Masini, Cristian (Petroleum Development Oman) | Al Shuaili, Khalid Said (Petroleum Development Oman) | Kuzmichev, Dmitry (Leap Energy) | Mironenko, Yulia (Leap Energy) | Majidaie, Saeed (Formerly with Leap Energy) | Buoy, Rina (Formerly with Leap Energy) | Alessio, Laurent Didier (Leap Energy) | Malakhov, Denis (Target Oilfield Services) | Ryzhov, Sergey (Formerly with Target Oilfield Services) | Postuma, Willem (Target Oilfield Services)
Unlocking the potential of existing assets and efficient production optimisation can be a challenging task from resource and technical execution point of view when using traditional static and dynamic modelling workflows making decision-making process inefficient and less robust.
A set of modern techniques in data processing and artificial intelligence could change the pattern of decision-making process for oil and gas fields within next few years. This paper presents an innovative workflow based on predictive analytics methods and machine learning to establish a new approach for assets management and fields’ optimisation. Based on the merge between classical reservoir engineering and Locate-the-Remaining-Oil (LTRO) techniques combined with smart data science and innovative deep learning algorithms this workflow proves that turnaround time for subsurface assets evaluation and optimisation could shrink from many months into a few weeks.
In this paper we present the results of the study, conducted on the Z field located in the South of Oman, using an efficient ROCM (Remaining Oil Compliant Mapping) workflow within an advanced LTRO software package. The goal of the study was to perform an evaluation of quantified and risked remaining oil for infill drilling and establish a field redevelopment strategy.
The resource in place assessment is complemented with production forecast. A neural network engine coupled with ROCM allowed to test various infill scenarios using predictive analytics. Results of the study have been validated against 3D reservoir simulation, whereby a dynamic sector model was created and history matched.
Z asset has a number of challenges starting from the fact that for the last 25 years the field has been developed by horizontal producers. The geological challenges are related to the high degree of reservoir heterogeneity which, combined with high oil viscosity, leads to water fingering effects. These aspects are making dynamic modelling challenging and time consuming.
In this paper, we describe in details the workflow elements to determine risked remaining oil saturation distribution, along with the results of ROCM and a full-field forecast for infill development scenarios by using neural network predictive analytics validated against drilled infills performance.
Within the operator's arsenal, a range of data acquisition methods are available such as: seismic, strat - hole, corehole and pilot - hole drilling. Commonly called'static' data acquisition from seismic, log and core unfortunately provide limited insight on reservoir and well performance, as permeability can only be measured through production testing (rather than from core poro - perm relationships in the case of conventional oil and gas assets). Therefore, pilot production testing is one imp ortant method to de - risk CBM plays. Operators are faced with the challenge of making the most of the production informationto characterise the underlying reservoir properties and predict future well performance. In order to extract reservoir characterisati on information from production data, history - matching of the data needs to be conducted using a mathematical model replicating the physics of production.
This paper illustrates, through field studies examples, why and how a structured approach towards managing uncertainties, and especially sampling biases, delivers valuable insights through the successive early asset life stages - exploration, appraisal and field development phases. In doing so, we respond to three fundamental questions.
Firstly, ‘What are the key uncertainties - those that matter?' Field studies should begin with a comprehensive upfront assessment of uncertainties' impact on historical and future well and field performance. However, often major factors are overlooked, leading to under-prediction of true outcome ranges and the inability to reconcile historical production. Our illustration is a large producing carbonate field, where after 15 years of production, large scale Karstification was finally evidenced to be the explanation for the field performance that couldn't be history matched with the measured matrix porosity and permeability ranges.
Secondly ‘What are realistic ranges for these uncertainties?' Known Industry best practices include intensive expert-assist, integration of drilling, mud-logging
and other traditional sources of data from the field, resorting to analogue benchmarking. Despite these, we often fail to understand and correct for sampling bias,
which we show often leads to over-optimism. The paper will highlight why such biases are present and propose simple and practical methods to remove them. The case study is the volumetric assessment of a gas discoveries portfolio, where geophysical techniques were instrumental in exploration and appraisal drilling.
Finally ‘How these uncertainties will evolve with time?' This is an important question for assessing value of Information: the impact that additional data may have on the uncertainty range of uncertainties and the base case. Unconventional fractured plays, often characterized by data abundance but extreme variability, provide surprising insights on how uncertainties ranges evolve. This paper presents methods to develop confidence curves for important parameters.
This paper illustrates, through field studies examples, why and how a structured approach towards managing sampling biases in reservoir evaluation delivers valuable insights through the successive early asset life stages - exploration, appraisal and field development phases.
Whilst the purpose of the paper is not to provide a comprehensive review of uncertainty management best practices, a workflow and fundamental steps are discussed herein, to provide some contextual framework. We then focus our illustration around identification of key uncertainties, defining realistic ranges for these, and finally assessing how ranges should evolve with time.
Alessio, Laurent Didier (CSMP - Shell) | Howells, Christopher (CSMP) | Aboel-Abbas, Sabry Abdel Mawla (Shell Malaysia) | Wade, Bruce Jerome (CS Murtiara Petroleum Sdn Bhd) | Chu, Joanne Lai-Jean | Ball, Stephen Farley
CS Mutiara Petroleum is a Petronas Carigali - Shell Malaysia joint operating company formed in 2001, operating since then the PM301 and PM302 exploration PSCs. The company enjoyed a 100% exploration success rate in the North Malay basin, and is now rapidly transitioning into a development venture.
A total of six discoveries were made since 2002 within the PM301 block. The nature of these discoveries: modest size, stacked pay, fluvio-marine transitional geological setting, high heterogeneity, partially sub-seismic resolution, creates a range of technical and economical challenges. The application of a number of specific technologies, notably to reservoir characterization are seen key to unlock the potential of these discovered volumes.
Technically, in the early stages, the seismic attribute-based prediction of gas sand, using a simultaneous inversion technique, was the key enabler for exploration success, allowing to map the presence of coal versus water and gas sands. Now, success through the development phase requires the application of the following technologies:
In addition to technical challenges, critical to unlocking these volumes is the economical optimisation of the cluster development, within a consistent portfolio management framework. This is done by using modelling at various levels: 3D modelling at field level, testing different geological concepts and the associated key uncertainties; those are then scaled up into an integrated surface-subsurface nodal network model to optimise the development of the discoveries as well as the near field potential remaining prospects.
History matching is traditionally complex and time-consuming: multiple parameters influence the match and their inter-dependency produces effects that are difficult to predict. Defining the match itself can be challenging, since various indexes or responses can be used: water breakthrough timing, pressures, layer contributions etc… Consequently, whilst multiple realisations methodologies are routinely applied for "green" field development planning, most of the time incremental activity screening on "brown" fields is done on a single matching realisation -"the" matched model - with little confidence that the full range of uncertainties is captured.
Experimental design provides a well-suited framework to tackle the challenge of multi-realisation history matching, following these key steps:
• Selection of key parameters with variance analysis,
• Reduction of dimensionality by creating hybrid parameters, using techniques related to principle component analysis,
• Predicting matching domains: combination of parameters levels (once discretised) that are likely to generate a match. This greatly helps the likelihood of finding multiple matching realisations, covering the range of parameter variation.
This methodology was successfully applied in the F6 subsurface studies, aimed at screening field redevelopment opportunities. F6 is the largest gas field in the Central Luconia carbonate province, offshore Sarawak (Borneo), having a GIIP of more than 7 Tscf. With over half the reserves produced, well capacity is now threatened by the rising aquifer. In order to safeguard and possibly increase the reserves, a field review was undertaken to identify further development opportunities, and a multi-realisation approach was chosen to capture the effect of key subsurface uncertainties on those activities.
A total of 28 matching realisations were generated, covering the variation range of the identified key seven parameters whilst optimising the number of runs performed, thus saving time. Key to the success of the method lies in the integration of disciplines to allow the upfront identification of parameters and their ranges.
The screening of redevelopment options against those realisations allowed to establish the range of expected incremental reserves, assess risks, and form a sound basis for business decisions.