the model. Running the model with an optimization tool, the damage mechanisms’ impact on productivity can be assessed separately or in a combination. A physics-based proxy is generated linking the change in productivity to typical well parameters such as cumulative production, drainage region depletion and drawdown. This proxy is then incorporated into a standard reservoir simulator through the utilization of scripts linking the PI evolution of the well to the typical well parameters stated above. The workflow increases the reliability of generated production forecasts by incorporating the best representation of the near wellbore flow patterns.
By varying the damage mechanism inputs the workflow is capable of history matching and forecasting the observed field behavior. The workflow has been validated for a high permeability, over pressured deep-water reservoir. The history match, PI prediction and damage mechanism analysis are presented in this paper. The new workflow can help assets to: (1) history match and forecast well performance under varying operating conditions; (2) identify the key damage mechanisms which allows for potential mitigation and remediation solutions and; (3) set operational limits that reduce the likelihood of future PI degradation and maintain current performance.
Indigo Natural Resources, Aethon Energy, and Rockcliff Energy are among the most active operators in the revived Haynesville Shale of North Louisiana and East Texas. And most people outside of the region likely have never heard of them. Ghawar vs. Permian Basin: Is There Even a Comparison? While some try to put the two enormous oil producers toe-to-toe, the best thing to do might be to understand why they are different. Encana CEO Doug Suttles assures that shale executives are acutely aware of the parent-child well challenge, and he doesn’t think it’s “a big threat” to the sector.
It follows that forecasts that deviate from the actual production with hindsight can still be good forecasts if the uncertainty range is properly defined, justified and documented as will be shown by Example 6 on the Production forecasting FAQs. However, forecasts that contradict each other even though they are based on the same information cannot be good forecasts, even if they are made for different purposes see Example 3 of the Production forecasting FAQs. It is customary in the industry to describe this uncertainty in terms of a low (P90)/high (P10) range. This is consistent with both the Petroleum Resource Management System (PRMS)  and the Securities and Exchange Commission (SEC) . For volume estimates, a low (P90)/high (P10) range is thus unambiguously defined by statistics.
Uncertainty range in production forecasting gives an introduction to uncertainty analysis in production forecasting, including a PRMS based definition of low, best and high production forecasts. This page topic builds on this with more details of how to approach uncertainty analysis as part of creating production forecasts. Probabilistic subsurface assessments are the norm within the exploration side of the oil and gas industry, both in majors and independents. However, in many companies, the production side is still in transition from single-valued deterministic assessments, sometimes carried out with ad-hoc sensitivity studies, to more-rigorous probabilistic assessments with an auditable trail of assumptions and a statistical underpinning. Reflecting these changes in practices and technology, recently SEC rules for reserves reporting (effective 1 January 2010) were revised, in line with PRMS, to allow for the use of both probabilistic and deterministic methods in addition to allowing reporting of reserves categories other than "proved." This section attempts to present some of the challenges facing probabilistic assessments and present some practical considerations to carry out the assessments effectively. It should be noted that for simplicity the examples referred to in this section are about calculating OOIP rather than generating probabilistic production forecasts directly. Clearly OOIP/GOIP is the starting point of any production forecast and gives a firm basis from which to build production forecasts.
There are two options for the dlim value: "dlimexponential" and "dlimhyperbolic". When using the "dlimexponential", the decline will transition such that the exponential portion of the decline will have an effective decline rate of the dlimvalue specified. When using the "dlimhyperbolic", the decline will transition when the hyperbolic portion reaches the specified dlim value. The exponential portion will then have an effective decline rate that is different from the dlim value. The stretched exponential decline method is a variation of the traditional Arps method, but is better suited to unconventional reservoirs due to its bounded nature.